Amazon DEA-C01 Exam Preparation Material | AWS Certified Data Engineer - Associate (DEA-C01)

Explanation:
This solution meets the requirement of logging all writes to the S3 bucket into another S3 bucket with the least operational effort. AWS CloudTrail is a service that records the API calls made to AWS services, including Amazon S3. By creating a trail of data events, you can capture the details of the requests that are made to the transactions S3 bucket, such as the requester, the time, the IP address, and the response elements. By specifying an empty prefix and write-only events, you can filter the data events to only include the ones that write to the bucket. By specifying the logs S3 bucket as the destination bucket, you can store the CloudTrail logs in another S3 bucket that is in the same AWS Region. This solution does not require any additional coding or configuration, and it is more scalable and reliable than using S3 Event Notifications and Lambda functions.
Reference: Logging Amazon S3 API calls using AWS CloudTrail
Creating a trail for data events
Enabling Amazon S3 server access logging

Explanation:
Amazon Athena achieves the fastest query performance when data is stored in columnar formats such as Apache Parquet and when queries can take advantage of partition pruning and predicate pushdown.
Converting CSV files to Parquet significantly reduces the amount of data scanned because Parquet stores data in a column-oriented layout. Since Athena queries only a subset of attributes, it reads only the required columns instead of scanning entire rows, which dramatically improves performance. Predicate pushdown further reduces query time by filtering data at the storage layer.
Partitioning the data by date ensures that Athena scans only the relevant partitions for same-day queries, minimizing unnecessary data reads. Storing one Parquet file per day is efficient and avoids the overhead of managing excessive small files.
ORC is also a columnar format, but Parquet is more commonly optimized and recommended for Athena workloads in AWS exam guidance. JSON and Avro are row-based or semi-row-based formats and result in larger scan sizes and slower query execution.
Therefore, Option D provides the shortest query times and aligns with Athena performance best practices.

Explanation:
Apache Iceberg query performance depends heavily on metadata optimization and file layout efficiency. Generating column-level statistics allows query engines such as AWS Glue, Amazon Athena, and Amazon Redshift to perform predicate pushdown and data skipping. These statistics help avoid scanning unnecessary Parquet files and significantly reduce query execution time.
Real-time streaming workloads often generate many small files, which degrades query performance. Table compaction consolidates small Parquet files into fewer, larger files, improving scan efficiency and reducing metadata overhead. Iceberg supports compaction as a core optimization technique, and Glue can schedule these operations automatically.
Iceberg does not use traditional indexes, so index optimization is not applicable. Copy-on-write affects write semantics and consistency, not query speed. Views do not improve physical query performance.
Therefore, generating statistics and compacting files are the two most effective optimizations.

Explanation:
Amazon DynamoDB is optimized for Query operations, which retrieve items efficiently using the partition key and optionally the sort key. Queries consume significantly fewer RCUs than Scan operations because only matching partitions are read.
By performing a Query using the partition key in the key condition expression, the AWS Glue job reads only the required items directly from DynamoDB. This minimizes the number of read capacity units consumed and avoids unnecessary reads.
Using a Scan operation, even when followed by filtering in a DynamicFrame, consumes RCUs for the entire table or index and is highly inefficient. Using only the sort key in a query is invalid without the partition key and would not meet DynamoDB query requirements. The Glue DynamoDB connector filter option still performs a scan under the hood and does not minimize RCUs.
The queried data can be directly loaded into a DynamicFrame, satisfying the existing processing logic requirement without additional transformations.
Therefore, Option D provides the most efficient and cost-effective solution.

Explanation:
Scheduling an AWS Glue crawler to periodically update the Data Catalog automates the process of detecting new partitions and updating the catalog, which minimizes manual maintenance and operational overhead.