Amazon DATA-ENGINEER-ASSOCIATE Online Practice Questions and Exam Preparation

Amazon DATA-ENGINEER-ASSOCIATE Online Questions & Answers

• Question 401:

  A company stores historical customer data in an Amazon Redshift table. A column named Email contains null entries and values that are not email addresses. The quality of the Email column is critical for multiple downstream processes. A data engineer must create an AWS Glue Data Quality rule that fails when the percentage of valid email addresses in the Email column is less than 90%.

  Which component of an AWS Glue Data Quality rule will meet these requirements?

  A. Uniqueness "Email" matches "[%@%.%]" with a threshold set to > 0.9
  B. ColumnValues "Email" matches "[%@%.%]" with a threshold set to > 0.1
  C. ColumnValues "Email" matches "[%@%.%]" with a threshold set to > 0.9
  D. UniqueValueRatio "Email" matches "[%@%.%]" with a threshold set to > 0.1
  C. ColumnValues "Email" matches "[%@%.%]" with a threshold set to > 0.9

• Question 402:

  A company has several new datasets in CSV and JSON formats. A data engineer needs to make the data available to a team of data analysts who will analyze the data by using SQL queries.

  Which solution will meet these requirements in the MOST cost-effective way?

  A. Create an Amazon RDS MySQL cluster. Use AWS Glue to transform and load the CSV and JSON files into database tables. Provide the data analysts acce to the MySQL cluster.
  B. Create an AWS Glue DataBrew project that contains the new data. Make the DataBrew project available to the data analysts.
  C. Store the data in an Amazon S3 bucket. Use an AWS Glue crawler to catalog the S3 bucket as tables. Create an Amazon Athena workgroup that has a data usage threshold. Grant the data analysts acce to the Athena workgroup.
  D. Load the data into Super-fast, Parallel, In-memory Calculation Engine (SPICE) in Amazon QuickSight. Allow the data analysts to create analyses and dashboards in QuickSight.
  C. Store the data in an Amazon S3 bucket. Use an AWS Glue crawler to catalog the S3 bucket as tables. Create an Amazon Athena workgroup that has a data usage threshold. Grant the data analysts acce to the Athena workgroup.

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  Explanation

  By storing the CSV and JSON files in Amazon S3 and running an AWS Glue crawler, you automatically catalog them as tables without upfront ETL. Amazon Athena then lets analysts run SQL queries directly against those tables on a pay-per-query basis. Using an Athena workgroup with a usage threshold keeps costs under control, making this the most cost-effective, low-operational-overhead solution.

• Question 403:

  Two data engineering teams use separate AWS accounts. Both teams request access to the same datashare in an Amazon Redshift cluster that is in a third AWS account. The datashare is named salesshare.

  A data engineer must use the Amazon Redshift SQL interface to grant both data engineering teams' access to the datashare.

  Which command or commands will meet this requirement?

  A. GRANT USAGE ON DATASHARE salesshare TO ACCOUNTS '<account ID 1>' AND '<account ID 2>';
  B. GRANT USAGE ON DATASHARE salesshare TO NAMESPACES '<account ID 1>' AND '<account ID 2>';
  C. GRANT USAGE ON DATASHARE salesshare TO ACCOUNT '<account ID 1>'; GRANT USAGE ON DATASHARE salesshare TO ACCOUNT '<account ID 2>';
  D. GRANT USAGE ON DATASHARE salesshare TO NAMESPACE '<account ID 1>'; GRANT USAGE ON DATASHARE salesshare TO NAMESPACE '<account ID 2>';
  D. GRANT USAGE ON DATASHARE salesshare TO NAMESPACE '<account ID 1>'; GRANT USAGE ON DATASHARE salesshare TO NAMESPACE '<account ID 2>';