Amazon Data-Engineer-Associate Exam Dumps

AWS Certified Data Engineer - Associate (DEA-C01)

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Amazon Data-Engineer-Associate Sample Questions

Question # 1

A data engineer needs to securely transfer 5 TB of data from an on-premises data center to an Amazon S3 bucket. Approximately 5% of the data changes every day. Updates to the data need to be regularlyproliferated to the S3 bucket. The data includes files that are in multiple formats. The data engineer needs to automate the transfer process and must schedule the process to run periodically.Which AWS service should the data engineer use to transfer the data in the MOST operationally efficient way?

A. AWS DataSync
B. AWS Glue
C. AWS Direct Connect
D. Amazon S3 Transfer Acceleration

Answer: A

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Question # 2

A company uses Amazon RDS for MySQL as the database for a critical application. The database workload is mostly writes, with a small number of reads.A data engineer notices that the CPU utilization of the DB instance is very high. The high CPU utilization is slowing down the application. The data engineer must reduce the CPU utilization of the DB Instance.Which actions should the data engineer take to meet this requirement? (Choose two.)

A. Use the Performance Insights feature of Amazon RDS to identify queries that have high CPU utilization. Optimize the problematic queries.
B. Modify the database schema to include additional tables and indexes.
C. Reboot the RDS DB instance once each week.
D. Upgrade to a larger instance size.
E. Implement caching to reduce the database query load.

Answer: A,E

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Question # 3

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.)

A. Partition the data that is in the S3 bucket. Organize the data by year, month, and day. atures.
B. Increase the AWS Glue instance size by scaling up the worker type.
C. Convert the AWS Glue schema to the DynamicFrame schema class.
D. Adjust AWS Glue job scheduling frequency so the jobs run half as many times each day.

Answer: A,B

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Question # 4

A data engineer needs to use AWS Step Functions to design an orchestration workflow. The workflow must parallel process a large collection of data files and apply a specific transformation to each file.Which Step Functions state should the data engineer use to meet these requirements?

A. Parallel state
B. Choice state
C. Map state
D. Wait state

Answer: C

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Question # 5

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.)

A. There is no connection between QuickSgqht and Athena.
B. The Athena tables are not cataloged.
C. QuickSiqht does not have access to the S3 bucket.
D. QuickSight does not have access to decrypt S3 data.
E. There is no 1AM role assigned to QuickSiqht.

Answer: C,D

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Question # 6

A data engineer is building a data pipeline on AWS by using AWS Glue extract, transform, and load (ETL) jobs. The data engineer needs to process data from Amazon RDS and MongoDB, perform transformations, and load the transformed data into Amazon Redshift for analytics. The data updates must occur every hour.Which combination of tasks will meet these requirements with the LEAST operational overhead? (Choose two.)

A. Configure AWS Glue triggers to run the ETL jobs even/ hour.
B. Use AWS Glue DataBrewto clean and prepare the data for analytics.
C. Use AWS Lambda functions to schedule and run the ETL jobs even/ hour.
D. Use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift.
E. Use the Redshift Data API to load transformed data into Amazon Redshift.

Answer: .

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Question # 7

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?

A. Use an Amazon EMR provisioned cluster to read from all sources. Use Apache Spark to join the data and perform the analysis.
B. Copy the data from DynamoDB, Amazon RDS, and Amazon Redshift into Amazon S3. Run Amazon Athena queries directly on the S3 files.
C. Use Amazon Athena Federated Query to join the data from all data sources.
D. Use Redshift Spectrum to query data from DynamoDB, Amazon RDS, and Amazon S3 directly from Redshift.

Answer: C

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Question # 8

A company currently stores all of its data in Amazon S3 by using the S3 Standard storage class.A data engineer examined data access patterns to identify trends. During the first 6 months, most data files are accessed several times each day. Between 6 months and 2 years, most data files are accessed once or twice each month. After 2 years, data files are accessed only once or twice each year.The data engineer needs to use an S3 Lifecycle policy to develop new data storage rules. The new storage solution must continue to provide high availability.Which solution will meet these requirements in the MOST cost-effective way?

A. Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.
B. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.
C. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.
D. Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.

