The answer is to use scheduled AWS Glue jobs that run every hour, consolidating multiple small batches of data. This approach minimizes cost by reducing the number of job invocations while still keeping latency acceptable for near-real-time needs, as Glue’s Spark-based serverless ETL engine efficiently transforms CSV, JSON, and Avro into Parquet format and catalogs the schema in the Glue Data Catalog for Athena queries. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of trade-offs between cost and latency in serverless data pipelines—a common scenario where continuous streaming via Firehose may not support all source formats, and Lambda is limited by its 15-minute timeout and memory constraints. A frequent trap is choosing a daily job to cut costs further, but that introduces too much latency for 10-minute batch arrivals. Memory tip: “Batch and wait, don’t stream too late—hourly Glue keeps costs straight.”
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 startup is building a data pipeline that ingests data from multiple sources into an Amazon S3 data lake. The data includes CSV files from legacy systems, JSON from web APIs, and Avro from mobile apps. The data must be transformed into Parquet format and cataloged for querying with Amazon Athena. The pipeline must be serverless and minimize operational overhead. The team has decided to use AWS Glue for ETL and cataloging. However, they are concerned about the cost of running Glue jobs continuously. The data arrives in small batches every 10 minutes. Which approach should the team use to minimize cost while meeting the requirements?
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.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use scheduled Glue jobs to process the data every hour, consolidating multiple batches
Triggering Glue jobs on schedule (e.g., every hour) to process accumulated data reduces the number of job runs and cost, while still meeting near-real-time needs. Option A is wrong because continuous streaming with Firehose may not handle all source formats. Option C is wrong because using Lambda for transformation is limited by timeout and memory. Option D is wrong because running a single daily job may introduce too much latency.
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 AWS Lambda functions to transform each file upon arrival and store as Parquet
Why it's wrong here
Lambda has limitations on execution time and memory for large transformations.
✗
Use Amazon Kinesis Data Firehose to stream data directly into S3 and use Glue to catalog it
Why it's wrong here
Firehose can deliver to S3 but does not transform to Parquet for all source types.
✓
Use scheduled Glue jobs to process the data every hour, consolidating multiple batches
Why this is correct
Hourly batch processing balances cost and latency.
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.
✗
Use a single daily Glue job to process all data at once
Why it's wrong here
Daily processing may not meet latency requirements of data arriving every 10 minutes.
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 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.
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: Use scheduled Glue jobs to process the data every hour, consolidating multiple batches — Triggering Glue jobs on schedule (e.g., every hour) to process accumulated data reduces the number of job runs and cost, while still meeting near-real-time needs. Option A is wrong because continuous streaming with Firehose may not handle all source formats. Option C is wrong because using Lambda for transformation is limited by timeout and memory. Option D is wrong because running a single daily job may introduce too much latency.
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.
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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Question Discussion
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