DEA-C01 practice questions 31–40

1. Question 31 · 313 in bank

   A company is creating near real-time dashboards to visualize time series data. The company ingests data into Amazon Managed Streaming for Apache Kafka (Amazon MSK). A customized data pipeline consumes the data. The pipeline then writes data to Amazon Keyspaces (for Apache Cassandra), Amazon OpenSearch Service, and Apache Avro objects in Amazon S3. Which solution will make the data available for the data visualizations with the LEAST latency?

   • ACreate OpenSearch Dashboards by using the data from OpenSearch Service.
   • BUse Amazon Athena with an Apache Hive metastore to query the Avro objects in Amazon S3. Use Amazon Managed Grafana to connect to Athena and to create the dashboards.
   • CUse Amazon Athena to query the data from the Avro objects in Amazon S3. Configure Amazon Keyspaces as the data catalog. Connect Amazon QuickSight to Athena to create the dashboards.
   • DUse AWS Glue to catalog the data. Use S3 Select to query the Avro objects in Amazon S3. Connect Amazon QuickSight to the S3 bucket to create the dashboards.

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   Answer: A

   Explanation

   Option A: Create OpenSearch Dashboards by using the data from OpenSearch Service is the best choice for achieving the least latency. OpenSearch is designed for low-latency data retrieval and visualization, making it ideal for near real-time dashboards

   Source: Adapted from community discussion — matt200

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2. Question 32 · 313 in bank

   A data engineer maintains a materialized view that is based on an Amazon Redshift database. The view has a column named load_date that stores the date when each row was loaded. The data engineer needs to reclaim database storage space by deleting all the rows from the materialized view. Which command will reclaim the MOST database storage space?

   • ADELETE FROM materialized_view_name where 1=1
   • BTRUNCATE materialized_view_name
   • CVACUUM table_name where load_date<=current_date materializedview
   • DDELETE FROM materialized_view_name where load_date<=current_date

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   Answer: B

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3. Question 33 · 313 in bank

   A media company wants to use Amazon OpenSearch Service to analyze rea-time data about popular musical artists and songs. The company expects to ingest millions of new data events every day. The new data events will arrive through an Amazon Kinesis data stream. The company must transform the data and then ingest the data into the OpenSearch Service domain. Which method should the company use to ingest the data with the LEAST operational overhead?

   • AUse Amazon Kinesis Data Firehose and an AWS Lambda function to transform the data and deliver the transformed data to OpenSearch Service.
   • BUse a Logstash pipeline that has prebuilt filters to transform the data and deliver the transformed data to OpenSearch Service.
   • CUse an AWS Lambda function to call the Amazon Kinesis Agent to transform the data and deliver the transformed data OpenSearch Service.
   • DUse the Kinesis Client Library (KCL) to transform the data and deliver the transformed data to OpenSearch Service.

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   Answer: A

   Explanation

   Option A: Use Amazon Kinesis Data Firehose and an AWS Lambda function to transform the data and deliver the transformed data to OpenSearch Service is the best choice for achieving the least operational overhead. Kinesis Data Firehose is a managed service that automates the data ingestion process, scales seamlessly, and integrates directly with OpenSearch Service, minimizing the need for manual intervention and infrastructure management.

   Source: Adapted from community discussion — matt200

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4. Question 34 · 313 in bank

   A company stores customer data tables that include customer addresses in an AWS Lake Formation data lake. To comply with new regulations, the company must ensure that users cannot access data for customers who are in Canada. The company needs a solution that will prevent user access to rows for customers who are in Canada. Which solution will meet this requirement with the LEAST operational effort?

   • ASet a row-level filter to prevent user access to a row where the country is Canada.
   • BCreate an IAM role that restricts user access to an address where the country is Canada.
   • CSet a column-level filter to prevent user access to a row where the country is Canada.
   • DApply a tag to all rows where Canada is the country. Prevent user access where the tag is equal to “Canada”.

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   Answer: A

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5. Question 35 · 313 in bank

   A company stores daily records of the financial performance of investment portfolios in .csv format in an Amazon S3 bucket. A data engineer uses AWS Glue crawlers to crawl the S3 data. The data engineer must make the S3 data accessible daily in the AWS Glue Data Catalog. Which solution will meet these requirements?

   • ACreate an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Create a daily schedule to run the crawler. Configure the output destination to a new path in the existing S3 bucket.
   • BCreate an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Create a daily schedule to run the crawler. Specify a database name for the output.
   • CCreate an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Allocate data processing units (DPUs) to run the crawler every day. Specify a database name for the output.
   • DCreate an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Allocate data processing units (DPUs) to run the crawler every day. Configure the output destination to a new path in the existing S3 bucket.

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   Answer: B

   Explanation

   B. Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Create a daily schedule to run the crawler. Specify a database name for the output. Explanation: Option B correctly sets up the IAM role with the necessary permissions using the AWSGlueServiceRole policy, which is designed for use with AWS Glue. It specifies the S3 bucket path of the source data as the crawler's data store and creates a daily schedule to run the crawler. Additionally, it specifies a database name for the output, ensuring that the crawled data is properly cataloged in the AWS Glue Data Catalog.

   Source: Adapted from community discussion — TonyStark0122

   References

   • AWS documentation

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6. Question 36 · 313 in bank

   A company has implemented a lake house architecture in Amazon Redshift. The company needs to give users the ability to authenticate into Redshift query editor by using a third-party identity provider (IdP). A data engineer must set up the authentication mechanism. What is the first step the data engineer should take to meet this requirement?

