AWS Certified Data Engineer – Associate practice questions

1. Question 1 of 25

   A data engineer is configuring an AWS Glue job to read data from an Amazon S3 bucket. The data engineer has set up the necessary AWS Glue connection details and an associated IAM role. However, when the data engineer attempts to run the AWS Glue job, the data engineer receives an error message that indicates that there are problems with the Amazon S3 VPC gateway endpoint. The data engineer must resolve the error and connect the AWS Glue job to the S3 bucket. Which solution will meet this requirement?

   • AUpdate the AWS Glue security group to allow inbound traffic from the Amazon S3 VPC gateway endpoint.
   • BConfigure an S3 bucket policy to explicitly grant the AWS Glue job permissions to access the S3 bucket.
   • CReview the AWS Glue job code to ensure that the AWS Glue connection details include a fully qualified domain name.
   • DVerify that the VPC's route table includes inbound and outbound routes for the Amazon S3 VPC gateway endpoint.

   Show answer

   Correct answer: D

   How others answered

   • D95%

   Explanation

   A - wrong - AWS glue - are serverless service, so it don't have any security groups B - wrong - Because we have error with VPC, not with S3 itself C - wrong - Becuase with S3 - we always have only FQDN for buckets

   Adapted from community discussion by HunkyBunky · 2024-09-23 · 6 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

   Supporting references

   • AWS documentation
   • AWS documentation

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   Candidate context

   Question 1 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

   1. H

      HunkyBunky

      2024-09-23

      Selected D6 upvotes

      A - wrong - AWS glue - are serverless service, so it don't have any security groups B - wrong - Because we have error with VPC, not with S3 itself C - wrong - Becuase with S3 - we always have only FQDN for buckets

   2. G

      GiorgioGss

      2024-09-23

      Selected D4 upvotes

      Although there is no such thing as "inbound and outbound routes" when we talk about VPC route table, when we define a S3 gateway endpoint we must have proper routes in place. I will go with D.

   3. N

      ninomfr64

      2025-04-07

      Selected D1 upvote

      A- NO: on SG we just need to allow outbound traffic, as SG i statefull reurn traffic is allowed B - NO: since we configured IAM permission for Glue Job, there is no need to configure a resource-policy (cross account is not mentioned) C- NO: in bucket connection configuration you just need to provide s3://bucket-name/prefix D - YES: although there is no inbound and outbound routes in route table, we need to ensure a route is in place to reach a the VPC Gateway Policy

   4. M

      MephiboshethGumani

      2025-02-23

      Selected D1 upvote

      D. Verify that the VPC's route table includes inbound and outbound routes for the Amazon S3 VPC gateway endpoint. Explanation: AWS Glue jobs need to connect to the S3 bucket through the Amazon S3 VPC gateway endpoint when they are in a VPC. If the route table does not have proper inbound and outbound routes to the S3 VPC gateway endpoint, the AWS Glue job will not be able to access S3, which results in an error.

2. Question 2 of 25

   A company uses a data lake that is based on an Amazon S3 bucket. To comply with regulations, the company must apply two layers of server-side encryption to files that are uploaded to the S3 bucket. The company wants to use an AWS Lambda function to apply the necessary encryption. Which solution will meet these requirements?

   • AUse both server-side encryption with AWS KMS keys (SSE-KMS) and the Amazon S3 Encryption Client.
   • BUse dual-layer server-side encryption with AWS KMS keys (DSSE-KMS).
   • CUse server-side encryption with customer-provided keys (SSE-C) before files are uploaded.
   • DUse server-side encryption with AWS KMS keys (SSE-KMS).

   Show answer

   Correct answer: B

   How others answered

   • B89%

   Explanation

   The most crucial objective in the problem is "Two layers of server-side encryption must be applied." A: SSE-KMS is a single-layer server-side encryption that uses AWS KMS keys to encrypt data. The Amazon S3 Encryption Client performs client-side encryption, not server-side encryption. C: SSE-C is server-side encryption that uses customer-provided encryption keys to encrypt data. This does not provide two layers of encryption. D: SSE-KMS is a single-layer server-side encryption. It does not meet the encryption requirement of two layers of encryption. B: DSSE-KMS (dual-layer server-side encryption) uses two layers of encryption to encrypt data using keys managed by AWS KMS. The first layer is used to encrypt the data key, and the second layer is used to encrypt the actual data. This provides the two layers of server-side encryption required to meet compliance requirements.

   Adapted from community discussion by samadal · 2024-08-21 · 3 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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   Question 2 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

   1. S

      samadal

      2024-08-21

      Selected B3 upvotes

      The most crucial objective in the problem is "Two layers of server-side encryption must be applied." A: SSE-KMS is a single-layer server-side encryption that uses AWS KMS keys to encrypt data. The Amazon S3 Encryption Client performs client-side encryption, not server-side encryption. C: SSE-C is server-side encryption that uses customer-provided encryption keys to encrypt data. This does not provide two layers of encryption. D: SSE-KMS is a single-layer server-side encryption. It does not meet the encryption requirement of two layers of encryption. B: DSSE-KMS (dual-layer server-side encryption) uses two layers of encryption to encrypt data using keys managed by AWS KMS. The first layer is used to encrypt the data key, and the second layer is used to encrypt the actual data. This provides the two layers of server-side encryption required to meet compliance requirements.

   2. S

      samadal

      2024-08-21

      1 upvote

      1) DSSE-KMS is a real feature. In 2021, AWS announced Dual-Layer Server-Side Encryption (DSSE-KMS) for S3. This feature can be used with S3 Managed Keys (SSE-S3) to provide an additional layer of security for your data. 2) Two-layer encryption generally refers to applying two different encryption mechanisms within the same system or service. Client-side encryption encrypts data before it reaches S3, so S3 itself treats data as already encrypted. Therefore, SSE-KMS does not provide an additional layer of encryption for client-side encrypted data; it simply encrypts already encrypted data once more. This may not provide much of a practical security enhancement. DSSE-KMS, on the other hand, encrypts data twice within S3 using two different keys, effectively providing two layers of encryption. Using client-side encryption together with SSE-KMS can provide an additional layer of security, but is not two-tier server-side encryption in the strict sense.

   3. J

      Ja13

      2024-07-03

      Selected B2 upvotes

      B. Use dual-layer server-side encryption with AWS KMS keys (DSSE-KMS). Dual-layer server-side encryption with AWS KMS keys (DSSE-KMS) is specifically designed to apply two layers of encryption to meet regulatory compliance requirements. This ensures that each object stored in Amazon S3 is encrypted twice, providing the additional security layer that the company needs.

   4. H

      HunkyBunky

      2024-06-24

      Selected B2 upvotes

      I guess that right answer is - B https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingDSSEncryption.html

3. Question 3 of 25

   A data engineer needs Amazon Athena queries to finish faster. The data engineer notices that all the files the Athena queries use are currently stored in uncompressed .csv format. The data engineer also notices that users perform most queries by selecting a specific column. Which solution will MOST speed up the Athena query performance?

   • AChange the data format from .csv to JSON format. Apply Snappy compression.
   • BCompress the .csv files by using Snappy compression.
   • CChange the data format from .csv to Apache Parquet. Apply Snappy compression.
   • DCompress the .csv files by using gzip compression.

   Show answer

   Correct answer: C

   How others answered

   • C100%

   Explanation

   C. Change the data format from .csv to Apache Parquet. Apply Snappy compression. Explanation: Apache Parquet is a columnar storage format optimized for analytical queries. It is highly efficient for query performance, especially when queries involve selecting specific columns, as it allows for column pruning and predicate pushdown optimizations.

   Adapted from community discussion by TonyStark0122 · 2024-02-01 · 6 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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   Question 3 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

   1. M

      milofficial

      2024-01-18

      Selected C11 upvotes

      If the exam would only have these kinds of questions everyone would be blessed

   2. T

      TonyStark0122

      2024-02-01

      6 upvotes

      C. Change the data format from .csv to Apache Parquet. Apply Snappy compression. Explanation: Apache Parquet is a columnar storage format optimized for analytical queries. It is highly efficient for query performance, especially when queries involve selecting specific columns, as it allows for column pruning and predicate pushdown optimizations.

   3. W

      wa212

      2024-04-08

      Selected C2 upvotes

      https://aws.amazon.com/jp/blogs/news/top-10-performance-tuning-tips-for-amazon-athena/

   4. K

      k350Secops

      2024-05-09

      Selected C1 upvote

      switching to Apache Parquet format with Snappy compression offers the most significant improvement in Athena query performance, especially for queries that select specific columns

4. Question 4 of 25

   A company plans to use Amazon Kinesis Data Firehose to store data in Amazon S3. The source data consists of 2 MB .csv files. The company must convert the .csv files to JSON format. The company must store the files in Apache Parquet format. Which solution will meet these requirements with the LEAST development effort?

   • AUse Kinesis Data Firehose to convert the .csv files to JSON. Use an AWS Lambda function to store the files in Parquet format.
   • BUse Kinesis Data Firehose to convert the .csv files to JSON and to store the files in Parquet format.
   • CUse Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON and stores the files in Parquet format.
   • DUse Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON. Use Kinesis Data Firehose to store the files in Parquet format.

   Show answer

   Correct answer: D

   How others answered

   • D54%
   • B40%

   Only 54% agreed on this answer — treat it as contested rather than settled.

   Explanation

   Answer D https://docs.aws.amazon.com/firehose/latest/dev/record-format-conversion.html Amazon Data Firehose can convert the format of your input data from JSON to Apache Parquet or Apache ORC before storing the data in Amazon S3. Parquet and ORC are columnar data formats that save space and enable faster queries compared to row-oriented formats like JSON. If you want to convert an input format other than JSON, such as comma-separated values (CSV) or structured text, you can use AWS Lambda to transform it to JSON first. For more information, see Transform data in Amazon Data Firehose.

