DEA-C01 practice questions 171–180

1. Question 171 · 313 in bank

   A company's data engineer needs to optimize the performance of table SQL queries. The company stores data in an Amazon Redshift cluster. The data engineer cannot increase the size of the cluster because of budget constraints. The company stores the data in multiple tables and loads the data by using the EVEN distribution style. Some tables are hundreds of gigabytes in size. Other tables are less than 10 MB in size. Which solution will meet these requirements?

   • AKeep using the EVEN distribution style for all tables. Specify primary and foreign keys for all tables.
   • BUse the ALL distribution style for large tables. Specify primary and foreign keys for all tables.
   • CUse the ALL distribution style for rarely updated small tables. Specify primary and foreign keys for all tables.
   • DSpecify a combination of distribution, sort, and partition keys for all tables.

   Show answer

   Answer: C

   Explanation

   Use the ALL Distribution Style for Rarely Updated Small Tables. This approach optimizes the performance of joins involving these smaller tables and is a common best practice in Redshift data warehousing. For the larger tables, maintaining the EVEN distribution style or considering a KEY-based distribution (if there are common join columns) could be more appropriate.

   Source: Adapted from community discussion — rralucard_

   References

   • AWS documentation

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2. Question 172 · 313 in bank

   A company receives .csv files that contain physical address data. The data is in columns that have the following names: Door_No, Street_Name, City, and Zip_Code. The company wants to create a single column to store these values in the following format:

   

   Which solution will meet this requirement with the LEAST coding effort?

   • AUse AWS Glue DataBrew to read the files. Use the NEST_TO_ARRAY transformation to create the new column.
   • BUse AWS Glue DataBrew to read the files. Use the NEST_TO_MAP transformation to create the new column.
   • CUse AWS Glue DataBrew to read the files. Use the PIVOT transformation to create the new column.
   • DWrite a Lambda function in Python to read the files. Use the Python data dictionary type to create the new column.

   Show answer

   Answer: B

   Explanation

   NEST_TO_ARRAY would result in: [ {"key": "key1", "value": "value1"}, {"key": "key2", "value": "value2"}, {"key": "key3", "value": "value3"}] while NEST_TO_MAP results: { "key1": "value1", "key2": "value2", "key3": "value3" } Therefore go with B

   Source: Adapted from community discussion — FuriouZ

   References

   • AWS documentation
   • AWS documentation

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3. Question 173 · 313 in bank

   A company receives call logs as Amazon S3 objects that contain sensitive customer information. The company must protect the S3 objects by using encryption. The company must also use encryption keys that only specific employees can access. Which solution will meet these requirements with the LEAST effort?

   • AUse an AWS CloudHSM cluster to store the encryption keys. Configure the process that writes to Amazon S3 to make calls to CloudHSM to encrypt and decrypt the objects. Deploy an IAM policy that restricts access to the CloudHSM cluster.
   • BUse server-side encryption with customer-provided keys (SSE-C) to encrypt the objects that contain customer information. Restrict access to the keys that encrypt the objects.
   • CUse server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the objects that contain customer information. Configure an IAM policy that restricts access to the KMS keys that encrypt the objects.
   • DUse server-side encryption with Amazon S3 managed keys (SSE-S3) to encrypt the objects that contain customer information. Configure an IAM policy that restricts access to the Amazon S3 managed keys that encrypt the objects.

   Show answer

   Answer: C

   Explanation

   C. Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the objects that contain customer information. Configure an IAM policy that restricts access to the KMS keys that encrypt the objects. Server-side encryption with AWS KMS (SSE-KMS) provides strong encryption for S3 objects while allowing fine-grained access control through AWS Key Management Service (KMS). With SSE-KMS, you can control access to encryption keys using IAM policies, ensuring that only specific employees can access them. This solution requires minimal effort as it leverages AWS's managed encryption service (SSE-KMS) and integrates seamlessly with S3. Additionally, IAM policies can be easily configured to restrict access to the KMS keys, providing granular control over who can access the encryption keys.

   Source: Adapted from community discussion — Ousseyni

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4. Question 174 · 313 in bank

   A financial services company stores financial data in Amazon Redshift. A data engineer wants to run real-time queries on the financial data to support a web-based trading application. The data engineer wants to run the queries from within the trading application. Which solution will meet these requirements with the LEAST operational overhead?

