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.

To prevent unintentional file deletions and meet the requirement with minimal operational overhead, enabling S3 Versioning is the best solution.

S3 Versioning:

S3 Versioning allows multiple versions of an object to be stored in the same S3 bucket. When a file is deleted or overwritten, S3 preserves the previous versions, which means you can recover from accidental deletions or modifications.

Enabling versioning requires minimal overhead, as it is a bucket-level setting and does not require additional backup processes or data replication.

Users can recover specific versions of files that were unintentionally deleted, meeting the needs of the data engineer to avoid accidental data loss.

Problem Analysis:

Input Requirements: Gaming app generates approximately 20 KB data records, which must be ingested and made available to three internal consumers with dedicated throughput.

Key Requirements:

High throughput for ingestion from each device.

Dedicated processing bandwidth for each consumer.

Key Considerations:

Amazon Kinesis Data Streams supports high-throughput ingestion with PutRecords API for batch writes.

The Enhanced Fan-Out feature provides dedicated throughput to each consumer, avoiding bandwidth contention.

This solution avoids bottlenecks and ensures optimal throughput for the gaming application and consumers.

Solution Analysis:

Option A: Kinesis Data Streams + Enhanced Fan-Out

PutRecords API is designed for batch writes, improving ingestion performance.

Enhanced Fan-Out allows each consumer to process the stream independently with dedicated throughput.

Option B: Data Firehose + Dedicated Throughput Request

Firehose is not designed for real-time stream processing or fan-out. It delivers data to destinations like S3, Redshift, or OpenSearch, not multiple independent consumers.

Option C: Data Firehose + Enhanced Fan-Out

Firehose does not support enhanced fan-out. This option is invalid.

Option D: Kinesis Data Streams + EC2 Instances

Hosting stream-processing applications on EC2 increases operational overhead compared to native enhanced fan-out.

Final Recommendation:

Use Kinesis Data Streams with Enhanced Fan-Out for high-throughput ingestion and dedicated consumer bandwidth.

Kinesis Data Streams Enhanced Fan-Out

PutRecords API for Batch Writes