   Adapted from community discussion by mzansikiller · 2024-07-21 · 6 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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   Question 4 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

   1. Q

      qwertyuio

      2024-07-17

      Selected D8 upvotes

      https://docs.aws.amazon.com/firehose/latest/dev/record-format-conversion.html

   2. M

      mzansikiller

      2024-07-21

      6 upvotes

      Answer D https://docs.aws.amazon.com/firehose/latest/dev/record-format-conversion.html Amazon Data Firehose can convert the format of your input data from JSON to Apache Parquet or Apache ORC before storing the data in Amazon S3. Parquet and ORC are columnar data formats that save space and enable faster queries compared to row-oriented formats like JSON. If you want to convert an input format other than JSON, such as comma-separated values (CSV) or structured text, you can use AWS Lambda to transform it to JSON first. For more information, see Transform data in Amazon Data Firehose.

   3. M

      mzansikiller

      2024-08-18

      Selected D3 upvotes

      Amazon Data Firehose can convert the format of your input data from JSON to Apache Parquet or Apache ORC before storing the data in Amazon S3. Parquet and ORC are columnar data formats that save space and enable faster queries compared to row-oriented formats like JSON. If you want to convert an input format other than JSON, such as comma-separated values (CSV) or structured text, you can use AWS Lambda to transform it to JSON first. For more information, see Transform source data in Amazon Data Firehose. Answer D

   4. V

      valuedate

      2024-07-24

      3 upvotes

      Option D - Need to convert the inout data from .csv to JSON first. Firehose can't do that without the help of a lambda function in this case. After firehose can convert to .parquet and deliver it to s3

5. Question 5 of 25

   A company loads transaction data for each day into Amazon Redshift tables at the end of each day. The company wants to have the ability to track which tables have been loaded and which tables still need to be loaded. A data engineer wants to store the load statuses of Redshift tables in an Amazon DynamoDB table. The data engineer creates an AWS Lambda function to publish the details of the load statuses to DynamoDB. How should the data engineer invoke the Lambda function to write load statuses to the DynamoDB table?

   • AUse a second Lambda function to invoke the first Lambda function based on Amazon CloudWatch events.
   • BUse the Amazon Redshift Data API to publish an event to Amazon EventBridge. Configure an EventBridge rule to invoke the Lambda function.
   • CUse the Amazon Redshift Data API to publish a message to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the SQS queue to invoke the Lambda function.
   • DUse a second Lambda function to invoke the first Lambda function based on AWS CloudTrail events.

   Show answer

   Correct answer: B

   How others answered

   • B88%

   Explanation

   The most appropriate way for the data engineer to invoke the Lambda function to write load statuses to the DynamoDB table is: B. Use the Amazon Redshift Data API to publish an event to Amazon EventBridge. Configure an EventBridge rule to invoke the Lambda function. Explanation: Option B leverages the Amazon Redshift Data API to publish events to Amazon EventBridge, which provides a serverless event bus service for handling events across AWS services. By configuring an EventBridge rule to invoke the Lambda function in response to events published by the Redshift Data API, the data engineer can ensure that the Lambda function is triggered whenever there is a new transaction data load in Amazon Redshift. This approach offers a straightforward and scalable solution for tracking table load statuses without relying on additional Lambda functions or services.

   Adapted from community discussion by TonyStark0122 · 2024-02-01 · 9 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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   Question 5 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

   1. M

      milofficial

      2024-01-20

      Selected B13 upvotes

      https://docs.aws.amazon.com/redshift/latest/mgmt/data-api-monitoring-events.html

   2. T

      TonyStark0122

      2024-02-01

      9 upvotes

      The most appropriate way for the data engineer to invoke the Lambda function to write load statuses to the DynamoDB table is: B. Use the Amazon Redshift Data API to publish an event to Amazon EventBridge. Configure an EventBridge rule to invoke the Lambda function. Explanation: Option B leverages the Amazon Redshift Data API to publish events to Amazon EventBridge, which provides a serverless event bus service for handling events across AWS services. By configuring an EventBridge rule to invoke the Lambda function in response to events published by the Redshift Data API, the data engineer can ensure that the Lambda function is triggered whenever there is a new transaction data load in Amazon Redshift. This approach offers a straightforward and scalable solution for tracking table load statuses without relying on additional Lambda functions or services.

   3. P

      pypelyncar

      2024-06-09

      1 upvote

      Im not 100% sure of B or C, this is a tricky question. The reason is due to either SQS or EventBridge has not direct connection natively speaking to Redshift Data API. There is no way to publish events by itself. So, this means either SQS / EventBridge eventually need a "proxy" (e.g lambda function) in order to publish events or process events to this 2 sources. In both services we need something to publish those events from Redshift. so Yes, we need a lamda function between Redshift Data API and (SQS|EB). so either B,C doesnt seem to be 100% right. I think this question its a good candidate to be "Choose two options" but none has 100% right. Both are valid considering that there is an adapter function between 2 solutions.

   4. M

      MephiboshethGumani

      2025-03-14

      Selected B1 upvote

      the data engineer should use Amazon EventBridge (formerly CloudWatch Events) to trigger the Lambda function based on a schedule or events that correspond to the completion of the data load process in Amazon Redshift.

6. Question 6 of 25

   A company has three subsidiaries. Each subsidiary uses a different data warehousing solution. The first subsidiary hosts its data warehouse in Amazon Redshift. The second subsidiary uses Teradata Vantage on AWS. The third subsidiary uses Google BigQuery. The company wants to aggregate all the data into a central Amazon S3 data lake. The company wants to use Apache Iceberg as the table format. A data engineer needs to build a new pipeline to connect to all the data sources, run transformations by using each source engine, join the data, and write the data to Iceberg. Which solution will meet these requirements with the LEAST operational effort?

   • AUse native Amazon Redshift, Teradata, and BigQuery connectors to build the pipeline in AWS Glue. Use native AWS Glue transforms to join the data. Run a Merge operation on the data lake Iceberg table.
   • BUse the Amazon Athena federated query connectors for Amazon Redshift, Teradata, and BigQuery to build the pipeline in Athena. Write a SQL query to read from all the data sources, join the data, and run a Merge operation on the data lake Iceberg table.
   • CUse the native Amazon Redshift connector, the Java Database Connectivity (JDBC) connector for Teradata, and the open source Apache Spark BigQuery connector to build the pipeline in Amazon EMR. Write code in PySpark to join the data. Run a Merge operation on the data lake Iceberg table.
   • DUse the native Amazon Redshift, Teradata, and BigQuery connectors in Amazon Appflow to write data to Amazon S3 and AWS Glue Data Catalog. Use Amazon Athena to join the data. Run a Merge operation on the data lake Iceberg table.

   Show answer

   Correct answer: B

   How others answered

   • B50%
   • A42%

   Only 50% agreed on this answer — treat it as contested rather than settled.

   Explanation

   Would it be B "If you have data in sources other than Amazon S3, you can use Athena Federated Query to query the data in place or build pipelines that extract data from multiple data sources and store them in Amazon S3. With Athena Federated Query, you can run SQL queries across data stored in relational, non-relational, object, and custom data sources." https://docs.aws.amazon.com/athena/latest/ug/connect-to-a-data-source.html

   Adapted from community discussion by Eleftheriia · 2024-11-14 · 3 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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   Question 6 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

   1. E

      Eleftheriia

      2024-11-14

      Selected B3 upvotes

      Would it be B "If you have data in sources other than Amazon S3, you can use Athena Federated Query to query the data in place or build pipelines that extract data from multiple data sources and store them in Amazon S3. With Athena Federated Query, you can run SQL queries across data stored in relational, non-relational, object, and custom data sources." https://docs.aws.amazon.com/athena/latest/ug/connect-to-a-data-source.html

   2. K

      kupo777

      2024-11-05

      3 upvotes

      Correct Answer: B Use the Amazon Athena federated query connectors for Amazon Redshift, Teradata, and BigQuery to build the pipeline in Athena. Write a SQL query to read from all the data sources, join the data, and run a Merge operation on the data lake Iceberg table.

   3. M

      michele_scar

      2024-11-15

      Selected B2 upvotes

      https://docs.aws.amazon.com/athena/latest/ug/connectors-available.html

   4. B

      bad1ccc

      2025-04-03

      Selected B1 upvote

      https://docs.aws.amazon.com/athena/latest/ug/federated-queries.html

7. Question 7 of 25

   A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling. Which solution will meet this requirement?

   • ATurn on concurrency scaling in workload management (WLM) for Redshift Serverless workgroups.
   • BTurn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.
   • CTurn on concurrency scaling in the settings during the creation of any new Redshift cluster.
   • DTurn on concurrency scaling for the daily usage quota for the Redshift cluster.

   Show answer

   Correct answer: B

   How others answered

   • B100%

   Explanation

   B. Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster. Explanation: Concurrency scaling in Amazon Redshift allows the cluster to automatically add and remove compute resources in response to workload demands. Enabling concurrency scaling at the workload management (WLM) queue level allows you to specify which queues can benefit from concurrency scaling based on the query workload.

   Adapted from community discussion by TonyStark0122 · 2024-02-01 · 9 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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   Question 7 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

   1. T

      TonyStark0122

      2024-02-01

      9 upvotes

      B. Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster. Explanation: Concurrency scaling in Amazon Redshift allows the cluster to automatically add and remove compute resources in response to workload demands. Enabling concurrency scaling at the workload management (WLM) queue level allows you to specify which queues can benefit from concurrency scaling based on the query workload.