   • AEstablish WebSocket connections to Amazon Redshift.
   • BUse the Amazon Redshift Data API.
   • CSet up Java Database Connectivity (JDBC) connections to Amazon Redshift.
   • DStore frequently accessed data in Amazon S3. Use Amazon S3 Select to run the queries.

   Show answer

   Answer: B

   Explanation

   "The Amazon Redshift Data API enables you to painlessly access data from Amazon Redshift with all types of traditional, cloud-native, and containerized, serverless web service-based applications and event-driven applications." https://aws.amazon.com/de/blogs/big-data/using-the-amazon-redshift-data-api-to-interact-with-amazon-redshift-clusters/#:~:text=The%20Amazon%20Redshift%20Data%20API%20is%20not%20a%20replacement%20for,supported%20by%20the%20AWS%20SDK.

   Source: Adapted from community discussion — DevoteamAnalytix

   References

   • AWS documentation
   • AWS documentation

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5. Question 175 · 313 in bank

   A company stores petabytes of data in thousands of Amazon S3 buckets in the S3 Standard storage class. The data supports analytics workloads that have unpredictable and variable data access patterns. The company does not access some data for months. However, the company must be able to retrieve all data within milliseconds. The company needs to optimize S3 storage costs. Which solution will meet these requirements with the LEAST operational overhead?

   • AUse S3 Storage Lens standard metrics to determine when to move objects to more cost-optimized storage classes. Create S3 Lifecycle policies for the S3 buckets to move objects to cost-optimized storage classes. Continue to refine the S3 Lifecycle policies in the future to optimize storage costs.
   • BUse S3 Storage Lens activity metrics to identify S3 buckets that the company accesses infrequently. Configure S3 Lifecycle rules to move objects from S3 Standard to the S3 Standard-Infrequent Access (S3 Standard-IA) and S3 Glacier storage classes based on the age of the data.
   • CUse S3 Intelligent-Tiering. Activate the Deep Archive Access tier.
   • DUse S3 Intelligent-Tiering. Use the default access tier.

   Show answer

   Answer: D

   Explanation

   Staying with "D"... The Amazon S3 Glacier Deep Archive storage class is designed for long-term data archiving where data retrieval times are flexible. It does not offer millisecond retrieval times. Instead, data retrieval from S3 Glacier Deep Archive typically takes 12 hours or more. For millisecond retrieval times, you would use the S3 Standard, S3 Standard-IA, or S3 One Zone-IA storage classes, which are designed for frequent or infrequent access with low latency.

   Source: Adapted from community discussion — rpwags

   References

   • AWS documentation

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6. Question 176 · 313 in bank

   Select 2

   During a security review, a company identified a vulnerability in an AWS Glue job. The company discovered that credentials to access an Amazon Redshift cluster were hard coded in the job script. A data engineer must remediate the security vulnerability in the AWS Glue job. The solution must securely store the credentials. Which combination of steps should the data engineer take to meet these requirements? (Choose two.)

   • AStore the credentials in the AWS Glue job parameters.
   • BStore the credentials in a configuration file that is in an Amazon S3 bucket.
   • CAccess the credentials from a configuration file that is in an Amazon S3 bucket by using the AWS Glue job.
   • DStore the credentials in AWS Secrets Manager.
   • EGrant the AWS Glue job IAM role access to the stored credentials.

   Show answer

   Answer: DE

   Explanation

   D because it's AWS best practice for securing creds and E because after you put cred in secrets you will need permissions for accesing

   Source: Adapted from community discussion — GiorgioGss

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7. Question 177 · 313 in bank

   A data engineer uses Amazon Redshift to run resource-intensive analytics processes once every month. Every month, the data engineer creates a new Redshift provisioned cluster. The data engineer deletes the Redshift provisioned cluster after the analytics processes are complete every month. Before the data engineer deletes the cluster each month, the data engineer unloads backup data from the cluster to an Amazon S3 bucket. The data engineer needs a solution to run the monthly analytics processes that does not require the data engineer to manage the infrastructure manually. Which solution will meet these requirements with the LEAST operational overhead?

   • AUse Amazon Step Functions to pause the Redshift cluster when the analytics processes are complete and to resume the cluster to run new processes every month.
   • BUse Amazon Redshift Serverless to automatically process the analytics workload.
   • CUse the AWS CLI to automatically process the analytics workload.
   • DUse AWS CloudFormation templates to automatically process the analytics workload.

