AWS Certified Data Engineer – Associate (DEA-C01) — Question 259

A company is building a data lake for a new analytics team. The company is using Amazon S3 for storage and Amazon Athena for query analysis. All data that is in Amazon S3 is in Apache Parquet format.

The company is running a new Oracle database as a source system in the company’s data center. The company has 70 tables in the Oracle database. All the tables have primary keys. Data can occasionally change in the source system. The company wants to ingest the tables every day into the data lake.

Which solution will meet this requirement with the LEAST effort?

Answer options

• A. Create an Apache Sqoop job in Amazon EMR to read the data from the Oracle database. Configure the Sqoop job to write the data to Amazon S3 in Parquet format.
• B. Create an AWS Glue connection to the Oracle database. Create an AWS Glue bookmark job to ingest the data incrementally and to write the data to Amazon S3 in Parquet format.
• C. Create an AWS Database Migration Service (AWS DMS) task for ongoing replication. Set the Oracle database as the source. Set Amazon S3 as the target. Configure the task to write the data in Parquet format.
• D. Create an Oracle database in Amazon RDS. Use AWS Database Migration Service (AWS DMS) to migrate the on-premises Oracle database to Amazon RDS. Configure triggers on the tables to invoke AWS Lambda functions to write changed records to Amazon S3 in Parquet format.

Correct answer: C

Explanation

The correct answer is C because using AWS Database Migration Service (AWS DMS) allows for ongoing replication with minimal effort, directly writing to Amazon S3 in the required Parquet format. Option A involves more manual configuration with Apache Sqoop, while option B requires additional setup with AWS Glue, and option D adds unnecessary complexity by migrating to RDS and using triggers.