   2. S

      saransh_001

      2025-02-13

      Selected B4 upvotes

      Concurrency Scaling in Amazon Redshift is a feature that automatically adds temporary clusters to handle spikes in query traffic, providing additional read and write capacity. This feature is enabled through Workload Management (WLM) at the queue level in Redshift. Each queue can be configured to use concurrency scaling for handling queries that exceed the capacity of the main cluster. Why option A is incorrect: Turn on concurrency scaling in workload management (WLM) for Redshift Serverless workgroups: This option is for Redshift Serverless rather than clusters on RA3 nodes. Serverless clusters handle scaling differently and don't require manual concurrency scaling settings like the RA3 clusters.

   3. M

      milofficial

      2024-01-20

      Selected B4 upvotes

      https://docs.aws.amazon.com/redshift/latest/dg/concurrency-scaling-queues.html

   4. S

      San_Juan

      2024-08-22

      Selected B2 upvotes

      B. According to documentation, the "concurrency scaling" is set up in workload management queue (see comment below). A. discarted, because redshift serverless scales automatically (it doesn't need enable "concurrency scaling").

8. Question 8 of 25

   Select 2

   A data engineer must orchestrate a series of Amazon Athena queries that will run every day. Each query can run for more than 15 minutes. Which combination of steps will meet these requirements MOST cost-effectively? (Choose two.)

   • AUse an AWS Lambda function and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.
   • BCreate an AWS Step Functions workflow and add two states. Add the first state before the Lambda function. Configure the second state as a Wait state to periodically check whether the Athena query has finished using the Athena Boto3 get_query_execution API call. Configure the workflow to invoke the next query when the current query has finished running.
   • CUse an AWS Glue Python shell job and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.
   • DUse an AWS Glue Python shell script to run a sleep timer that checks every 5 minutes to determine whether the current Athena query has finished running successfully. Configure the Python shell script to invoke the next query when the current query has finished running.
   • EUse Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the Athena queries in AWS Batch.

   Show answer

   Correct answer: A, B

   How others answered

   • AB66%
   • BE16%

   Only 66% agreed on this answer — treat it as contested rather than settled.

   Explanation

   AWS Lambda can be effectively used to trigger Athena queries. By using the start_query_execution API from the Athena Boto3 client, you can programmatically start Athena queries. Lambda functions are cost-effective as they charge based on the compute time used, and there's no charge when the code is not running. However, Lambda has a maximum execution timeout of 15 minutes, which means it's not suitable for long-running operations but can be used to trigger or start queries. AWS Step Functions can orchestrate multiple AWS services in workflows. By using a Wait state, the workflow can periodically check the status of the Athena query, and proceed to the next step once the query is complete. This approach is more scalable and reliable compared to continuously running a Lambda function, as Step Functions can handle long-running processes better and can maintain the state of each step in the workflow.

   Adapted from community discussion by rralucard_ · 2024-02-05 · 10 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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   Question 8 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

   1. R

      rralucard_

      2024-02-05

      Selected A, B10 upvotes

      AWS Lambda can be effectively used to trigger Athena queries. By using the start_query_execution API from the Athena Boto3 client, you can programmatically start Athena queries. Lambda functions are cost-effective as they charge based on the compute time used, and there's no charge when the code is not running. However, Lambda has a maximum execution timeout of 15 minutes, which means it's not suitable for long-running operations but can be used to trigger or start queries. AWS Step Functions can orchestrate multiple AWS services in workflows. By using a Wait state, the workflow can periodically check the status of the Athena query, and proceed to the next step once the query is complete. This approach is more scalable and reliable compared to continuously running a Lambda function, as Step Functions can handle long-running processes better and can maintain the state of each step in the workflow.

   2. A

      arvehisa

      2024-03-30

      Selected A, B4 upvotes

      A: Lambda is a good option and it only trigger the athena not actually run it. No need 15 min for it. B. it mentioned a series of athena queries and it may means that one query should wait until the former one finished. B is the perfect way to do it. And lambda and step functions are very cost effective.

   3. C

      certplan

      2024-03-20

      2 upvotes

      Option E: Using Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate Athena queries in AWS Batch. While Amazon MWAA provides managed Apache Airflow environments, which can be used for orchestrating workflows, it might not be the most cost-effective option for orchestrating Athena queries due to: - **Complexity**: Setting up and managing an Amazon MWAA environment can introduce additional complexity and potentially higher costs compared to other options. - **Resource Allocation**: Amazon MWAA environments come with a minimum cost, regardless of usage, and managing resources in AWS Batch might not be as cost-efficient for this specific use case compared to simpler solutions like Step Functions or Glue Python shell scripts. Reference: [Amazon Managed Workflows for Apache Airflow Documentation]https://docs.aws.amazon.com/mwaa/index.html

   4. C

      certplan

      2024-03-20

      2 upvotes

      To justify the selection of options B and D as the most cost-effective combination for orchestrating Amazon Athena queries, let's refer to the official AWS documentation: 1. **AWS Step Functions Documentation**: - AWS Step Functions is a fully managed service provided by AWS for coordinating the components of distributed applications and microservices using visual workflows. With Step Functions, you can build workflows that execute a sequence of AWS Lambda functions, API calls, and other AWS services. The documentation provides information on how to create workflows, define states, and configure wait states for checking the status of tasks, which aligns with the requirements of orchestrating Amazon Athena queries. - Reference: [AWS Step Functions Documentation]https://docs.aws.amazon.com/step-functions/index.html

9. Question 9 of 25

   A retail company has a customer data hub in an Amazon S3 bucket. Employees from many countries use the data hub to support company-wide analytics. A governance team must ensure that the company's data analysts can access data only for customers who are within the same country as the analysts. Which solution will meet these requirements with the LEAST operational effort?

   • ACreate a separate table for each country's customer data. Provide access to each analyst based on the country that the analyst serves.
   • BRegister the S3 bucket as a data lake location in AWS Lake Formation. Use the Lake Formation row-level security features to enforce the company's access policies.
   • CMove the data to AWS Regions that are close to the countries where the customers are. Provide access to each analyst based on the country that the analyst serves.
   • DLoad the data into Amazon Redshift. Create a view for each country. Create separate IAM roles for each country to provide access to data from each country. Assign the appropriate roles to the analysts.

   Show answer

   Correct answer: B

   How others answered

   • B91%

   Explanation

   AWS Lake Formation: It's specifically designed for managing data lakes on AWS, providing capabilities for securing and controlling access to data. Row-Level Security: With Lake Formation, you can define fine-grained access control policies, including row-level security. This means you can enforce policies to restrict access to data based on specific conditions, such as the country associated with each customer. Least Operational Effort: Once the policies are defined within Lake Formation, they can be centrally managed and applied to the data in the S3 bucket without the need for creating separate tables or views for each country, as in options A, C, and D. This reduces operational overhead and complexity.

   Adapted from community discussion by k350Secops · 2024-05-09 · 12 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

   Supporting references

   • AWS documentation
   • AWS documentation

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   Candidate context

   Question 9 discussion

   These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

   1. K

      k350Secops

      2024-05-09

      Selected B12 upvotes

      AWS Lake Formation: It's specifically designed for managing data lakes on AWS, providing capabilities for securing and controlling access to data. Row-Level Security: With Lake Formation, you can define fine-grained access control policies, including row-level security. This means you can enforce policies to restrict access to data based on specific conditions, such as the country associated with each customer. Least Operational Effort: Once the policies are defined within Lake Formation, they can be centrally managed and applied to the data in the S3 bucket without the need for creating separate tables or views for each country, as in options A, C, and D. This reduces operational overhead and complexity.

   2. [

      [Removed]

      2024-01-21

      Selected B3 upvotes

      https://docs.aws.amazon.com/lake-formation/latest/dg/register-data-lake.html https://docs.aws.amazon.com/lake-formation/latest/dg/registration-role.html

   3. B

      BartoszGolebiowski24

      2024-02-08

      2 upvotes

      Creating DataLake takes at least few days to set up and the solution should be LEAST operational. I think B is not correct.

   4. M

      mattia_besharp

      2024-04-02

      Selected B1 upvote

      AWS really likes Lakeformation, plus creating separate tables might require some refactoring, and the requirements is about the LEAST operational effor

10. Question 10 of 25

    A company is migrating on-premises workloads to AWS. The company wants to reduce overall operational overhead. The company also wants to explore serverless options. The company's current workloads use Apache Pig, Apache Oozie, Apache Spark, Apache Hbase, and Apache Flink. The on-premises workloads process petabytes of data in seconds. The company must maintain similar or better performance after the migration to AWS. Which extract, transform, and load (ETL) service will meet these requirements?

    • AAWS Glue
    • BAmazon EMR
    • CAWS Lambda
    • DAmazon Redshift

    Show answer

    Correct answer: B

    How others answered

    • B82%
    • A18%

    Explanation

    Glue is like the more good-looking one, but weaker brother of EMR. So when it's about petabyte scales, let EMR do the work and have Glue stay away from the action.

    Adapted from community discussion by milofficial · 2024-01-20 · 18 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

    Supporting references

    • AWS documentation
    • AWS documentation
    • AWS documentation
    • AWS documentation
    • AWS documentation

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    Candidate context

    Question 10 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

    1. M

       milofficial

       2024-01-20

       Selected B18 upvotes

       Glue is like the more good-looking one, but weaker brother of EMR. So when it's about petabyte scales, let EMR do the work and have Glue stay away from the action.

