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

Problem Analysis:

The company needs to migrate both an application and an on-premises Apache Kafka server to AWS.

Incremental updates from an on-premises Oracle database are processed by Kafka.

The solution must follow a replatform migration strategy, prioritizing minimal changes and low management overhead.

Key Considerations:

Replatform Strategy: This approach keeps the application and architecture as close to the original as possible, reducing the need for refactoring.

The solution must provide a managed Kafka service to minimize operational burden.

Low overhead solutions like serverless services are preferred.

Solution Analysis:

Option A: Kinesis Data Streams

Kinesis Data Streams is an AWS-native streaming service but is not a direct substitute for Kafka.

This option would require significant application refactoring, which does not align with the replatform strategy.

Option B: MSK Provisioned Cluster

Managed Kafka service with fully configurable clusters.

Provides the same Kafka APIs but requires cluster management (e.g., scaling, patching), increasing management overhead.

Option C: Amazon Kinesis Data Firehose

Kinesis Data Firehose is designed for data delivery rather than real-time streaming and processing.

Not suitable for Kafka-based applications.

Option D: MSK Serverless

MSK Serverless eliminates the need for cluster management while maintaining compatibility with Kafka APIs.

Automatically scales based on workload, reducing operational overhead.

Ideal for replatform migrations, as it requires minimal changes to the application.

Final Recommendation:

Amazon MSK Serverless is the best solution for migrating the Kafka server and application with minimal changes and the least management overhead.

Amazon MSK Serverless Overview

Comparison of Amazon MSK and Kinesis

AWS Glue DataBrew provides a no-code way to define and run data quality rulesets for data stored in S3. You can trigger profiling jobs via Amazon EventBridge on new uploads for automated checks.

“Use AWS Glue DataBrew to define and run data quality rules on S3 datasets with minimal coding effort. Automate validation by triggering jobs through EventBridge.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

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.