Free Amazon AWS-DEA-C01 Exam Questions

Question 1

A company has three subsidiaries. Each subsidiary uses a different data warehousing solution. The firstsubsidiary hosts its data warehouse in Amazon Redshift. The second subsidiary uses Teradata Vantage onAWS. 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 useApache Iceberg as the table format.A data engineer needs to build a new pipeline to connect to all the data sources, run transformations by usingeach source engine, join the data, and write the data to Iceberg.Which solution will meet these requirements with the LEAST operational effort?

A : 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.

B : 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

C : 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.

D : 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.

Answer: B

Question 2

A Data Engineering team is planning to migrate a complex legacy Microsoft SQL Server database to Amazon Aurora MySQL. The existing SQL Server database contains several stored procedures, triggers, and complex joins. The team is concerned about potential compatibility issues and the effort needed to rewrite substantial amounts of SQL code manually. They aim to use AWS services to facilitate an efficient and accurate schema conversion, ensuring minimal downtime and data consistency during the migration process.

What sequence of steps should the team follow using AWS services to achieve this migration?

A : Use AWS Database Migration Service (AWS DMS) for an initial assessment of the schema conversion, apply the AWS Schema Conversion Tool (AWS SCT) for the actual schema conversion, then use AWS DMS again to migrate the data to Amazon Aurora MySQL.

B : First, apply the AWS Schema Conversion Tool (AWS SCT) to assess and convert the SQL Server schema and SQL code to a format compatible with Aurora MySQL. Following this, use AWS DMS to migrate the data from SQL Server to Aurora MySQL.

C : Directly utilize AWS DMS to migrate the SQL Server database to Amazon Aurora MySQL, relying on the DMS’s built-in schema conversion feature to handle any compatibility issues during the migration.

D : Start with the AWS Schema Conversion Tool (AWS SCT) for converting the SQL Server schema and code to Aurora MySQL, utilize Amazon RDS to temporarily host the converted schema, and then use AWS DMS for the final migration to Aurora MySQL.

Answer: B

Question 3

A company wants to migrate a data warehouse from Teradata to Amazon Redshift. Which solution will meet this requirement with the LEAST operational effort?

A : Use AWS Database Migration Service (AWS DMS) Schema Conversion to migrate the schema. Use AWS DMS to migrate the data.

B : Use the AWS Schema Conversion Tool (AWS SCT) to migrate the schema. Use AWS Database Migration Service (AWS DMS) to migrate the data.

C : Use AWS Database Migration Service (AWS DMS) to migrate the data. Use automatic schema conversion.

D : Manually export the schema definition from Teradata. Apply the schema to the Amazon Redshift database. Use AWS Database Migration Service (AWS DMS) to migrate the data.

Answer: B

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

Question 5

A data engineer has two datasets that contain sales information for multiple cities and states. One dataset is named reference, and the other dataset is named primary. The data engineer needs a solution to determine whether a specific set of values in the city and state columns of the primary dataset exactly match the same specific values in the reference dataset. The data engineer wants to use Data Quality Definition Language (DQDL) rules in an AWS Glue Data Quality job. Which rule will meet these requirements?

A : DatasetMatch "reference" "city->ref_city, state->ref_state" = 1.0

B : ReferentialIntegrity "city,state" "reference.{ref_city,ref_state}" = 1.0

C : DatasetMatch "reference" "city->ref_city, state->ref_state" = 100

D : ReferentialIntegrity "city,state" "reference.{ref_city,ref_state}" = 100

Answer: A