DEA-C01 Data Ingestion and Transformation Practice Question

A data engineer is building an AWS Glue ETL job that reads large Parquet datasets from Amazon S3 and must optimize performance and cost. The engineer wants to reduce the number of small files written to the target S3 prefix and improve read efficiency. (Choose two.)

⚠ Common exam trap

The trap here is focusing on worker scaling or bookmarks for small-file issues, when the fix is input grouping and output partition control.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Use the AWS Glue groupFiles and groupSize options to coalesce small input files before processing.

Input-side coalescing with groupFiles and groupSize reduces the number of read tasks, while output-side coalesce or repartition controls how many files are written. Together they attack the small-file problem from both ends, improving performance and lowering cost for Parquet datasets in S3.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Set the Glue job's worker type to G.1X and increase the number of workers to the maximum allowed.
Why it's wrong here
Increasing worker count adds parallelism but does not control output file count or coalesce small input files. Without partition management, more workers can actually produce more small output files, worsening the small-file problem and increasing cost without addressing the root cause.
✗
Convert the output format to CSV so that multiple small Parquet files can be merged by the S3 service automatically.
Why it's wrong here
S3 does not automatically merge objects, and switching to CSV would remove Parquet's columnar benefits and compression, hurting query performance. This neither compacts files nor improves read efficiency, and it degrades the dataset for analytics workloads that expect Parquet.
✓
Use the AWS Glue groupFiles and groupSize options to coalesce small input files before processing.
Why this is correct
groupFiles and groupSize allow the Glue reader to combine many small input files into larger groups, reducing the number of tasks and improving read efficiency. This directly addresses the small-file problem on the input side, which lowers overhead and improves job performance when reading Parquet datasets.
✓
Call coalesce or repartition on the DynamicFrame before writing to reduce the number of output files.
Why this is correct
coalesce reduces the number of partitions to match the desired output file count, while repartition reshuffles data for even distribution. Applying one of these before the write prevents many small output files, improving downstream read performance and reducing S3 request overhead.
✗
Enable the Glue job bookmark to skip previously processed files and reduce the number of files read.
Why it's wrong here
Job bookmarks prevent reprocessing of already-handled data across runs, but they do not coalesce small files within a single run or reduce output file count. They address incremental processing, not the small-file compaction and read-efficiency goals described in the scenario.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

Go deeper

DEA-C01 Data Ingestion and Transformation Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

Related to this question