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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 10–12 of 32 questions

Question 10 (Topic 1)

A data engineer needs to create a new empty table in Amazon Athena that has the same schema as an existing table named old_table. Which SQL statement should the data engineer use to meet this requirement?

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  • ○ CREATE TABLE new_table AS SELECT * FROM old_tables;
  • ○ INSERT INTO new_table SELECT * FROM old_table;
  • ○ CREATE TABLE new_table (LIKE old_table);
  • ○ CREATE TABLE new_table AS (SELECT * FROM old_table) WITH NO DATA;
Question 11 (Topic 1)

A company has a data lake in Amazon S3. The company uses AWS Glue to catalog data and AWS Glue Studio to implement data extract, transform, and load (ETL) pipelines. The company needs to ensure that data quality issues are checked every time the pipelines run. A data engineer must enhance the existing pipelines to evaluate data quality rules based on predefined thresholds. Which solution will meet these requirements with the LEAST implementation effort?

Select an option, then click Submit answer.

  • ○ Add a new transform that is defined by a SQL query to each Glue ETL job. Use the SQL query to implement a ruleset that includes the data quality rules that need to be evaluated.
  • ○ Add a new Evaluate Data Quality transform to each Glue ETL job. Use Data Quality Definition Language (DQDL) to implement a ruleset that includes the data quality rules that need to be evaluated.
  • ○ Add a new custom transform to each Glue ETL job. Use the PyDeequ library to implement a ruleset that includes the data quality rules that need to be evaluated.
  • ○ Add a new custom transform to each Glue ETL job. Use the Great Expectations library to implement a ruleset that includes the data quality rules that need to be evaluated.
Question 12 (Topic 1)

A company has three subsidiaries. Each subsidiary uses a different data warehousing solution. The first subsidiary hosts its data warehouse in Amazon Redshift. The second subsidiary uses Teradata Vantage on AWS. The third subsidiary uses Google BigQuery. The company wants to aggregate all the data into a central Amazon S3 data lake. The company wants to use Apache Iceberg as the table format. A data engineer needs to build a new pipeline to connect to all the data sources, run transformations by using each source engine, join the data, and write the data to Iceberg. Which solution will meet these requirements with the LEAST operational effort?

Select an option, then click Submit answer.

  • ○ Use native Amazon Redshift, Teradata, and BigQuery connectors to build the pipeline in AWS Glue. Use native AWS Glue transforms to join the data. Run a Merge operation on the data lake Iceberg table.
  • ○ Use the Amazon Athena federated query connectors for Amazon Redshift, Teradata, and BigQuery to build the pipeline in Athena. Write a SQL query to read from all the data sources, join the data, and run a Merge operation on the data lake Iceberg table.
  • ○ Use the native Amazon Redshift connector, the Java Database Connectivity (JDBC) connector for Teradata, and the open source Apache Spark BigQuery connector to build the pipeline in Amazon EMR. Write code in PySpark to join the data. Run a Merge operation on the data lake Iceberg table.
  • ○ Use the native Amazon Redshift, Teradata, and BigQuery connectors in Amazon Appflow to write data to Amazon S3 and AWS Glue Data Catalog. Use Amazon Athena to join the data. Run a Merge operation on the data lake Iceberg table.