   Show answer

   Answer: B

   Explanation

   Fully Managed, Serverless: Redshift Serverless eliminates the need to manually create, manage, or delete clusters. It automatically scales resources based on the workload, reducing operational overhead significantly. Cost-Effective for Infrequent Workloads: Since the analytics processes run only once a month, Redshift Serverless's pay-per-use model is ideal for minimizing costs during downtime. Seamless S3 Integration: Redshift Serverless natively integrates with S3 for backup and restore operations, ensuring compatibility with the existing process.

   Source: Adapted from community discussion — Christina666

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8. Question 178 · 313 in bank

   A company receives a daily file that contains customer data in .xls format. The company stores the file in Amazon S3. The daily file is approximately 2 GB in size. A data engineer concatenates the column in the file that contains customer first names and the column that contains customer last names. The data engineer needs to determine the number of distinct customers in the file. Which solution will meet this requirement with the LEAST operational effort?

   • ACreate and run an Apache Spark job in an AWS Glue notebook. Configure the job to read the S3 file and calculate the number of distinct customers.
   • BCreate an AWS Glue crawler to create an AWS Glue Data Catalog of the S3 file. Run SQL queries from Amazon Athena to calculate the number of distinct customers.
   • CCreate and run an Apache Spark job in Amazon EMR Serverless to calculate the number of distinct customers.
   • DUse AWS Glue DataBrew to create a recipe that uses the COUNT_DISTINCT aggregate function to calculate the number of distinct customers.

   Show answer

   Answer: D

   Explanation

   AWS Glue DataBrew: AWS Glue DataBrew is a visual data preparation tool that allows data engineers and data analysts to clean and normalize data without writing code. Using DataBrew, a data engineer could create a recipe that includes the concatenation of the customer first and last names and then use the COUNT_DISTINCT function. This would not require complex code and could be performed through the DataBrew user interface, representing a lower operational effort.

   Source: Adapted from community discussion — rralucard_

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9. Question 179 · 313 in bank

   A healthcare company uses Amazon Kinesis Data Streams to stream real-time health data from wearable devices, hospital equipment, and patient records. A data engineer needs to find a solution to process the streaming data. The data engineer needs to store the data in an Amazon Redshift Serverless warehouse. The solution must support near real-time analytics of the streaming data and the previous day's data. Which solution will meet these requirements with the LEAST operational overhead?

   • ALoad data into Amazon Kinesis Data Firehose. Load the data into Amazon Redshift.
   • BUse the streaming ingestion feature of Amazon Redshift.
   • CLoad the data into Amazon S3. Use the COPY command to load the data into Amazon Redshift.
   • DUse the Amazon Aurora zero-ETL integration with Amazon Redshift.

   Show answer

   Answer: B

   Explanation

   https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-streaming-ingestion.html Use the Streaming Ingestion Feature of Amazon Redshift: Amazon Redshift recently introduced streaming data ingestion, allowing Redshift to consume data directly from Kinesis Data Streams in near real-time. This feature simplifies the architecture by eliminating the need for intermediate steps or services, and it is specifically designed to support near real-time analytics. The operational overhead is minimal since the feature is integrated within Redshift.

   Source: Adapted from community discussion — rralucard_

   References

   • AWS documentation

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10. Question 180 · 313 in bank

    Select 2

    A data engineer needs to use an Amazon QuickSight dashboard that is based on Amazon Athena queries on data that is stored in an Amazon S3 bucket. When the data engineer connects to the QuickSight dashboard, the data engineer receives an error message that indicates insufficient permissions. Which factors could cause to the permissions-related errors? (Choose two.)

    • AThere is no connection between QuickSight and Athena.
    • BThe Athena tables are not cataloged.
    • CQuickSight does not have access to the S3 bucket.
    • DQuickSight does not have access to decrypt S3 data.
    • EThere is no IAM role assigned to QuickSight.

    Show answer

    Answer: CD

    Explanation

    C. QuickSight does not have access to the S3 bucket: Amazon QuickSight needs to have the necessary permissions to access the S3 bucket where the data resides. If QuickSight lacks the permissions to read the data from the S3 bucket, it would result in an error indicating insufficient permissions. D. QuickSight does not have access to decrypt S3 data: If the data in S3 is encrypted, QuickSight needs permissions to use the necessary keys to decrypt the data. Without access to the decryption keys, typically managed by AWS Key Management Service (KMS), QuickSight cannot read the encrypted data and would give an error.

    Source: Adapted from community discussion — rralucard_

    References

    • AWS documentation

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DEA-C01 practice, question 171. 313 questions available.