The correct answer is to combine the small JSON files into larger Parquet files using a Spark job on Amazon EMR. This approach directly addresses the core performance bottleneck of SageMaker training data optimization for small files: when a dataset consists of millions of tiny objects, SageMaker’s S3 data loading incurs excessive LIST API calls and metadata overhead, dramatically slowing I/O throughput. By coalescing the data into fewer, larger Parquet files, you reduce the number of S3 requests and enable columnar compression and predicate pushdown, which accelerates training. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of data preprocessing for distributed training—a common trap is to confuse S3 Select or Athena as performance solutions, but these are for querying, not for optimizing training data layout. Remember the memory tip: “Small files, big pain; Parquet and Spark break the chain.”
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 wants to use Amazon SageMaker to train a model on a dataset stored in Amazon S3. The dataset is 100 GB and consists of millions of small JSON files. What should the data engineering team do to optimize training performance?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Combine the small JSON files into larger Parquet files using a Spark job on Amazon EMR.
Option D is correct because combining small files into larger ones reduces S3 LIST overhead and improves I/O performance. Option A is wrong because Athena is not needed for training. Option B is wrong because EBS is ephemeral and not shared across instances. Option C is wrong because S3 Select is for server-side filtering, not for training 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.
✓
Combine the small JSON files into larger Parquet files using a Spark job on Amazon EMR.
Why this is correct
Parquet with larger files improves read efficiency and reduces overhead.
Related concept
Read the scenario before looking for a memorised answer.
✗
Copy the data to an Amazon EBS volume attached to the training instance.
Why it's wrong here
EBS volumes are not shared and have limited throughput; not scalable.
✗
Use Amazon Athena to convert the data into a single CSV file.
Why it's wrong here
Athena converts data but output may still have many files; also adds latency.
✗
Use S3 Select to filter data before training.
Why it's wrong here
S3 Select reduces data transferred but does not optimize file read patterns.
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.
Trap categories for this question
Command / output trap
Athena converts data but output may still have many files; also adds latency.
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.
What to study next
Got this wrong? Here's your next step.
Identify which MLS-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.
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Combine the small JSON files into larger Parquet files using a Spark job on Amazon EMR. — Option D is correct because combining small files into larger ones reduces S3 LIST overhead and improves I/O performance. Option A is wrong because Athena is not needed for training. Option B is wrong because EBS is ephemeral and not shared across instances. Option C is wrong because S3 Select is for server-side filtering, not for training performance.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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