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

 Amazon Athena is a serverless interactive query service that allows you to analyze data in Amazon S3 using standard SQL. Athena supports various data formats, such as CSV, JSON, ORC, Avro, and Parquet. However, not all data formats are equally efficient for querying. Some data formats, such as CSV and JSON, are row-oriented, meaning that they store data as a sequence of records, each with the same fields. Row-oriented formats are suitable for loading and exporting data, but they are not optimal for analytical queries that often access only a subset of columns. Row-oriented formats also do not support compression or encoding techniques that can reduce the data size and improve the query performance.

On the other hand, some data formats, such as ORC and Parquet, are column-oriented, meaning that they store data as a collection of columns, each with a specific data type. Column-oriented formats are ideal for analytical queries that often filter, aggregate, or join data by columns. Column-oriented formats also support compression and encoding techniques that can reduce the data size and improve the query performance. For example, Parquet supports dictionary encoding, which replaces repeated values with numeric codes, and run-length encoding, which replaces consecutive identical values with a single value and a count. Parquet also supports various compression algorithms, such as Snappy, GZIP, and ZSTD, that can further reduce the data size and improve the query performance.

Therefore, changing the data format from CSV to Parquet and applying Snappy compression will most speed up the Athena query performance. Parquet is a column-oriented format that allows Athena to scan only the relevant columns and skip the rest, reducing the amount of data read from S3. Snappy is a compression algorithm that reduces the data size without compromising the query speed, as it is splittable and does not require decompression before reading. This solution will also reduce the cost of Athena queries, as Athena charges based on the amount of data scanned from S3.

The other options are not as effective as changing the data format to Parquet and applying Snappy compression. Changing the data format from CSV to JSON and applying Snappy compression will not improve the query performance significantly, as JSON is also a row-oriented format that does not support columnar access or encoding techniques. Compressing the CSV files by using Snappy compression will reduce the data size, but it will not improve the query performance significantly, as CSV is still a row-oriented format that does not support columnar access or encoding techniques. Compressing the CSV files by using gzjg compression will reduce the data size, but it will degrade the query performance, as gzjg is not a splittable compression algorithm and requires decompression before reading. References:

Amazon Athena

Choosing the Right Data Format

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 5: Data Analysis and Visualization, Section 5.1: Amazon Athena

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.

Apache Iceberg is a table format designed for large-scale data lakes that supports ACID transactions, schema evolution, time travel, and row-level updates and deletes. Using S3 Tables with Apache Iceberg provides a fully managed experience that integrates natively with Amazon Athena, Amazon Redshift, and Amazon EMR.

By using AWS Glue with the Iceberg catalog, the data engineer can perform daily updates and deletions without managing Spark clusters, compaction scheduling, or metadata cleanup manually. Iceberg handles snapshots, file pruning, and unreferenced file removal automatically, significantly reducing operational overhead.

Apache Hudi requires Amazon EMR clusters, Spark jobs, and manual compaction orchestration, increasing complexity. The Parquet-only approaches in options C and D do not support updates or deletes efficiently and would require full rewrites of datasets, which is not scalable.

Therefore, using S3 Tables with Apache Iceberg provides the most efficient, scalable, and low-maintenance solution that satisfies all query and update requirements.