- A
Launch an Amazon EMR cluster with Spark, transform the data, and terminate the cluster after completion.
Why wrong: EMR requires provisioning and managing clusters; Glue offers lower operational overhead for scheduled jobs.
- B
Use an AWS Glue ETL job with a schedule trigger to perform the transformation and write to S3.
Glue ETL is serverless, can handle complex transformations, and scheduling is built-in.
- C
Use AWS Lambda functions triggered by S3 events to transform each file incrementally.
Why wrong: Lambda is limited to 15-minute execution and 512 MB disk, unsuitable for large-scale daily transformations.
- D
Use Amazon Athena to run CTAS queries to convert and partition the data daily.
Why wrong: Athena CTAS is convenient but charges per scan; for daily runs, Glue ETL is more cost-effective and offers more transformation capabilities.
Quick Answer
The answer is an AWS Glue ETL job with a schedule trigger because it provides a fully serverless ETL solution that converts JSON to Parquet for Athena with minimal operational overhead. Glue ETL jobs are purpose-built for batch transformations like cleaning, deduplication, and enrichment of large datasets, and they automatically handle partitioning and schema inference when combined with Glue Crawlers. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of cost-effective serverless ETL patterns versus alternatives like Athena (a query engine, not ETL), EMR (requires cluster management), and Lambda (limited by 15-minute execution and memory for large datasets). A common trap is choosing Lambda for its simplicity, but Glue is designed for heavy-lift data processing at scale. Memory tip: “Glue glues your data together” — for batch ETL and cataloging, Glue is the serverless glue that holds the pipeline.
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. 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 transform raw clickstream data (JSON files) stored in S3 into a partitioned Parquet dataset for querying with Athena. The transformation includes cleaning, deduplication, and enrichment. The pipeline should run daily. Which solution is MOST cost-effective and requires the least operational overhead?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"least"Why it matters: You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
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 an AWS Glue ETL job with a schedule trigger to perform the transformation and write to S3.
Option B is correct because AWS Glue crawlers can catalog the data, and Glue ETL jobs are serverless, cost-effective, and can be scheduled. Option A is wrong because Athena is for querying, not ETL. Option C is wrong because EMR requires cluster management. Option D is wrong because Lambda has execution time limits and is not ideal for large datasets.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Launch an Amazon EMR cluster with Spark, transform the data, and terminate the cluster after completion.
Why it's wrong here
EMR requires provisioning and managing clusters; Glue offers lower operational overhead for scheduled jobs.
- ✓
Use an AWS Glue ETL job with a schedule trigger to perform the transformation and write to S3.
Why this is correct
Glue ETL is serverless, can handle complex transformations, and scheduling is built-in.
Clue confirmation
The clue word "least" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
- ✗
Use AWS Lambda functions triggered by S3 events to transform each file incrementally.
Why it's wrong here
Lambda is limited to 15-minute execution and 512 MB disk, unsuitable for large-scale daily transformations.
- ✗
Use Amazon Athena to run CTAS queries to convert and partition the data daily.
Why it's wrong here
Athena CTAS is convenient but charges per scan; for daily runs, Glue ETL is more cost-effective and offers more transformation capabilities.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Data Engineering — This question tests Data Engineering — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Use an AWS Glue ETL job with a schedule trigger to perform the transformation and write to S3. — Option B is correct because AWS Glue crawlers can catalog the data, and Glue ETL jobs are serverless, cost-effective, and can be scheduled. Option A is wrong because Athena is for querying, not ETL. Option C is wrong because EMR requires cluster management. Option D is wrong because Lambda has execution time limits and is not ideal for large datasets.
What should I do if I get this MLS-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
Are there clue words in this question I should notice?
Yes — watch for: "least". You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 20, 2026
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