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Free AWS-CERTIFIED-DATA-ENGINEER-ASSOCIATE-DEA-C01 – Amazon AWS-CERTIFIED-DATA-ENGINEER-ASSOCIATE-DEA-C01 Practice Test Question

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Showing 1–3 of 32 questions

Question 1 (Topic 1)

A data engineer must use AWS services to ingest a dataset into an Amazon S3 data lake. The data engineer profiles the dataset and discovers that the dataset contains personally identifiable information (PII). The data engineer must implement a solution to profile the dataset and obfuscate the PII. Which solution will meet this requirement with the LEAST operational effort?

Select an option, then click Submit answer.

  • ○ Use an Amazon Kinesis Data Firehose delivery stream to process the dataset. Create an AWS Lambda transform function to identify the PII. Use an AWS SDK to obfuscate the PII. Set the S3 data lake as the target for the delivery stream.
  • ○ Use the Detect PII transform in AWS Glue Studio to identify the PII. Obfuscate the PII. Use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake.
  • ○ Use the Detect PII transform in AWS Glue Studio to identify the PII. Create a rule in AWS Glue Data Quality to obfuscate the PII. Use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake.
  • ○ Ingest the dataset into Amazon DynamoDB. Create an AWS Lambda function to identify and obfuscate the PII in the DynamoDB table and to transform the data. Use the same Lambda function to ingest the data into the S3 data lake.
Question 2 (Topic 1)

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?

Select an option, then click Submit answer.

  • ○ Use 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.
  • ○ Access 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.
  • ○ Create 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.
  • ○ Connect 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.
Question 3 (Topic 1)

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.)

Select an option, then click Submit answer.

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