- A
Use S3 Select with Glue to filter data before transformation.
Why wrong: S3 Select is used with Athena or applications, not directly within Glue jobs.
- B
Use parallel reads with pushdown predicates in the Glue job's source connection, and write the output in columnar format (Parquet) partitioned by date.
Parallel reads with partition pushdown reduce load on RDS and speed up extraction; Parquet with partitioning reduces storage and query costs.
- C
Increase the number of Glue DPUs to 100 and enable job bookmarking.
Why wrong: Increasing DPUs may not improve performance if the source database is the bottleneck; job bookmarking helps with incremental processing but not with current backfill.
- D
Use Amazon Redshift Spectrum to perform transformations in place on S3.
Why wrong: Redshift Spectrum is for querying external data, not for transforming it.
Quick Answer
The correct answer is to use parallel reads with pushdown predicates in the Glue job’s source connection and write the output in columnar format (Parquet) partitioned by date. This approach directly addresses the bottleneck of extracting 100 GB from Amazon RDS for MySQL by splitting the workload across multiple connections, while pushdown predicates filter data at the source to reduce the volume transferred. Writing to Parquet, a columnar format, minimizes storage costs and dramatically speeds up downstream queries in services like Athena, which is critical when data volume grows 10x. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of Glue job optimization for large-scale ETL, often appearing as a scenario where you must distinguish between scaling compute (DPUs) and optimizing data movement. A common trap is choosing to increase DPUs alone, which fails if the source database is the bottleneck. Memory tip: “Parallel pulls, pushdown filters, Parquet partitions” — the three P’s for Glue performance.
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 company uses AWS Glue to run ETL jobs that transform data from Amazon RDS for MySQL to Amazon S3. The current job runs daily and takes 3 hours to process 100 GB of data. The company expects data volume to grow 10x in the next year. They need to reduce job runtime and cost. Which approach should they take?
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 parallel reads with pushdown predicates in the Glue job's source connection, and write the output in columnar format (Parquet) partitioned by date.
Option D is correct because parallel reads from RDS with pushdown predicates reduce the load on the source and speed up extraction; using columnar formats like Parquet reduces storage and scanning costs in Athena. Option A is wrong because increasing DPUs without changing the extraction method may not help if the bottleneck is the source database. Option B is wrong because S3 Select is for server-side filtering, not for Glue jobs. Option C is wrong because Redshift Spectrum is for querying data in S3, not for transforming it.
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 S3 Select with Glue to filter data before transformation.
Why it's wrong here
S3 Select is used with Athena or applications, not directly within Glue jobs.
- ✓
Use parallel reads with pushdown predicates in the Glue job's source connection, and write the output in columnar format (Parquet) partitioned by date.
Why this is correct
Parallel reads with partition pushdown reduce load on RDS and speed up extraction; Parquet with partitioning reduces storage and query costs.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the number of Glue DPUs to 100 and enable job bookmarking.
Why it's wrong here
Increasing DPUs may not improve performance if the source database is the bottleneck; job bookmarking helps with incremental processing but not with current backfill.
- ✗
Use Amazon Redshift Spectrum to perform transformations in place on S3.
Why it's wrong here
Redshift Spectrum is for querying external data, not for transforming it.
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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
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 parallel reads with pushdown predicates in the Glue job's source connection, and write the output in columnar format (Parquet) partitioned by date. — Option D is correct because parallel reads from RDS with pushdown predicates reduce the load on the source and speed up extraction; using columnar formats like Parquet reduces storage and scanning costs in Athena. Option A is wrong because increasing DPUs without changing the extraction method may not help if the bottleneck is the source database. Option B is wrong because S3 Select is for server-side filtering, not for Glue jobs. Option C is wrong because Redshift Spectrum is for querying data in S3, not for transforming it.
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.
About these practice questions
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Last reviewed: Jun 20, 2026
This MLS-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 MLS-C01 exam.
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