    2. C

       certplan

       2024-03-20

       2 upvotes

       A. AWS Glue: AWS Glue is a fully managed extract, transform, and load (ETL) service provided by Amazon Web Services (AWS). It allows users to prepare and load data for analytics purposes B. Amazon EMR: Amazon Elastic MapReduce (EMR) is a cloud-based big data platform provided by AWS. It allows users to process and analyze large amounts of data using popular frameworks such as Apache Hadoop, Apache Spark, Apache Hive, Apache HBase, and more. https://docs.aws.amazon.com/emr/index.html https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-best-practices.html https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-manage.html https://docs.aws.amazon.com/emr/latest/DeveloperGuide/emr-developer-guide.html As per the AWS/Amazon docs, option B specifically calls out it out with the specific features/options that the question asked directly about.

    3. P

       pypelyncar

       2024-06-09

       Selected B3 upvotes

       EMR provides a managed Hadoop framework that natively supports Apache Pig, Oozie, Spark, and Flink. This allows the company to migrate their existing workloads with minimal code changes, reducing development effort

    4. H

       heavenlypearl

       2024-11-06

       Selected B2 upvotes

       Amazon EMR Serverless is a deployment option for Amazon EMR that provides a serverless runtime environment. This simplifies the operation of analytics applications that use the latest open-source frameworks, such as Apache Spark and Apache Hive. With EMR Serverless, you don’t have to configure, optimize, secure, or operate clusters to run applications with these frameworks. https://docs.aws.amazon.com/emr/latest/EMR-Serverless-UserGuide/emr-serverless.html

11. Question 11 of 25

    A company maintains multiple extract, transform, and load (ETL) workflows that ingest data from the company's operational databases into an Amazon S3 based data lake. The ETL workflows use AWS Glue and Amazon EMR to process data. The company wants to improve the existing architecture to provide automated orchestration and to require minimal manual effort. Which solution will meet these requirements with the LEAST operational overhead?

    • AAWS Glue workflows
    • BAWS Step Functions tasks
    • CAWS Lambda functions
    • DAmazon Managed Workflows for Apache Airflow (Amazon MWAA) workflows

    Show answer

    Correct answer: B

    How others answered

    • B74%
    • A26%

    Only 74% agreed on this answer — treat it as contested rather than settled.

    Explanation

    For me it's B because I did not found a possibility how Glue can trigger/orchestrate EMR processes OOTB. But with StepFunction there is a way: https://aws.amazon.com/blogs/big-data/orchestrate-amazon-emr-serverless-jobs-with-aws-step-functions/

    Adapted from community discussion by DevoteamAnalytix · 2024-05-03 · 7 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    • AWS documentation
    • AWS documentation
    • AWS documentation
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    Question 11 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

    1. D

       DevoteamAnalytix

       2024-05-03

       Selected B7 upvotes

       For me it's B because I did not found a possibility how Glue can trigger/orchestrate EMR processes OOTB. But with StepFunction there is a way: https://aws.amazon.com/blogs/big-data/orchestrate-amazon-emr-serverless-jobs-with-aws-step-functions/

    2. [

       [Removed]

       2024-01-21

       Selected B4 upvotes

       Orchestrating different AWS services is a typical use case for Step Functions: https://docs.aws.amazon.com/step-functions/latest/dg/connect-emr.html https://docs.aws.amazon.com/step-functions/latest/dg/connect-glue.html

    3. C

       certplan

       2024-03-20

       2 upvotes

       Here's an example of how you can use AWS Glue to initiate an EMR (Elastic MapReduce) job: Let's assume you have an AWS Glue job that performs ETL tasks on data stored in Amazon S3. You want to leverage EMR for a specific task within this job, such as running a complex Spark job. 1. Define a Glue Job: Create an AWS Glue job using the AWS Glue console, SDK, or CLI. Define the input and output data sources, as well as the transformations you want to apply. 2. Incorporate EMR Step: Within the Glue job script, include a section where you define an EMR step. An EMR step is a unit of work that performs a specific task on an EMR cluster. Code follows in the next entry...

    4. O

       ottarg

       2024-03-05

       3 upvotes

       Can you provide an example of Glue initiating an EMR job? Or somewhere in the documents? AFAIK, Glue workflows are only to be used for Glue related things e.g. pull data, transform it, and store it somewhere else (ETL). Executing commands on behalf of other services can be done using boto in glue, but it feels weird using Glue like that when you have step functions which are designed for orchestrating different services.

12. Question 12 of 25

    A company maintains an Amazon Redshift provisioned cluster that the company uses for extract, transform, and load (ETL) operations to support critical analysis tasks. A sales team within the company maintains a Redshift cluster that the sales team uses for business intelligence (BI) tasks. The sales team recently requested access to the data that is in the ETL Redshift cluster so the team can perform weekly summary analysis tasks. The sales team needs to join data from the ETL cluster with data that is in the sales team's BI cluster. The company needs a solution that will share the ETL cluster data with the sales team without interrupting the critical analysis tasks. The solution must minimize usage of the computing resources of the ETL cluster. Which solution will meet these requirements?

    • ASet up the sales team BI cluster as a consumer of the ETL cluster by using Redshift data sharing.
    • BCreate materialized views based on the sales team's requirements. Grant the sales team direct access to the ETL cluster.
    • CCreate database views based on the sales team's requirements. Grant the sales team direct access to the ETL cluster.
    • DUnload a copy of the data from the ETL cluster to an Amazon S3 bucket every week. Create an Amazon Redshift Spectrum table based on the content of the ETL cluster.

    Show answer

    Correct answer: A

    How others answered

    • A70%
    • D30%

    Only 70% agreed on this answer — treat it as contested rather than settled.

    Explanation

    To share data between Redshift clusters and meet the requirements of sharing ETL cluster data with the sales team without interrupting critical analysis tasks and minimizing the usage of the ETL cluster's computing resources, Redshift Data Sharing is the way to go https://docs.aws.amazon.com/redshift/latest/dg/data_sharing_intro.html "Supporting different kinds of business-critical workloads – Use a central extract, transform, and load (ETL) cluster that shares data with multiple business intelligence (BI) or analytic clusters. This approach provides read workload isolation and chargeback for individual workloads. You can size and scale your individual workload compute according to the workload-specific requirements of price and performance"

    Adapted from community discussion by [Removed] · 2024-01-21 · 4 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

    Supporting references

    • AWS documentation
    • AWS documentation
    • AWS documentation
    • AWS documentation

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    Candidate context

    Question 12 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

    1. [

       [Removed]

       2024-01-21

       Selected A4 upvotes

       To share data between Redshift clusters and meet the requirements of sharing ETL cluster data with the sales team without interrupting critical analysis tasks and minimizing the usage of the ETL cluster's computing resources, Redshift Data Sharing is the way to go https://docs.aws.amazon.com/redshift/latest/dg/data_sharing_intro.html "Supporting different kinds of business-critical workloads – Use a central extract, transform, and load (ETL) cluster that shares data with multiple business intelligence (BI) or analytic clusters. This approach provides read workload isolation and chargeback for individual workloads. You can size and scale your individual workload compute according to the workload-specific requirements of price and performance"

    2. A

       arvehisa

       2024-03-30

       Selected A5 upvotes

       A: redshift data sharing: https://docs.aws.amazon.com/redshift/latest/dg/data_sharing_intro.html With data sharing, you can securely and easily share live data across Amazon Redshift clusters. B: materialized view is only within 1 redshift cluster, across different tables

    3. G

       GiorgioGss

       2024-03-11

       Selected A5 upvotes

       Initially I would go with B but that definitely will use more resource.

    4. D

       d8945a1

       2024-05-07

       Selected A4 upvotes

       Typical usecase of datasharing in Redshift. The question mentions that - 'team needs to join data from the ETL cluster with data that is in the sales team's BI cluster.' This is possible with datashare.

13. Question 13 of 25

    A data engineer needs to join data from multiple sources to perform a one-time analysis job. The data is stored in Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3. Which solution will meet this requirement MOST cost-effectively?

    • AUse an Amazon EMR provisioned cluster to read from all sources. Use Apache Spark to join the data and perform the analysis.
    • BCopy the data from DynamoDB, Amazon RDS, and Amazon Redshift into Amazon S3. Run Amazon Athena queries directly on the S3 files.
    • CUse Amazon Athena Federated Query to join the data from all data sources.
    • DUse Redshift Spectrum to query data from DynamoDB, Amazon RDS, and Amazon S3 directly from Redshift.

    Show answer

    Correct answer: C

    How others answered

    • C100%

    Explanation

    I would go for C because Federated Query is typical for this porpouse. Besides, we don't need to add/duplicate resources in S3. But I see that, becasuse Athena is more optimized for S3, it can be considered a tricky question, since there can be more trade-offs to consider, such as data governance that are easier if data is centralized in S3 in my opinion.

    Adapted from community discussion by lucas_rfsb · 2024-09-30 · 7 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    • AWS documentation
    • AWS documentation
    • AWS documentation
    • AWS documentation

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    Question 13 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

    1. L

       lucas_rfsb

       2024-09-30

       Selected C7 upvotes

       I would go for C because Federated Query is typical for this porpouse. Besides, we don't need to add/duplicate resources in S3. But I see that, becasuse Athena is more optimized for S3, it can be considered a tricky question, since there can be more trade-offs to consider, such as data governance that are easier if data is centralized in S3 in my opinion.

    2. P

       pypelyncar

       2024-12-09

       Selected C4 upvotes

       Serverless Processing: Athena is a serverless query service, meaning you only pay for the queries you run. This eliminates the need to provision and manage compute resources like in EMR clusters, making it ideal for one-time jobs. Federated Query Capability: Athena Federated Query allows you to directly query data from various sources like DynamoDB, RDS, Redshift, and S3 without physically moving the data. This eliminates data movement costs and simplifies the analysis process. Reduced Cost for Large Datasets: Compared to copying data to S3, which can be expensive for large datasets, Athena Federated Query avoids unnecessary data movement, reducing overall costs.

