DEA-C01 practice questions 11–20

1. Question 11 · 313 in bank

   A data engineer needs to debug an AWS Glue job that reads from Amazon S3 and writes to Amazon Redshift. The data engineer enabled the bookmark feature for the AWS Glue job. The data engineer has set the maximum concurrency for the AWS Glue job to 1. The AWS Glue job is successfully writing the output to Amazon Redshift. However, the Amazon S3 files that were loaded during previous runs of the AWS Glue job are being reprocessed by subsequent runs. What is the likely reason the AWS Glue job is reprocessing the files?

   • AThe AWS Glue job does not have the s3:GetObjectAcl permission that is required for bookmarks to work correctly.
   • BThe maximum concurrency for the AWS Glue job is set to 1.
   • CThe data engineer incorrectly specified an older version of AWS Glue for the Glue job.
   • DThe AWS Glue job does not have a required commit statement.

   Show answer

   Answer: D

   Explanation

   Ensure that your job run script ends with the following commit: job.commit() When you include this object, AWS Glue records the timestamp and path of the job run. If you run the job again with the same path, AWS Glue processes only the new files. If you don't include this object and job bookmarks are enabled, the job reprocesses the already processed files along with the new files and creates redundancy in the job's target data store. https://docs.aws.amazon.com/glue/latest/dg/glue-troubleshooting-errors.html#error-job-bookmarks-reprocess-data

   Source: Adapted from community discussion — proserv

   References

   • AWS documentation

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2. Question 12 · 313 in bank

   An ecommerce company wants to use AWS to migrate data pipelines from an on-premises environment into the AWS Cloud. The company currently uses a third-party tool in the on-premises environment to orchestrate data ingestion processes. The company wants a migration solution that does not require the company to manage servers. The solution must be able to orchestrate Python and Bash scripts. The solution must not require the company to refactor any code. Which solution will meet these requirements with the LEAST operational overhead?

   • AAWS Lambda
   • BAmazon Managed Workflows for Apache Airflow (Amazon MVVAA)
   • CAWS Step Functions
   • DAWS Glue

   Show answer

   Answer: B

   Explanation

   "All of the components contained in the outer box (in the image below) appear as a single Amazon MWAA environment in your account. The Apache Airflow Scheduler and Workers are AWS Fargate (Fargate) containers that connect to the private subnets in the Amazon VPC for your environment. Each environment has its own Apache Airflow metadatabase managed by AWS that is accessible to the Scheduler and Workers Fargate containers via a privately-secured VPC endpoint." https://docs.aws.amazon.com/mwaa/latest/userguide/what-is-mwaa.html So MWAA is hosted on ECS Fargate - serverless. Supports Bash and Python. B is correct.

   Source: Adapted from community discussion — didorins

   References

   • AWS documentation

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3. Question 13 · 313 in bank

   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

   Answer: C

   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.

   Source: Adapted from community discussion — TonyStark0122

   References

   • AWS documentation
   • AWS documentation

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4. Question 14 · 313 in bank

   A retail company stores data from a product lifecycle management (PLM) application in an on-premises MySQL database. The PLM application frequently updates the database when transactions occur. The company wants to gather insights from the PLM application in near real time. The company wants to integrate the insights with other business datasets and to analyze the combined dataset by using an Amazon Redshift data warehouse. The company has already established an AWS Direct Connect connection between the on-premises infrastructure and AWS. Which solution will meet these requirements with the LEAST development effort?

   • ARun a scheduled AWS Glue extract, transform, and load (ETL) job to get the MySQL database updates by using a Java Database Connectivity (JDBC) connection. Set Amazon Redshift as the destination for the ETL job.
   • BRun a full load plus CDC task in AWS Database Migration Service (AWS DMS) to continuously replicate the MySQL database changes. Set Amazon Redshift as the destination for the task.
   • CUse the Amazon AppFlow SDK to build a custom connector for the MySQL database to continuously replicate the database changes. Set Amazon Redshift as the destination for the connector.
   • DRun scheduled AWS DataSync tasks to synchronize data from the MySQL database. Set Amazon Redshift as the destination for the tasks.

   Show answer

   Answer: B

   References

   • AWS documentation

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5. Question 15 · 313 in bank

   A marketing company uses Amazon S3 to store clickstream data. The company queries the data at the end of each day by using a SQL JOIN clause on S3 objects that are stored in separate buckets. The company creates key performance indicators (KPIs) based on the objects. The company needs a serverless solution that will give users the ability to query data by partitioning the data. The solution must maintain the atomicity, consistency, isolation, and durability (ACID) properties of the data. Which solution will meet these requirements MOST cost-effectively?