Answer: C

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Question # 9

A company stores datasets in JSON format and .csv format in an Amazon S3 bucket. The company has Amazon RDS for Microsoft SQL Server databases, Amazon DynamoDB tables that are in provisionedcapacity mode, and an Amazon Redshift cluster. A data engineering team must develop a solution that will give data scientists the ability to query all data sources by using syntax similar to SQL.Which solution will meet these requirements with the LEAST operational overhead?

A. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Amazon Athena to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.
B. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Redshift Spectrum to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.
C. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use AWS Glue jobs to transform data that is in JSON format to Apache Parquet or .csv format. Store the transformed data in an S3 bucket. Use Amazon Athena to query the original and transformed data from the S3 bucket.
D. Use AWS Lake Formation to create a data lake. Use Lake Formation jobs to transform the data from all data sources to Apache Parquet format. Store the transformed data in an S3 bucket. Use Amazon Athena or Redshift Spectrum to query the data.

Answer: A

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Question # 10

A data engineer runs Amazon Athena queries on data that is in an Amazon S3 bucket. The Athena queries use AWS Glue Data Catalog as a metadata table.The data engineer notices that the Athena query plans are experiencing a performance bottleneck. The data engineer determines that the cause of the performance bottleneck is the large number of partitions that are in the S3 bucket. The data engineer must resolve the performance bottleneck and reduce Athena query planning time.Which solutions will meet these requirements? (Choose two.)

A. Create an AWS Glue partition index. Enable partition filtering.
B. Bucketthe data based on a column thatthe data have in common in a WHERE clause of the user query
C. Use Athena partition projection based on the S3 bucket prefix.
D. Transform the data that is in the S3 bucket to Apache Parquet format.
E. Use the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects.

Answer: A,C

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Question # 11

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?

A. Update the AWS Glue security group to allow inbound traffic from the Amazon S3 VPC gateway endpoint.
B. Configure an S3 bucket policy to explicitly grant the AWS Glue job permissions to access the S3 bucket.
C. Review the AWS Glue job code to ensure that the AWS Glue connection details include a fully qualified domain name.
D. Verify that the VPC's route table includes inbound and outbound routes for the Amazon S3 VPC gateway endpoint.

Answer: D

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Question # 12

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?

A. Create 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.
B. Create 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.
C. Create and run an Apache Spark job in Amazon EMR Serverless to calculate the number of distinct customers.
D. Use AWS Glue DataBrew to create a recipe that uses the COUNT_DISTINCT aggregate function to calculate the number of distinct customers.

Answer: D

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Question # 13

A media company uses software as a service (SaaS) applications to gather data by using third-party tools. The company needs to store the data in an Amazon S3 bucket. The company will use Amazon Redshift to perform analytics based on the data.Which AWS service or feature will meet these requirements with the LEAST operational overhead?

A. Amazon Managed Streaming for Apache Kafka (Amazon MSK)
B. Amazon AppFlow
C. AWS Glue Data Catalog
D. Amazon Kinesis

Answer: B

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Question # 14

A data engineer must build an extract, transform, and load (ETL) pipeline to process and load data from 10 source systems into 10 tables that are in an Amazon Redshift database. All the source systems generate .csv, JSON, or Apache Parquet files every 15 minutes. The source systems all deliver files into one Amazon S3 bucket. The file sizes range from 10 MB to 20 GB. The ETL pipeline must function correctly despite changes to the data schema.Which data pipeline solutions will meet these requirements? (Choose two.)

A. Use an Amazon EventBridge rule to run an AWS Glue job every 15 minutes. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
B. Use an Amazon EventBridge rule to invoke an AWS Glue workflow job every 15 minutes. Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
C. Configure an AWS Lambda function to invoke an AWS Glue crawler when a file is loaded into the S3 bucket. Configure an AWS Glue job to process and load the data into the Amazon Redshift tables. Create a second Lambda function to run the AWS Glue job. Create an Amazon EventBridge rule to invoke the second Lambda function when the AWS Glue crawler finishes running successfully.
D. Configure an AWS Lambda function to invoke an AWS Glue workflow when a file is loaded into the S3 bucket. Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
E. Configure an AWS Lambda function to invoke an AWS Glue job when a file is loaded into the S3 bucket. Configure the AWS Glue job to read the files from the S3 bucket into an Apache Spark DataFrame. Configure the AWS Glue job to also put smaller partitions of the DataFrame into an Amazon Kinesis Data Firehose delivery stream. Configure the delivery stream to load data into the Amazon Redshift tables.

Answer: A,B

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