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.

The streaming ingestion feature of Amazon Redshift enables you to ingest data from streaming sources, such as Amazon Kinesis Data Streams, into Amazon Redshift tables in near real-time. You can use the streaming ingestion feature to process the streaming data from the wearable devices, hospital equipment, and patient records. The streaming ingestion feature also supports incremental updates, which means you can append new data or update existing data in the Amazon Redshift tables. This way, you can store the data in an Amazon Redshift Serverless warehouse and support near real-time analytics of the streaming data and the previous day’s data. This solution meets the requirements with the least operational overhead, as it does not require any additional services or components to ingest and process the streaming data. The other options are either not feasible or not optimal. Loading data into Amazon Kinesis Data Firehose and then into Amazon Redshift (option A) would introduce additional latency and cost, as well as require additional configuration and management. Loading data into Amazon S3 and then using the COPY command to load the data into Amazon Redshift (option C) would also introduce additional latency and cost, as well as require additional storage space and ETL logic. Using the Amazon Aurora zero-ETL integration with Amazon Redshift (option D) would not work, as it requires the data to be stored in Amazon Aurora first, which is not the case for the streaming data from the healthcare company. References:

Using streaming ingestion with Amazon Redshift

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.5: Amazon Redshift Streaming Ingestion

Option B is correct because CloudWatch Logs subscription filters are the native way to send only matching log events to a downstream destination such as Amazon Data Firehose. AWS states that a subscription filter defines the filter pattern for which log events get delivered and where they are sent. AWS also documents that CloudWatch Logs events can be sent to Firehose by using subscription filters. This directly satisfies the requirement to deliver only relevant logs. Once the logs are in Firehose, AWS documents that Firehose can invoke an AWS Lambda function to transform incoming source data before delivering it to the destination, which satisfies the requirement to deliver the logs in a compatible format for analysis.

Option A is incorrect because S3 bucket rules do not filter CloudWatch log events by timestamp for ingestion control, and using S3 triggers adds unnecessary post-delivery processing. Option C is invalid because the subscription filter applies at the CloudWatch Logs log group, not by “monitoring the Firehose stream.” Option D is incorrect because Firehose does not natively ingest only CloudWatch log groups based on tags or automatically reformat arbitrary logs without a transformation step. The most reliable and AWS-native design is subscription filtering + Firehose + Lambda transformation.