   • ARegister the third-party IdP as an identity provider in the configuration settings of the Redshift cluster.
   • BRegister the third-party IdP as an identity provider from within Amazon Redshift.
   • CRegister the third-party IdP as an identity provider for AVS Secrets Manager. Configure Amazon Redshift to use Secrets Manager to manage user credentials.
   • DRegister the third-party IdP as an identity provider for AWS Certificate Manager (ACM). Configure Amazon Redshift to use ACM to manage user credentials.

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   Answer: B

   Explanation

   https://docs.aws.amazon.com/redshift/latest/mgmt/redshift-iam-access-control-native-idp.html register the identity provider with Amazon Redshift, using SQL statements, which set authentication parameters that are unique to the identity provider.

   Source: Adapted from community discussion — PashoQ

   References

   • AWS documentation

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7. Question 37 · 313 in bank

   A company currently uses a provisioned Amazon EMR cluster that includes general purpose Amazon EC2 instances. The EMR cluster uses EMR managed scaling between one to five task nodes for the company’s long-running Apache Spark extract, transform, and load (ETL) job. The company runs the ETL job every day. When the company runs the ETL job, the EMR cluster quickly scales up to five nodes. The EMR cluster often reaches maximum CPU usage, but the memory usage remains under 30%. The company wants to modify the EMR cluster configuration to reduce the EMR costs to run the daily ETL job. Which solution will meet these requirements MOST cost-effectively?

   • AIncrease the maximum number of task nodes for EMR managed scaling to 10.
   • BChange the task node type from general purpose EC2 instances to memory optimized EC2 instances.
   • CSwitch the task node type from general purpose Re instances to compute optimized EC2 instances.
   • DReduce the scaling cooldown period for the provisioned EMR cluster.

   Show answer

   Answer: C

   Explanation

   Since the ETL job reaches maximum CPU usage but not memory usage, switching from general-purpose instances to compute-optimized instances (such as C5 or C6g instances) can provide better performance per dollar for CPU-bound workloads.

   Source: Adapted from community discussion — Shanmahi

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8. Question 38 · 313 in bank

   A company uploads .csv files to an Amazon S3 bucket. The company’s data platform team has set up an AWS Glue crawler to perform data discovery and to create the tables and schemas. An AWS Glue job writes processed data from the tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creates the Amazon Redshift tables in the Redshift database appropriately. If the company reruns the AWS Glue job for any reason, duplicate records are introduced into the Amazon Redshift tables. The company needs a solution that will update the Redshift tables without duplicates. Which solution will meet these requirements?

   • AModify the AWS Glue job to copy the rows into a staging Redshift table. Add SQL commands to update the existing rows with new values from the staging Redshift table.
   • BModify the AWS Glue job to load the previously inserted data into a MySQL database. Perform an upsert operation in the MySQL database. Copy the results to the Amazon Redshift tables.
   • CUse Apache Spark’s DataFrame dropDuplicates() API to eliminate duplicates. Write the data to the Redshift tables.
   • DUse the AWS Glue ResolveChoice built-in transform to select the value of the column from the most recent record.

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   Answer: A

   Explanation

   Two step approach involving creating a staging table, followed by using Redshift's merge statement to update the target table from staging table and finally truncate/housekeep the staging table.

   Source: Adapted from community discussion — Shanmahi

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9. Question 39 · 313 in bank

   A company is using Amazon Redshift to build a data warehouse solution. The company is loading hundreds of files into a fact table that is in a Redshift cluster. The company wants the data warehouse solution to achieve the greatest possible throughput. The solution must use cluster resources optimally when the company loads data into the fact table. Which solution will meet these requirements?

   • AUse multiple COPY commands to load the data into the Redshift cluster.
   • BUse S3DistCp to load multiple files into Hadoop Distributed File System (HDFS). Use an HDFS connector to ingest the data into the Redshift cluster.
   • CUse a number of INSERT statements equal to the number of Redshift cluster nodes. Load the data in parallel into each node.
   • DUse a single COPY command to load the data into the Redshift cluster.

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   Answer: D

   Explanation

   A single COPY command automatically parallelizes the load operation across all nodes in the Redshift cluster. This ensures optimal use of cluster resources.

   Source: Adapted from community discussion — antun3ra

   References

   • AWS documentation

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10. Question 40 · 313 in bank

    A company ingests data from multiple data sources and stores the data in an Amazon S3 bucket. An AWS Glue extract, transform, and load (ETL) job transforms the data and writes the transformed data to an Amazon S3 based data lake. The company uses Amazon Athena to query the data that is in the data lake. The company needs to identify matching records even when the records do not have a common unique identifier. Which solution will meet this requirement?

    • AUse Amazon Macie pattern matching as part of the ETL job.
    • BTrain and use the AWS Glue PySpark Filter class in the ETL job.
    • CPartition tables and use the ETL job to partition the data on a unique identifier.
    • DTrain and use the AWS Lake Formation FindMatches transform in the ETL job.

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    Answer: D

    Explanation

    AWS Lake Formation provides machine learning capabilities to create custom transforms to cleanse your data. There is currently one available transform named FindMatches. The FindMatches transform enables you to identify duplicate or matching records in your dataset, even when the records do not have a common unique identifier and no fields match exactly. This will not require writing any code or knowing how machine learning works.

    Source: Adapted from community discussion — Shanmahi

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DEA-C01 practice, question 31. 313 questions available.