    3. [

       [Removed]

       2024-07-20

       Selected C4 upvotes

       You can query these sources by using Federated Queries, which is a native feature of Athena. The other options may increase costs and operational overhead, as they use more than one service to achieve the same result https://docs.aws.amazon.com/athena/latest/ug/connectors-available.html

    4. C

       certplan

       2024-09-21

       2 upvotes

       Amazon Athena Federated Query allows you to query data from multiple federated data sources including relational databases, NoSQL databases, and object stores directly from Athena. While this might seem like an efficient way to join data from different sources without the need for copying data into Amazon S3, it's essential to consider the cost implications. AWS documentation on Amazon Athena Federated Query [1] explains that while Federated Query enables you to query data from external data sources without data movement, it does not eliminate data transfer costs. Depending on the data sources involved (such as Amazon RDS, DynamoDB, etc.), there might be data transfer costs associated with querying data directly from these sources. [1] Amazon Athena Federated Query Documentation: https://docs.aws.amazon.com/athena/latest/ug/federated-data-sources.html

14. Question 14 of 25

    A company wants to implement real-time analytics capabilities. The company wants to use Amazon Kinesis Data Streams and Amazon Redshift to ingest and process streaming data at the rate of several gigabytes per second. The company wants to derive near real-time insights by using existing business intelligence (BI) and analytics tools. Which solution will meet these requirements with the LEAST operational overhead?

    • AUse Kinesis Data Streams to stage data in Amazon S3. Use the COPY command to load data from Amazon S3 directly into Amazon Redshift to make the data immediately available for real-time analysis.
    • BAccess the data from Kinesis Data Streams by using SQL queries. Create materialized views directly on top of the stream. Refresh the materialized views regularly to query the most recent stream data.
    • CCreate an external schema in Amazon Redshift to map the data from Kinesis Data Streams to an Amazon Redshift object. Create a materialized view to read data from the stream. Set the materialized view to auto refresh.
    • DConnect Kinesis Data Streams to Amazon Kinesis Data Firehose. Use Kinesis Data Firehose to stage the data in Amazon S3. Use the COPY command to load the data from Amazon S3 to a table in Amazon Redshift.

    Show answer

    Correct answer: C

    How others answered

    • C56%
    • D39%

    Only 56% agreed on this answer — treat it as contested rather than settled.

    Explanation

    However, after some investigation, I found out that Amazon Kinesis Data Streams provides a way to ingest stream data directly into an Amazon Redshift cluster. https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-streaming-ingestion.html Materialized views with auto-refresh enabled will continuously ingest new data from the stream as it arrives, keeping the view updated with the latest data in real time. So I think the correct answer is C. The COPY command also supports loading data from streaming sources like Kinesis Data Streams or Kinesis Data Firehose. When used with these services, COPY provides a way to ingest real-time streaming data into Redshift tables. But this solution is not an option for this question.

    Adapted from community discussion by BartoszGolebiowski24 · 2024-02-11 · 5 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

    Supporting references

    • AWS documentation
    • AWS documentation
    • AWS documentation
    • AWS documentation
    • AWS documentation

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    Question 14 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

    1. B

       blackgamer

       2024-03-29

       Selected C8 upvotes

       The answer is C. It can provide near real-time insight analysis. Refer the article from AWS - https://aws.amazon.com/blogs/big-data/real-time-analytics-with-amazon-redshift-streaming-ingestion/

    2. H

       helpaws

       2024-03-16

       Selected C7 upvotes

       Key word here is near real-time. If it's involve S3 and COPY, it's not gonna be near real-time

    3. B

       BartoszGolebiowski24

       2024-02-11

       5 upvotes

       However, after some investigation, I found out that Amazon Kinesis Data Streams provides a way to ingest stream data directly into an Amazon Redshift cluster. https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-streaming-ingestion.html Materialized views with auto-refresh enabled will continuously ingest new data from the stream as it arrives, keeping the view updated with the latest data in real time. So I think the correct answer is C. The COPY command also supports loading data from streaming sources like Kinesis Data Streams or Kinesis Data Firehose. When used with these services, COPY provides a way to ingest real-time streaming data into Redshift tables. But this solution is not an option for this question.

    4. M

       markill123

       2024-09-14

       6 upvotes

       Redshift cannot create external schemas that map directly to Kinesis Data Streams. You would still need an intermediary step, such as Firehose or S3, to handle data ingestion. Additionally, maintaining auto-refreshing materialized views directly from a stream isn't feasible with Redshift.

15. Question 15 of 25

    Select 2

    A company uses an Amazon QuickSight dashboard to monitor usage of one of the company's applications. The company uses AWS Glue jobs to process data for the dashboard. The company stores the data in a single Amazon S3 bucket. The company adds new data every day. A data engineer discovers that dashboard queries are becoming slower over time. The data engineer determines that the root cause of the slowing queries is long-running AWS Glue jobs. Which actions should the data engineer take to improve the performance of the AWS Glue jobs? (Choose two.)

    • APartition the data that is in the S3 bucket. Organize the data by year, month, and day.
    • BIncrease the AWS Glue instance size by scaling up the worker type.
    • CConvert the AWS Glue schema to the DynamicFrame schema class.
    • DAdjust AWS Glue job scheduling frequency so the jobs run half as many times each day.
    • EModify the IAM role that grants access to AWS glue to grant access to all S3 features.

    Show answer

    Correct answer: A, B

    How others answered

    • AB100%

    Explanation

    1. **Partition the Data in Amazon S3**: - AWS documentation on optimizing Amazon S3 performance: https://docs.aws.amazon.com/AmazonS3/latest/userguide/optimizing-performance.html - AWS Glue documentation on partitioning data for AWS Glue jobs: https://docs.aws.amazon.com/glue/latest/dg/how-it-works.html#how-partitioning-works - Best practices for partitioning in Amazon S3: https://docs.aws.amazon.com/AmazonS3/latest/userguide/best-practices-partitioning.html 2. **Optimizing AWS Glue Job Settings**: - AWS Glue documentation on optimizing job performance: https://docs.aws.amazon.com/glue/latest/dg/best-practices.html - AWS Glue documentation on scaling AWS Glue job resources: https://docs.aws.amazon.com/glue/latest/dg/monitor-profile-glue-job-cloudwatch-metrics.html By referring to these documentation resources, the data engineer can gain insights into best practices and recommendations provided by AWS for optimizing AWS Glue jobs, thereby justifying the suggested actions to address the issue of slowing job performance.

    Adapted from community discussion by certplan · 2024-09-21 · 2 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    • AWS documentation
    • AWS documentation
    • AWS documentation
    • AWS documentation
    • AWS documentation

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    Question 15 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

    1. R

       rralucard_

       2024-08-03

       Selected A, B10 upvotes

       A. Partition the data that is in the S3 bucket. Organize the data by year, month, and day. • Partitioning data in Amazon S3 can significantly improve query performance. By organizing the data by year, month, and day, AWS Glue and Amazon QuickSight can scan only the relevant partitions of data, which reduces the amount of data read and processed. This approach is particularly effective for time-series data, where queries often target specific time ranges. B. Increase the AWS Glue instance size by scaling up the worker type. • Scaling up the worker type can provide more computational resources to the AWS Glue jobs, enabling them to process data faster. This can be especially beneficial when dealing with large datasets or complex transformations. It’s important to monitor the performance improvements and cost implications of scaling up.

    2. C

       certplan

       2024-09-21

       2 upvotes

       1. **Partition the Data in Amazon S3**: - AWS documentation on optimizing Amazon S3 performance: https://docs.aws.amazon.com/AmazonS3/latest/userguide/optimizing-performance.html - AWS Glue documentation on partitioning data for AWS Glue jobs: https://docs.aws.amazon.com/glue/latest/dg/how-it-works.html#how-partitioning-works - Best practices for partitioning in Amazon S3: https://docs.aws.amazon.com/AmazonS3/latest/userguide/best-practices-partitioning.html 2. **Optimizing AWS Glue Job Settings**: - AWS Glue documentation on optimizing job performance: https://docs.aws.amazon.com/glue/latest/dg/best-practices.html - AWS Glue documentation on scaling AWS Glue job resources: https://docs.aws.amazon.com/glue/latest/dg/monitor-profile-glue-job-cloudwatch-metrics.html By referring to these documentation resources, the data engineer can gain insights into best practices and recommendations provided by AWS for optimizing AWS Glue jobs, thereby justifying the suggested actions to address the issue of slowing job performance.

    3. T

       tgv

       2024-11-28

       2 upvotes

       It looks like there are various worker types in AWS Glue actually. I'll go with AB as well. "With AWS Glue, you only pay for the time your ETL job takes to run. There are no resources to manage, no upfront costs, and you are not charged for startup or shutdown time. You are charged an hourly rate based on the number of Data Processing Units (or DPUs) used to run your ETL job. A single Data Processing Unit (DPU) is also referred to as a worker. AWS Glue comes with three worker types to help you select the configuration that meets your job latency and cost requirements. Workers come in Standard, G.1X, G.2X, and G.025X configurations." https://docs.aws.amazon.com/glue/latest/dg/components-key-concepts.html

    4. D

       DevoteamAnalytix

       2024-11-03

       2 upvotes

       Here you can find 5 different Worker types: https://docs.aws.amazon.com/glue/latest/dg/add-job.html

16. Question 16 of 25

    A media company wants to improve a system that recommends media content to customer based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform. The company wants to minimize the effort and time required to incorporate third-party datasets. Which solution will meet these requirements with the LEAST operational overhead?

