Free Amazon AWS-DEA-C01 Exam Questions

Question 1

A data engineer needs to build an enterprise data catalog based on the company's Amazon S3 buckets andAmazon RDS databases. The data catalog must include storage format metadata for the data in the catalog.Which solution will meet these requirements with the LEAST effort?

A : Use an AWS Glue crawler to scan the S3 buckets and RDS databases and build a data catalog. Use data stewards to inspect the data and update the data catalog with the data format.

B : Use an AWS Glue crawler to build a data catalog. Use AWS Glue crawler classifiers to recognize the format of data and store the format in the catalog.

C : Use Amazon Macie to build a data catalog and to identify sensitive data elements. Collect the data format information from Macie.

D : Use scripts to scan data elements and to assign data classifications based on the format of the data.

Answer: B

Question 2

A data engineer wants to orchestrate a set of extract, transform, and load (ETL) jobs that run on AWS. TheETL jobs contain tasks that must run Apache Spark jobs on Amazon EMR, make API calls to Salesforce, andload data into Amazon Redshift.The ETL jobs need to handle failures and retries automatically. The data engineer needs to use Python toorchestrate the jobs.Which service will meet these requirements?

A : Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

B : AWS Step Functions

C : AWS Glue

D : Amazon EventBridge

Answer: A

Question 3

A company is integrating a business intelligence (BI) tool with their data warehouse hosted on Microsoft SQL Server. The BI team requires regular data extracts to be transformed and stored in Amazon S3 for further analysis. The BI team needs a solution to manage this ETL process efficiently and at a low cost.

Which AWS service or feature is the most cost-effective for orchestrating an ETL pipeline that extracts data from Microsoft SQL Server, transforms it, and loads it into Amazon S3?

A : AWS Batch

B : AWS Glue DataBrew

C : AWS Data Pipeline

D : AWS Glue workflows

Answer: D

Question 4

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

Question 5

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