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

AWS Glue DataBrew provides a no-code data preparation and validation interface. It allows you to set data profiling, completeness checks, and numeric range validations directly through its UI—ideal for IoT validation use cases.

“AWS Glue DataBrew enables users to define validation rules such as completeness and value range checks without writing code.”

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

This fulfills the requirement for minimal coding and automatic data quality flagging.

 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

Amazon Kinesis Data Firehose is a fully managed data ingestion service that enables reliable and scalable delivery of streaming and batch data into Amazon S3 with minimal operational overhead. This directly satisfies the requirement for a fully managed AWS ingestion service while avoiding the need to provision, scale, or manage infrastructure.

By using the Amazon Kinesis Agent on the on-premises servers, the company can automatically forward daily data files to Kinesis Data Firehose. Firehose handles buffering, retry logic, scaling, and delivery without requiring administrative effort. Delivering the data to Amazon S3 allows seamless integration with Amazon Athena, which natively queries data stored in S3 without requiring data movement or transformation.

Enabling Amazon S3 versioning protects files from accidental deletion by preserving previous versions of objects. This aligns with AWS best practices for data durability and governance, especially for analytics workloads and compliance requirements.

Other options introduce unnecessary operational complexity. AWS DataSync with Amazon EFS is not optimized for Athena-based analytics. AWS Glue jobs and Amazon RDS are unsuitable for file-based analytical access. A self-managed Apache Kafka solution with Amazon MSK significantly increases operational overhead.

Therefore, option B is the most efficient, scalable, and operationally optimal solution according to AWS Certified Data Engineer – Associate best practices.