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Question 240 of 1,786

Data Ingestion and Transformation →hardMultiple ChoiceObjective-mapped

Improve AWS Glue Performance by Consolidating Small Files Before Processing

Q: How is Data Ingestion and Transformation tested on the DEA-C01 exam?

Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..

Q: What is the correct answer to this Data Ingestion and Transformation DEA-C01 question?

The correct answer is: Pre-process the data to consolidate small files into larger files before the Glue job. — The most impactful optimization for reducing the runtime of a Glue job that reads millions of small files is to consolidate those files into larger files prior to processing. Each small file incurs overhead for listing, opening, and reading, and Spark's task scheduler must create a separate task for each file or partition. By grouping small files (e.g., via S3 batch operations or a compaction job), the number of files decreases dramatically, reducing scheduling overhead and I/O operations. While converting to Parquet (Option B) and increasing DPUs (Option C) can improve performance, their benefits are limited if the underlying file count remains high. A larger Spark shuffle partition size (Option D) only affects shuffle operations, not the initial file read overhead. Therefore, file consolidation is the most effective single change.

Q: Why do candidates get Data Ingestion and Transformation questions wrong on the DEA-C01 exam?

Identify which DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

This DEA-C01 practice question tests your understanding of data ingestion and transformation. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

A company uses AWS Glue to process data from multiple S3 buckets. The Glue job runs daily and reads data from a bucket that contains millions of small files (each < 1 MB). The job has been running for hours and is often close to the 8-hour timeout limit. Which optimization would MOST reduce the job's runtime?

A
Pre-process the data to consolidate small files into larger files before the Glue job.
Fewer, larger files reduce the overhead of opening and reading files.
B
Convert the source data from CSV to Parquet format.
Why wrong: Parquet is efficient but still many small files cause overhead.
C
Increase the number of DPUs allocated to the Glue job.
Why wrong: More DPUs can improve parallelism but the overhead of many small files remains.
D
Use a larger Spark shuffle partition size.
Why wrong: Shuffle partitions affect data shuffling, not input read performance.

Full breakdown with real-world context →

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

✓

Pre-process the data to consolidate small files into larger files before the Glue job.

The most impactful optimization for reducing the runtime of a Glue job that reads millions of small files is to consolidate those files into larger files prior to processing. Each small file incurs overhead for listing, opening, and reading, and Spark's task scheduler must create a separate task for each file or partition. By grouping small files (e.g., via S3 batch operations or a compaction job), the number of files decreases dramatically, reducing scheduling overhead and I/O operations. While converting to Parquet (Option B) and increasing DPUs (Option C) can improve performance, their benefits are limited if the underlying file count remains high. A larger Spark shuffle partition size (Option D) only affects shuffle operations, not the initial file read overhead. Therefore, file consolidation is the most effective single change.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Answer analysis

Option-by-option breakdown

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

✓
Pre-process the data to consolidate small files into larger files before the Glue job.
Why this is correct
Fewer, larger files reduce the overhead of opening and reading files.
Related concept
Read the scenario before looking for a memorised answer.
✗
Convert the source data from CSV to Parquet format.
Why it's wrong here
Parquet is efficient but still many small files cause overhead.
✗
Increase the number of DPUs allocated to the Glue job.
Why it's wrong here
More DPUs can improve parallelism but the overhead of many small files remains.
✗
Use a larger Spark shuffle partition size.
Why it's wrong here
Shuffle partitions affect data shuffling, not input read performance.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.

Detailed technical explanation

How to think about this question

This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.

KKey Concepts to Remember

Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.

TExam Day Tips

Underline the problem statement mentally.
Watch for words such as best, first, most likely and least administrative effort.
Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.

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

What to study next

Got this wrong? Here's your next step.

Identify which DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

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FAQ

Questions learners often ask

What does this DEA-C01 question test?

Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Pre-process the data to consolidate small files into larger files before the Glue job. — The most impactful optimization for reducing the runtime of a Glue job that reads millions of small files is to consolidate those files into larger files prior to processing. Each small file incurs overhead for listing, opening, and reading, and Spark's task scheduler must create a separate task for each file or partition. By grouping small files (e.g., via S3 batch operations or a compaction job), the number of files decreases dramatically, reducing scheduling overhead and I/O operations. While converting to Parquet (Option B) and increasing DPUs (Option C) can improve performance, their benefits are limited if the underlying file count remains high. A larger Spark shuffle partition size (Option D) only affects shuffle operations, not the initial file read overhead. Therefore, file consolidation is the most effective single change.

What should I do if I get this DEA-C01 question wrong?

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

About these practice questions

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This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.