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Data Ingestion and Transformation →hardMultiple ChoiceObjective-mapped

DEA-C01 Data Ingestion and Transformation Practice Question

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 runs an AWS Glue ETL job that reads data from Amazon S3, transforms it, and writes back to S3 in a different partition structure. The job uses the 'spark.sql.shuffle.partitions' option set to 200. After the job completes, the output has many small files. The data engineer wants to minimize the number of output files while maintaining job performance. Which action should the engineer take?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

A
Use 'coalesce(n)' with n based on target file size (e.g., 128 MB) before writing.
Coalesce reduces partitions without a full shuffle, minimizing files.
B
Enable S3 multipart upload for the Glue job.
Why wrong: Multipart upload is for large files, not file count.
C
Increase the 'spark.sql.shuffle.partitions' to 500.
Why wrong: Increases parallelism, leading to more files.
D
Reduce the 'spark.sql.shuffle.partitions' to 50.
Why wrong: Reduces parallelism, may cause OOM, but still produces many small files.

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

✓

Use 'coalesce(n)' with n based on target file size (e.g., 128 MB) before writing.

Option A is correct because 'coalesce(n)' reduces the number of partitions without triggering a full shuffle, allowing you to control the number of output files based on a target file size (e.g., 128 MB). This minimizes small files while preserving job performance, as coalesce is a narrow transformation that avoids the overhead of a shuffle. In contrast, 'repartition(n)' would cause a full shuffle, degrading performance.

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.

✓
Use 'coalesce(n)' with n based on target file size (e.g., 128 MB) before writing.
Why this is correct
Coalesce reduces partitions without a full shuffle, minimizing files.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Enable S3 multipart upload for the Glue job.
Why it's wrong here
Multipart upload is for large files, not file count.
✗
Increase the 'spark.sql.shuffle.partitions' to 500.
Why it's wrong here
Increases parallelism, leading to more files.
✗
Reduce the 'spark.sql.shuffle.partitions' to 50.
Why it's wrong here
Reduces parallelism, may cause OOM, but still produces many small files.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often confuse 'coalesce' with 'repartition' or assume that adjusting 'spark.sql.shuffle.partitions' directly controls output file count, when in fact it only controls the number of partitions during shuffle operations, not the final write partition count.

Detailed technical explanation

How to think about this question

Under the hood, 'coalesce(n)' works by merging existing partitions without a full shuffle, using narrow dependencies that minimize data movement across executors. This is particularly effective when the number of output files is too high due to many small partitions, as it combines them into fewer, larger partitions. In real-world scenarios, targeting a file size of 128 MB (matching S3's optimal read chunk size) can significantly reduce S3 LIST and GET costs, and improve downstream processing performance.

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.

TExam Day Tips

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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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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: Use 'coalesce(n)' with n based on target file size (e.g., 128 MB) before writing. — Option A is correct because 'coalesce(n)' reduces the number of partitions without triggering a full shuffle, allowing you to control the number of output files based on a target file size (e.g., 128 MB). This minimizes small files while preserving job performance, as coalesce is a narrow transformation that avoids the overhead of a shuffle. In contrast, 'repartition(n)' would cause a full shuffle, degrading performance.

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

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

Are there clue words in this question I should notice?

Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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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.