    • AUse API calls to access and integrate third-party datasets from AWS Data Exchange.
    • BUse API calls to access and integrate third-party datasets from AWS DataSync.
    • CUse Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories.
    • DUse Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR).

    Show answer

    Correct answer: A

    How others answered

    • A93%

    Explanation

    AWS DataSync is primarily used for data transfer services designed to simplify, automate, and accelerate moving data between on-premises storage systems and AWS storage services, as well as between different AWS storage services. Its primary role is not for accessing third-party datasets but for efficiently transferring large volumes of data. In contrast, AWS Data Exchange is designed specifically for discovering and subscribing to third-party data in the cloud, providing direct API access to these datasets, which aligns perfectly with the company's need to integrate this data into their recommendation systems with minimal overhead.

    Adapted from community discussion by KelvinPun · 2024-09-20 · 13 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    • AWS documentation

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    Question 16 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

    1. K

       KelvinPun

       2024-09-20

       Selected A13 upvotes

       AWS DataSync is primarily used for data transfer services designed to simplify, automate, and accelerate moving data between on-premises storage systems and AWS storage services, as well as between different AWS storage services. Its primary role is not for accessing third-party datasets but for efficiently transferring large volumes of data. In contrast, AWS Data Exchange is designed specifically for discovering and subscribing to third-party data in the cloud, providing direct API access to these datasets, which aligns perfectly with the company's need to integrate this data into their recommendation systems with minimal overhead.

    2. H

       hsnin

       2024-03-18

       Selected A4 upvotes

       AWS Data Exchange is a service that makes it easy to share and manage data permissions from other organizations

    3. G

       GiorgioGss

       2024-03-07

       Selected A3 upvotes

       A for me. "You can also discover and subscribe to new third-party data sets available through AWS Data Exchange" https://docs.aws.amazon.com/data-exchange/latest/userguide/what-is.html

    4. T

       ttpro1995

       2024-12-23

       Selected A3 upvotes

       Yeah, AWS want people to buy data from their marketplace. So, ... you know.

17. Question 17 of 25

    Select 2

    A company is building an analytics solution. The solution uses Amazon S3 for data lake storage and Amazon Redshift for a data warehouse. The company wants to use Amazon Redshift Spectrum to query the data that is in Amazon S3. Which actions will provide the FASTEST queries? (Choose two.)

    • AUse gzip compression to compress individual files to sizes that are between 1 GB and 5 GB.
    • BUse a columnar storage file format.
    • CPartition the data based on the most common query predicates.
    • DSplit the data into files that are less than 10 KB.
    • EUse file formats that are not splittable.

    Show answer

    Correct answer: B, C

    How others answered

    • BC100%

    Explanation

    B. Use a columnar storage file format: This is an excellent approach. Columnar storage formats like Parquet and ORC are highly recommended for use with Redshift Spectrum. They store data in columns, which allows Spectrum to scan only the needed columns for a query, significantly improving query performance and reducing the amount of data scanned. C. Partition the data based on the most common query predicates: Partitioning data in S3 based on commonly used query predicates (like date, region, etc.) allows Redshift Spectrum to skip large portions of data that are irrelevant to a particular query. This can lead to substantial performance improvements, especially for large datasets.

    Adapted from community discussion by rralucard_ · 2024-02-03 · 5 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    • AWS documentation
    • AWS documentation
    • AWS documentation

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    Candidate context

    Question 17 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

    1. R

       rralucard_

       2024-02-03

       Selected B, C5 upvotes

       B. Use a columnar storage file format: This is an excellent approach. Columnar storage formats like Parquet and ORC are highly recommended for use with Redshift Spectrum. They store data in columns, which allows Spectrum to scan only the needed columns for a query, significantly improving query performance and reducing the amount of data scanned. C. Partition the data based on the most common query predicates: Partitioning data in S3 based on commonly used query predicates (like date, region, etc.) allows Redshift Spectrum to skip large portions of data that are irrelevant to a particular query. This can lead to substantial performance improvements, especially for large datasets.

    2. G

       GiorgioGss

       2024-03-11

       Selected B, C6 upvotes

       https://docs.aws.amazon.com/redshift/latest/dg/c-spectrum-external-performance.html

    3. P

       pypelyncar

       2024-06-09

       Selected B, C3 upvotes

       Redshift Spectrum is optimized for querying data stored in columnar formats like Parquet or ORC. These formats store each data column separately, allowing Redshift Spectrum to only scan the relevant columns for a specific query, significantly improving performance compared to row-oriented formats Partitioning organizes data files in S3 based on specific column values (e.g., date, region). When your queries filter or join data based on these partitioning columns (common query predicates), Redshift Spectrum can quickly locate the relevant data files, minimizing the amount of data scanned and accelerating query execution

    4. C

       certplan

       2024-03-21

       1 upvote

       2. **Partitioning**: AWS documentation for Amazon Redshift Spectrum highlights the importance of partitioning data based on commonly used query predicates to improve query performance. By partitioning data, Redshift Spectrum can prune unnecessary partitions during query execution, reducing the amount of data scanned and improving overall query performance. This guidance can be found in the AWS documentation for Amazon Redshift Spectrum under "Using Partitioning to Improve Query Performance": https://docs.aws.amazon.com/redshift/latest/dg/c-using-spectrum-partitioning.html

18. Question 18 of 25

    Select 2

    A company uses Amazon RDS to store transactional data. The company runs an RDS DB instance in a private subnet. A developer wrote an AWS Lambda function with default settings to insert, update, or delete data in the DB instance. The developer needs to give the Lambda function the ability to connect to the DB instance privately without using the public internet. Which combination of steps will meet this requirement with the LEAST operational overhead? (Choose two.)

    • ATurn on the public access setting for the DB instance.
    • BUpdate the security group of the DB instance to allow only Lambda function invocations on the database port.
    • CConfigure the Lambda function to run in the same subnet that the DB instance uses.
    • DAttach the same security group to the Lambda function and the DB instance. Include a self-referencing rule that allows access through the database port.
    • EUpdate the network ACL of the private subnet to include a self-referencing rule that allows access through the database port.

    Show answer

    Correct answer: C, D

    How others answered

    • CD62%
    • BD24%

    Only 62% agreed on this answer — treat it as contested rather than settled.

    Explanation

    This solution only modifies the inbound rules of the security group of the DB instance, but it does not modify the outbound rules of the security group of the Lambda function. Additionally, this solution does not facilitate a private connection from the Lambda function to the DB instance, hence, the Lambda function would still need to use the public internet to access the DB instance. Therefore, this option does not fulfill the requirements.

    Adapted from community discussion by Alagong · 2024-03-29 · 6 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    Question 18 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

    1. A

       Alagong

       2024-03-29

       Selected C, D6 upvotes

       This solution only modifies the inbound rules of the security group of the DB instance, but it does not modify the outbound rules of the security group of the Lambda function. Additionally, this solution does not facilitate a private connection from the Lambda function to the DB instance, hence, the Lambda function would still need to use the public internet to access the DB instance. Therefore, this option does not fulfill the requirements.

    2. C

       certplan

       2024-03-21

       4 upvotes

       So, there coudl be a justified argument for the following: C. Configure the Lambda function to run in the same subnet that the DB instance uses: By running the Lambda function in the same subnet as the RDS DB instance, you enable them to communicate privately within the same network, eliminating the need for public internet access and reducing operational overhead. D. Attach the same security group to the Lambda function and the DB instance. Include a self-referencing rule that allows access through the database port: By attaching the same security group to both the Lambda function and the RDS DB instance, and including a self-referencing rule that allows access through the database port, you ensure secure communication between them within the same VPC without exposing the database to the public internet. This approach minimizes operational overhead by centralizing security management and simplifying access control.

    3. A

       arvehisa

       2024-03-31

       Selected C, D4 upvotes

       B: need update security group. and there there may be other application need to access db except for lambda function D: it works and reuse security group which has less operational overhead

    4. C

       certplan

       2024-03-21

       1 upvote

       Here's how you would implement this: 1. **Attach the same security group to both the Lambda function and the RDS DB instance**: Ensure that both resources are associated with the same security group. 2. **Create an inbound rule in the security group**: Configure the security group to allow inbound traffic on the database port (e.g., 3306 for MySQL) from the security group itself. For example, if the security group ID is sg-1234567890 and the database port is 3306, the inbound rule would look something like this: Type: Custom TCP Rule Protocol: TCP Port Range: 3306 (or the port your database uses) Source: sg-1234567890 (the security group ID itself) This rule allows the Lambda function, which is also part of the same security group, to communicate with the RDS DB instance through the specified port. It effectively creates a loopback or self-referencing rule within the security group, allowing internal communication between resources while maintaining security boundaries.

19. Question 19 of 25

    A company has a production AWS account that runs company workloads. The company's security team created a security AWS account to store and analyze security logs from the production AWS account. The security logs in the production AWS account are stored in Amazon CloudWatch Logs. The company needs to use Amazon Kinesis Data Streams to deliver the security logs to the security AWS account. Which solution will meet these requirements?

    • ACreate a destination data stream in the production AWS account. In the security AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the production AWS account.
    • BCreate a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the security AWS account.
    • CCreate a destination data stream in the production AWS account. In the production AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the security AWS account.
    • DCreate a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the production AWS account.

    Show answer

    Correct answer: D

    How others answered

    • D100%

    Explanation

    1. **Cross-Account Access:** - AWS Documentation: [Cross-Account Access] https://docs.aws.amazon.com/IAM/latest/UserGuide/tutorial_cross-account-with-roles.html - This documentation provides detailed instructions on how to set up cross-account access using IAM roles and trust policies, which is essential for allowing CloudWatch Logs in one AWS account to put data into a Kinesis Data Stream in another AWS account. 2. **Configuring CloudWatch Logs Subscription Filters:** - AWS Documentation: [Subscription Filters for Amazon CloudWatch Logs] https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/SubscriptionFilters.html - This documentation explains how to create subscription filters for CloudWatch Logs, which enable you to route log data to various destinations, including Kinesis Data Streams. Placing the subscription filter in the production AWS account ensures that only the relevant security logs are sent to the Kinesis Data Stream in the security AWS account.

