AWS Certified Data Engineer - Associate DEA-C01 Practice Test

AWS Certified Data Engineer - Associate DEA-C01 Practice Test practice questions, page 10 of 15, with answer review and explanations.

Question 46

A company needs to partition the Amazon S3 storage that the company uses for a data lake. The partitioning will use a path of the S3 object keys in the following format: s3://bucket/prefix/year=2023/month=01/day=01. A data engineer must ensure that the AWS Glue Data Catalog synchronizes with the S3 storage when the company adds new partitions to the bucket. Which solution will meet these requirements with the LEAST latency?

  1. Schedule an AWS Glue crawler to run every morning.
  2. Manually run the AWS Glue CreatePartition API twice each day.
  3. Use code that writes data to Amazon S3 to invoke the Boto3 AWS Glue create_partition API call.
  4. Run the MSCK REPAIR TABLE command from the AWS Glue console.

Question 47

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?

  1. Amazon Managed Streaming for Apache Kafka (Amazon MSK)
  2. Amazon AppFlow
  3. AWS Glue Data Catalog
  4. Amazon Kinesis

Question 48

A data engineer is using Amazon Athena to analyze sales data that is in Amazon S3. The data engineer writes a query to retrieve sales amounts for 2023 for several products from a table named sales_data. However, the query does not return results for all of the products that are in the sales_data table. The data engineer needs to troubleshoot the query to resolve the issue. The data engineer's original query is as follows: SELECT product_name, sum(sales_amount) FROM sales_data - WHERE year = 2023 - GROUP BY product_name - How should the data engineer modify the Athena query to meet these requirements?

  1. Replace sum(sales_amount) with count(*) for the aggregation.
  2. Change WHERE year = 2023 to WHERE extract(year FROM sales_data) = 2023.
  3. Add HAVING sum(sales_amount) > 0 after the GROUP BY clause.
  4. Remove the GROUP BY clause.

Question 49

A data engineer has a one-time task to read data from objects that are in Apache Parquet format in an Amazon S3 bucket. The data engineer needs to query only one column of the data. Which solution will meet these requirements with the LEAST operational overhead?

  1. Configure an AWS Lambda function to load data from the S3 bucket into a pandas dataframe. Write a SQL SELECT statement on the dataframe to query the required column.
  2. Use S3 Select to write a SQL SELECT statement to retrieve the required column from the S3 objects.
  3. Prepare an AWS Glue DataBrew project to consume the S3 objects and to query the required column.
  4. Run an AWS Glue crawler on the S3 objects. Use a SQL SELECT statement in Amazon Athena to query the required column.

Question 50

A company uses Amazon Redshift for its data warehouse. The company must automate refresh schedules for Amazon Redshift materialized views. Which solution will meet this requirement with the LEAST effort?

  1. Use Apache Airflow to refresh the materialized views.
  2. Use an AWS Lambda user-defined function (UDF) within Amazon Redshift to refresh the materialized views.
  3. Use the query editor v2 in Amazon Redshift to refresh the materialized views.
  4. Use an AWS Glue workflow to refresh the materialized views.