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

For high-performance analytics in Athena and EMR Spark, the columnar storage formats Parquet and ORC deliver the shortest query times. These formats minimize I/O, enable predicate pushdown, and support efficient compression and parallel reads.

When integrated with the AWS Glue Data Catalog, schema evolution is automatically tracked and managed across queries.

This combination (Parquet/ORC + Glue Catalog) is explicitly recommended by AWS for analytical workloads on S3 data lakes.

“For analytical workloads using Amazon EMR and Athena, store data in columnar formats such as Parquet or ORC. These formats provide optimized compression and query performance.”

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

“Use the AWS Glue Data Catalog to manage table schemas and schema evolution for columnar data formats such as Parquet and ORC.”

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

Therefore, using ORC or Parquet along with the Glue Data Catalog achieves the shortest Athena query times and seamless schema management.

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

The DatasetMatch rule in DQDL checks for full value equivalence between mapped fields. A value of 1.0 indicates a 100% match. The correct syntax and metric for an exact match scenario are:

“Use DatasetMatch when comparing mapped fields between two datasets. The comparison score of 1.0 confirms a perfect match.”

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

Options with “100” use incorrect syntax since DQDL uses floating-point scores (e.g., 1.0, 0.95), not percentages.