    Adapted from community discussion by certplan · 2024-09-21 · 2 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    Question 19 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

    1. C

       Christina666

       2024-10-12

       Selected D6 upvotes

       Cross-Account Delivery: Kinesis Data Streams in the security account ensures the logs reside in the designated security-focused environment. CloudWatch Logs Integration: Granting CloudWatch Logs permissions to put records into the Kinesis Data Stream directly establishes a streamlined and secure data flow from the production account. Filtering Controls: The subscription filter in the production account provides precise control over which log events are sent to the security account.

    2. C

       certplan

       2024-09-21

       2 upvotes

       1. **Cross-Account Access:** - AWS Documentation: [Cross-Account Access] https://docs.aws.amazon.com/IAM/latest/UserGuide/tutorial_cross-account-with-roles.html - This documentation provides detailed instructions on how to set up cross-account access using IAM roles and trust policies, which is essential for allowing CloudWatch Logs in one AWS account to put data into a Kinesis Data Stream in another AWS account. 2. **Configuring CloudWatch Logs Subscription Filters:** - AWS Documentation: [Subscription Filters for Amazon CloudWatch Logs] https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/SubscriptionFilters.html - This documentation explains how to create subscription filters for CloudWatch Logs, which enable you to route log data to various destinations, including Kinesis Data Streams. Placing the subscription filter in the production AWS account ensures that only the relevant security logs are sent to the Kinesis Data Stream in the security AWS account.

    3. S

       Salam9

       2025-01-24

       Selected D2 upvotes

       https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/SubscriptionFilters-AccountLevel.html#DestinationKinesisExample-AccountLevel

    4. G

       GiorgioGss

       2024-09-11

       Selected D2 upvotes

       https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/CrossAccountSubscriptions-Kinesis.html

20. Question 20 of 25

    A company uses Amazon S3 to store semi-structured data in a transactional data lake. Some of the data files are small, but other data files are tens of terabytes. A data engineer must perform a change data capture (CDC) operation to identify changed data from the data source. The data source sends a full snapshot as a JSON file every day and ingests the changed data into the data lake. Which solution will capture the changed data MOST cost-effectively?

    • ACreate an AWS Lambda function to identify the changes between the previous data and the current data. Configure the Lambda function to ingest the changes into the data lake.
    • BIngest the data into Amazon RDS for MySQL. Use AWS Database Migration Service (AWS DMS) to write the changed data to the data lake.
    • CUse an open source data lake format to merge the data source with the S3 data lake to insert the new data and update the existing data.
    • DIngest the data into an Amazon Aurora MySQL DB instance that runs Aurora Serverless. Use AWS Database Migration Service (AWS DMS) to write the changed data to the data lake.

    Show answer

    Correct answer: C

    How others answered

    • C93%

    Explanation

    Relative to cost, here are docs for the reason for option C: https://docs.aws.amazon.com/AmazonS3/latest/dev/Welcome.html https://aws.amazon.com/blogs/big-data/ https://docs.aws.amazon.com/glue/latest/dg/welcome.html https://docs.aws.amazon.com/emr/ Here are docs for reasons the others are not correct: https://aws.amazon.com/lambda/pricing/ https://aws.amazon.com/rds/pricing/ https://aws.amazon.com/dms/pricing/

    Adapted from community discussion by certplan · 2024-03-22 · 2 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    • AWS documentation
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    Candidate context

    Question 20 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

    1. G

       GiorgioGss

       2024-03-11

       Selected C7 upvotes

       https://aws.amazon.com/blogs/big-data/implement-a-cdc-based-upsert-in-a-data-lake-using-apache-iceberg-and-aws-glue/

    2. G

       GiorgioGss

       2024-03-11

       6 upvotes

       "other data files are tens of terabytes" - good luck with DMS on that :) I think it's C

    3. [

       [Removed]

       2024-01-21

       Selected C4 upvotes

       This is a tricky one. Although option A seems like the best choice since it uses an AWS service, I believe using Delta/Iceberg APIs would be easier than writing custom code on Lambda

    4. H

       Houyon

       2024-02-13

       4 upvotes

       If all files were small I believe it would be a great idea. However, you wouldn't be able to compare heavy files with lambda due to its memory/capacity and runtime constraints

21. Question 21 of 25

    A company is planning to upgrade its Amazon Elastic Block Store (Amazon EBS) General Purpose SSD storage from gp2 to gp3. The company wants to prevent any interruptions in its Amazon EC2 instances that will cause data loss during the migration to the upgraded storage. Which solution will meet these requirements with the LEAST operational overhead?

    • ACreate snapshots of the gp2 volumes. Create new gp3 volumes from the snapshots. Attach the new gp3 volumes to the EC2 instances.
    • BCreate new gp3 volumes. Gradually transfer the data to the new gp3 volumes. When the transfer is complete, mount the new gp3 volumes to the EC2 instances to replace the gp2 volumes.
    • CChange the volume type of the existing gp2 volumes to gp3. Enter new values for volume size, IOPS, and throughput.
    • DUse AWS DataSync to create new gp3 volumes. Transfer the data from the original gp2 volumes to the new gp3 volumes.

    Show answer

    Correct answer: C

    How others answered

    • C100%

    Explanation

    Option C: Check section under "To modify an Amazon EBS volume using the AWS Management Console“ in GiorgioGss's link Amazon EBS Elastic Volumes enable you to modify your volume type from gp2 to gp3 without detaching volumes or restarting instances (requirements for modification), which means that there are no interruptions to your applications during modification.

    Adapted from community discussion by fceb2c1 · 2024-09-24 · 6 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    • AWS documentation
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    Question 21 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

    1. F

       fceb2c1

       2024-09-24

       Selected C6 upvotes

       Option C: Check section under "To modify an Amazon EBS volume using the AWS Management Console“ in GiorgioGss's link Amazon EBS Elastic Volumes enable you to modify your volume type from gp2 to gp3 without detaching volumes or restarting instances (requirements for modification), which means that there are no interruptions to your applications during modification.

    2. G

       GiorgioGss

       2024-09-19

       Selected C6 upvotes

       https://aws.amazon.com/blogs/storage/migrate-your-amazon-ebs-volumes-from-gp2-to-gp3-and-save-up-to-20-on-costs/

    3. R

       rralucard_

       2024-08-04

       Selected C2 upvotes

       Option C is the most straightforward and efficient approach to upgrading from gp2 to gp3 EBS volumes, providing an in-place upgrade path with minimal operational overhead and no interruption in service.

    4. L

       lcsantos99

       2025-01-26

       Selected C1 upvote

       the correct answer is C https://aws.amazon.com/pt/blogs/storage/migrate-your-amazon-ebs-volumes-from-gp2-to-gp3-and-save-up-to-20-on-costs/

22. Question 22 of 25

    A company is migrating its database servers from Amazon EC2 instances that run Microsoft SQL Server to Amazon RDS for Microsoft SQL Server DB instances. The company's analytics team must export large data elements every day until the migration is complete. The data elements are the result of SQL joins across multiple tables. The data must be in Apache Parquet format. The analytics team must store the data in Amazon S3. Which solution will meet these requirements in the MOST operationally efficient way?

    • ACreate a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create an AWS Glue job that selects the data directly from the view and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.
    • BSchedule SQL Server Agent to run a daily SQL query that selects the desired data elements from the EC2 instance-based SQL Server databases. Configure the query to direct the output .csv objects to an S3 bucket. Create an S3 event that invokes an AWS Lambda function to transform the output format from .csv to Parquet.
    • CUse a SQL query to create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create and run an AWS Glue crawler to read the view. Create an AWS Glue job that retrieves the data and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.
    • DCreate an AWS Lambda function that queries the EC2 instance-based databases by using Java Database Connectivity (JDBC). Configure the Lambda function to retrieve the required data, transform the data into Parquet format, and transfer the data into an S3 bucket. Use Amazon EventBridge to schedule the Lambda function to run every day.

    Show answer

    Correct answer: A

    How others answered

    • A53%
    • C47%

    Only 53% agreed on this answer — treat it as contested rather than settled.

    Explanation

    Option A involves creating a view in the EC2 instance-based SQL Server databases that contains the required data elements. An AWS Glue job is then created to select the data directly from the view and transfer the data in Parquet format to an S3 bucket. This job is scheduled to run every day. This approach is operationally efficient as it leverages managed services (AWS Glue) and does not require additional transformation steps. Option D involves creating an AWS Lambda function that queries the EC2 instance-based databases using JDBC. The Lambda function is configured to retrieve the required data, transform the data into Parquet format, and transfer the data into an S3 bucket. This approach could work, but managing and scheduling Lambda functions could add operational overhead compared to using managed services like AWS Glue.

    Adapted from community discussion by bakarys · 2024-07-01 · 3 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    Question 22 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

    1. B

       bakarys

       2024-07-01

       Selected A3 upvotes

       Option A involves creating a view in the EC2 instance-based SQL Server databases that contains the required data elements. An AWS Glue job is then created to select the data directly from the view and transfer the data in Parquet format to an S3 bucket. This job is scheduled to run every day. This approach is operationally efficient as it leverages managed services (AWS Glue) and does not require additional transformation steps. Option D involves creating an AWS Lambda function that queries the EC2 instance-based databases using JDBC. The Lambda function is configured to retrieve the required data, transform the data into Parquet format, and transfer the data into an S3 bucket. This approach could work, but managing and scheduling Lambda functions could add operational overhead compared to using managed services like AWS Glue.

