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.

Option B is the most operationally efficient because it checks the business requirement directly: whether the target table contains the most recent data, not merely whether a job failed. Monitoring only failures (Options C and D) can produce false positives (a job failure might not impact freshness) and false negatives (a job can succeed but still load stale or incomplete data). The study material emphasizes implementing data quality validation as part of the ETL process so data can be verified before or as it is stored, rather than relying only on pipeline execution status.

Using a data quality rule focused on freshness (for example, validating that a “max event timestamp” or “latest partition date” meets today’s expected value) lets the pipeline detect stale loads even when the workflow runs. Then, an EventBridge rule can route failures of that data quality check to SNS for immediate notification, keeping operations serverless and centralized. Macie (Option A) is designed for sensitive-data discovery/classification, not operational “freshness” checks on Redshift tables, so it adds unnecessary services and effort compared to a Glue-native data quality validation approach.

 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