   • AAmazon S3 Select
   • BAmazon Redshift Spectrum
   • CAmazon Athena
   • DAmazon EMR

   Show answer

   Answer: C

   Explanation

   C. Amazon Athena Here's why Amazon Athena is suitable: Serverless: Amazon Athena is a serverless query service that allows you to run SQL queries directly on data stored in Amazon S3 without the need to manage infrastructure. Partitioning: Athena supports querying data by partitioning, which can significantly improve query performance by limiting the amount of data scanned. ACID Properties: Although Amazon S3 itself does not provide ACID properties, Amazon Athena ensures consistency in query results and durability of the data stored in S3 through its managed query execution. Cost-effective: With Amazon Athena, you only pay for the queries you run and the amount of data scanned, making it a cost-effective choice compared to managing infrastructure or using dedicated services like Amazon Redshift Spectrum or Amazon EMR.

   Source: Adapted from community discussion — Ja13

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6. Question 16 · 313 in bank

   A company wants to migrate data from an Amazon RDS for PostgreSQL DB instance in the eu-east-1 Region of an AWS account named Account_A. The company will migrate the data to an Amazon Redshift cluster in the eu-west-1 Region of an AWS account named Account_B. Which solution will give AWS Database Migration Service (AWS DMS) the ability to replicate data between two data stores?

   • ASet up an AWS DMS replication instance in Account_B in eu-west-1.
   • BSet up an AWS DMS replication instance in Account_B in eu-east-1.
   • CSet up an AWS DMS replication instance in a new AWS account in eu-west-1.
   • DSet up an AWS DMS replication instance in Account_A in eu-east-1.

   Show answer

   Answer: A

   References

   • AWS documentation
   • AWS documentation

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7. Question 17 · 313 in bank

   A company uses Amazon S3 as a data lake. The company sets up a data warehouse by using a multi-node Amazon Redshift cluster. The company organizes the data files in the data lake based on the data source of each data file. The company loads all the data files into one table in the Redshift cluster by using a separate COPY command for each data file location. This approach takes a long time to load all the data files into the table. The company must increase the speed of the data ingestion. The company does not want to increase the cost of the process. Which solution will meet these requirements?

   • AUse a provisioned Amazon EMR cluster to copy all the data files into one folder. Use a COPY command to load the data into Amazon Redshift.
   • BLoad all the data files in parallel into Amazon Aurora. Run an AWS Glue job to load the data into Amazon Redshift.
   • CUse an AWS Give job to copy all the data files into one folder. Use a COPY command to load the data into Amazon Redshift.
   • DCreate a manifest file that contains the data file locations. Use a COPY command to load the data into Amazon Redshift.

   Show answer

   Answer: D

   References

   • AWS documentation
   • AWS documentation

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8. Question 18 · 313 in bank

   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

   Answer: D

   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.

   Source: Adapted from community discussion — mzansikiller

   References

   • AWS documentation
   • AWS documentation

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9. Question 19 · 313 in bank

   A company is using an AWS Transfer Family server to migrate data from an on-premises environment to AWS. Company policy mandates the use of TLS 1.2 or above to encrypt the data in transit. Which solution will meet these requirements?

   • AGenerate new SSH keys for the Transfer Family server. Make the old keys and the new keys available for use.
   • BUpdate the security group rules for the on-premises network to allow only connections that use TLS 1.2 or above.
   • CUpdate the security policy of the Transfer Family server to specify a minimum protocol version of TLS 1.2
   • DInstall an SSL certificate on the Transfer Family server to encrypt data transfers by using TLS 1.2.

   Show answer

   Answer: C

   References

   • AWS documentation

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10. Question 20 · 313 in bank

    A company wants to migrate an application and an on-premises Apache Kafka server to AWS. The application processes incremental updates that an on-premises Oracle database sends to the Kafka server. The company wants to use the replatform migration strategy instead of the refactor strategy. Which solution will meet these requirements with the LEAST management overhead?

    • AAmazon Kinesis Data Streams
    • BAmazon Managed Streaming for Apache Kafka (Amazon MSK) provisioned cluster
    • CAmazon Kinesis Data Firehose
    • DAmazon Managed Streaming for Apache Kafka (Amazon MSK) Serverless

    Show answer

    Answer: D

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DEA-C01 practice, question 11. 313 questions available.