Amazon Web Services Data-Engineer-Associate Exam With Confidence Using Practice Dumps

The correct answer is B because AWS Glue streaming ETL jobs are built to continuously consume data from streaming sources such as Amazon Kinesis Data Streams, transform the data, and load the results into Amazon S3 data lakes. The requirement is not just delivery to S3; the telemetry JSON must be flattened before storage, and this needs to happen with low latency. Amazon Data Firehose can deliver streaming data, but the option incorrectly assumes automatic flattening without a transformation layer. SQS plus scheduled batch Glue introduces batch delay and does not meet the lowest-latency requirement. Athena is a query engine, not a real-time transformation and ingestion service for writing flattened streaming output to S3. AWS Glue streaming ETL is the correct serverless streaming transformation layer.

Amazon Kinesis Data Firehose is a fully managed data ingestion service that enables reliable and scalable delivery of streaming and batch data into Amazon S3 with minimal operational overhead. This directly satisfies the requirement for a fully managed AWS ingestion service while avoiding the need to provision, scale, or manage infrastructure.

By using the Amazon Kinesis Agent on the on-premises servers, the company can automatically forward daily data files to Kinesis Data Firehose. Firehose handles buffering, retry logic, scaling, and delivery without requiring administrative effort. Delivering the data to Amazon S3 allows seamless integration with Amazon Athena, which natively queries data stored in S3 without requiring data movement or transformation.

Enabling Amazon S3 versioning protects files from accidental deletion by preserving previous versions of objects. This aligns with AWS best practices for data durability and governance, especially for analytics workloads and compliance requirements.

Other options introduce unnecessary operational complexity. AWS DataSync with Amazon EFS is not optimized for Athena-based analytics. AWS Glue jobs and Amazon RDS are unsuitable for file-based analytical access. A self-managed Apache Kafka solution with Amazon MSK significantly increases operational overhead.

Therefore, option B is the most efficient, scalable, and operationally optimal solution according to AWS Certified Data Engineer – Associate best practices.

Problem Analysis:

The company needs near real-time replication of MySQL updates to Amazon Redshift.

Minimal development effort is required for this solution.

Key Considerations:

AWS DMS provides a full load + CDC (Change Data Capture) mode for continuous replication of database changes.

DMS integrates natively with both MySQL and Redshift, simplifying setup.

Solution Analysis:

Option A: AWS Glue Job

Glue is batch-oriented and does not support near real-time replication.

Option B: DMS with Full Load + CDC

Efficiently handles initial database load and continuous updates.

Requires minimal setup and operational overhead.

Option C: AppFlow SDK

AppFlow is not designed for database replication. Custom connectors increase development effort.

Option D: DataSync

DataSync is for file synchronization and not suitable for database updates.

Final Recommendation:

Use AWS DMS in full load + CDC mode for continuous replication.

AWS Database Migration Service Documentation

Setting Up DMS with Redshift