    2. M

       mtrianac

       2024-12-04

       Selected A3 upvotes

       No, in this case, using an AWS Glue Crawler is not necessary. The schema is already defined in the SQL Server database, as the created view contains the required structure (columns and data types). AWS Glue can directly connect to the database via JDBC, extract the data, transform it into Parquet format, and store it in S3 without additional steps. A crawler is useful if you're working with data that doesn't have a predefined schema (e.g., files in S3) or if you need the data to be cataloged for services like Amazon Athena. However, for this ETL flow, using just a Glue Job simplifies the process and reduces operational complexity.

    3. R

       rralucard_

       2024-02-04

       Selected A3 upvotes

       Option A (Creating a view in the EC2 instance-based SQL Server databases and creating an AWS Glue job that selects data from the view, transfers it in Parquet format to S3, and schedules the job to run every day) seems to be the most operationally efficient solution. It leverages AWS Glue’s ETL capabilities for direct data extraction and transformation, minimizes manual steps, and effectively automates the process.

    4. L

       leonardoFelipe

       2024-11-03

       Selected A3 upvotes

       Usually, views aren't true objects in a SGBD, they're just a "nickname" for a specific query string, different of Materialized Views. So, my questions is: can glue crawler understand their metadata? I'd go with A

23. Question 23 of 25

    A company uses Amazon Redshift for its data warehouse. The company must automate refresh schedules for Amazon Redshift materialized views. Which solution will meet this requirement with the LEAST effort?

    • AUse Apache Airflow to refresh the materialized views.
    • BUse an AWS Lambda user-defined function (UDF) within Amazon Redshift to refresh the materialized views.
    • CUse the query editor v2 in Amazon Redshift to refresh the materialized views.
    • DUse an AWS Glue workflow to refresh the materialized views.

    Show answer

    Correct answer: C

    How others answered

    • C88%

    Explanation

    Amazon Redshift can automatically refresh materialized views with up-to-date data from its base tables when materialized views are created with or altered to have the autorefresh option. For more details, refer to the documentation here, https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-refresh.html.

    Adapted from community discussion by Christina666 · 2024-10-13 · 2 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    Question 23 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

    1. P

       pypelyncar

       2024-12-10

       Selected C2 upvotes

       the company can automate the refresh schedules for materialized views with minimal effort. This approach leverages the built-in capabilities of Amazon Redshift, reducing the need for additional services, configurations, or custom code. It aligns with the principle of using the simplest and most straightforward solution that meets the requirements, minimizing operational overhead and complexity

    2. D

       d8945a1

       2024-11-08

       Selected C3 upvotes

       We can schedule the refresh using query scheduler from Query Editor V2.

    3. C

       Christina666

       2024-10-13

       Selected C2 upvotes

       Amazon Redshift can automatically refresh materialized views with up-to-date data from its base tables when materialized views are created with or altered to have the autorefresh option. For more details, refer to the documentation here, https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-refresh.html.

    4. F

       FuriouZ

       2024-09-23

       Selected C2 upvotes

       You can set autorefresh for materialized views using CREATE MATERIALIZED VIEW. You can also use the AUTO REFRESH clause to refresh materialized views automatically.

24. Question 24 of 25

    A company created an extract, transform, and load (ETL) data pipeline in AWS Glue. A data engineer must crawl a table that is in Microsoft SQL Server. The data engineer needs to extract, transform, and load the output of the crawl to an Amazon S3 bucket. The data engineer also must orchestrate the data pipeline. Which AWS service or feature will meet these requirements MOST cost-effectively?

    • AAWS Step Functions
    • BAWS Glue workflows
    • CAWS Glue Studio
    • DAmazon Managed Workflows for Apache Airflow (Amazon MWAA)

    Show answer

    Correct answer: B

    How others answered

    • B100%

    Explanation

    I asked an AI. Analysis of the answers: A. AWS Step Functions: It is a good option for orchestrating workflows with steps from different AWS services, but requires additional development to connect to Microsoft SQL Server. B. AWS Glue Workflows: This is the best and most profitable option. AWS Glue is designed specifically for ETL on AWS and integrates directly with data sources such as Microsoft SQL Server through connectors. This allows for easier configuration and avoids the need for additional development. C. AWS Glue Studio: It is a visual interface for AWS Glue that makes it easy to create and manage ETL jobs. However, the underlying functionality comes from AWS Glue (B) workflows. D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA): It's a viable option, but it's generally more expensive than native AWS services like AWS Glue Workflows. Additionally, it requires some Airflow experience for setup and maintenance.

    Adapted from community discussion by dev_vicente · 2024-09-23 · 7 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

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    Question 24 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

    1. D

       dev_vicente

       2024-09-23

       Selected B7 upvotes

       I asked an AI. Analysis of the answers: A. AWS Step Functions: It is a good option for orchestrating workflows with steps from different AWS services, but requires additional development to connect to Microsoft SQL Server. B. AWS Glue Workflows: This is the best and most profitable option. AWS Glue is designed specifically for ETL on AWS and integrates directly with data sources such as Microsoft SQL Server through connectors. This allows for easier configuration and avoids the need for additional development. C. AWS Glue Studio: It is a visual interface for AWS Glue that makes it easy to create and manage ETL jobs. However, the underlying functionality comes from AWS Glue (B) workflows. D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA): It's a viable option, but it's generally more expensive than native AWS services like AWS Glue Workflows. Additionally, it requires some Airflow experience for setup and maintenance.

    2. M

       milofficial

       2024-01-18

       Selected B9 upvotes

       Glue workflows are the easiest solution here: https://aws.amazon.com/blogs/big-data/orchestrate-an-etl-pipeline-using-aws-glue-workflows-triggers-and-crawlers-with-custom-classifiers/ https://aws.amazon.com/blogs/big-data/extracting-multidimensional-data-from-microsoft-sql-server-analysis-services-using-aws-glue/

    3. D

       DevoteamAnalytix

       2024-05-03

       Selected B3 upvotes

       Agree with B. CRAWLING and ETL are the main functions of a Glue workflow and MS SQL is supported: https://docs.aws.amazon.com/glue/latest/dg/crawler-data-stores.html

    4. A

       Adrifersilva

       2024-09-30

       Selected B1 upvote

       https://community.aws/content/2iBQiAGS4RvEolgSQKu4iF8InTV/choose-the-right-data-orchestration-service-for-your-data-pipeline?lang=en

25. Question 25 of 25

    A data engineer is configuring Amazon SageMaker Studio to use AWS Glue interactive sessions to prepare data for machine learning (ML) models. The data engineer receives an access denied error when the data engineer tries to prepare the data by using SageMaker Studio. Which change should the engineer make to gain access to SageMaker Studio?

    • AAdd the AWSGlueServiceRole managed policy to the data engineer's IAM user.
    • BAdd a policy to the data engineer's IAM user that includes the sts:AssumeRole action for the AWS Glue and SageMaker service principals in the trust policy.
    • CAdd the AmazonSageMakerFullAccess managed policy to the data engineer's IAM user.
    • DAdd a policy to the data engineer's IAM user that allows the sts:AddAssociation action for the AWS Glue and SageMaker service principals in the trust policy.

    Show answer

    Correct answer: B

    How others answered

    • B61%
    • C39%

    Only 61% agreed on this answer — treat it as contested rather than settled.

    Explanation

    B. Add a policy to the data engineer’s IAM user that includes the sts:AssumeRole action for the AWS Glue and SageMaker service principals in the trust policy. • This is the most appropriate solution. The sts:AssumeRole action allows the data engineer’s IAM user to assume a role that has the necessary permissions for both AWS Glue and SageMaker. This is a common approach for granting cross-service access in AWS.

    Adapted from community discussion by atu1789 · 2024-02-17 · 2 community upvotes. Third-party contribution; reviewed by our quality filter, not independently verified.

    Supporting references

    • AWS documentation
    • repost.aws documentation
    • AWS documentation

    View community discussion4

    Candidate context

    Question 25 discussion

    These are quality-filtered excerpts from third-party community discussion. They can be useful reasoning signals, but they are not official answers and have not been independently verified by DEA-C01 or CertExamHQ.

    1. T

       tgv

       2024-05-28

       Selected B6 upvotes

       I don't believe you're supposed to assign a FullAccess policy, so I will go with B.

    2. G

       GiorgioGss

       2024-03-19

       Selected B5 upvotes

       I will go with B since you can get access denied even with the AmazonSageMakerFullAccess. See here: https://stackoverflow.com/questions/64709871/aws-sagemaker-studio-createdomain-access-error

    3. A

       atu1789

       2024-02-17

       Selected B2 upvotes

       B. Add a policy to the data engineer’s IAM user that includes the sts:AssumeRole action for the AWS Glue and SageMaker service principals in the trust policy. • This is the most appropriate solution. The sts:AssumeRole action allows the data engineer’s IAM user to assume a role that has the necessary permissions for both AWS Glue and SageMaker. This is a common approach for granting cross-service access in AWS.

    4. N

       nyaopoko

       2024-04-06

       2 upvotes

       Option A (AWSGlueServiceRole managed policy) is not relevant, as this policy is intended for the AWS Glue service itself, not for users accessing SageMaker Studio. Option B (adding a policy with sts:AssumeRole action) is not necessary, as SageMaker handles the role assumption process internally. Option D (sts:AddAssociation action) is not a valid action and is not required for accessing SageMaker Studio or using AWS Glue interactive sessions.

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DEA-C01 practice, question 1 of 25