- A
AWS Glue ETL job
Glue ETL can process streaming data (via Glue streaming ETL), convert to Parquet, partition, and catalog the output.
- B
Amazon EMR with Spark Streaming
Why wrong: EMR can do this, but it's more complex to manage than Glue's serverless offering. Glue is simpler for this use case.
- C
Amazon Kinesis Data Analytics
Why wrong: Kinesis Data Analytics performs real-time SQL analytics but does not natively catalog data or write partitioned Parquet to S3.
- D
Amazon SageMaker Data Wrangler
Why wrong: Data Wrangler is for interactive data preparation, not for building production streaming pipelines.
Question 659 of 1,000
mediumMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A data engineer needs to integrate a new…
This MLA-C01 practice question tests your understanding of a data engineer needs to integrate a new…. 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 data engineer needs to integrate a new streaming data source into an existing ML pipeline. The data arrives as JSON records and must be transformed to Parquet format, partitioned by date, and stored in Amazon S3. The engineer also needs to catalog the data for querying with Amazon Athena. Which service should be used to perform the transformation and cataloging?
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
✓
AWS Glue ETL job
AWS Glue ETL jobs can read streaming data (e.g., from Kinesis), transform it (e.g., JSON to Parquet), write to S3 with partitioning, and update the Glue Data Catalog for Athena to query. This is a managed, serverless solution.
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.
- ✓
AWS Glue ETL job
Why this is correct
Glue ETL can process streaming data (via Glue streaming ETL), convert to Parquet, partition, and catalog the output.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon EMR with Spark Streaming
Why it's wrong here
EMR can do this, but it's more complex to manage than Glue's serverless offering. Glue is simpler for this use case.
- ✗
Amazon Kinesis Data Analytics
Why it's wrong here
Kinesis Data Analytics performs real-time SQL analytics but does not natively catalog data or write partitioned Parquet to S3.
- ✗
Amazon SageMaker Data Wrangler
Why it's wrong here
Data Wrangler is for interactive data preparation, not for building production streaming pipelines.
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 MLA-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 MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: AWS Glue ETL job — AWS Glue ETL jobs can read streaming data (e.g., from Kinesis), transform it (e.g., JSON to Parquet), write to S3 with partitioning, and update the Glue Data Catalog for Athena to query. This is a managed, serverless solution.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-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.
About these practice questions
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Last reviewed: Jul 4, 2026
This MLA-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 MLA-C01 exam.
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