The answer is the G.1X worker type, which provides more memory per worker than the Standard type, making it the most cost-effective way to improve Glue ETL job performance. This works because many ETL jobs, especially those processing Parquet data for machine learning, are memory-bound rather than CPU-bound; the G.1X worker doubles the memory per DPU without increasing the DPU count, so you get better resource utilization and faster execution for the same cost. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of Glue worker types and the trade-off between scaling horizontally (adding more DPUs) and scaling vertically (using a memory-optimized worker). A common trap is to assume more DPUs always solve performance issues, but that increases cost linearly. Remember the memory tip: “G.1X gives your data more room to breathe” — it’s the lean upgrade when your job is choking on memory, not parallelism.
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. 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 prepare data for machine learning. The data is stored in Amazon S3 in Parquet format. A data engineer notices that the Glue job is running slowly and consuming a lot of resources. What is the MOST cost-effective way to improve the performance of the Glue job?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use the G.1X worker type, which provides more memory per worker compared to the Standard worker type.
Increasing the number of DPUs (Data Processing Units) in AWS Glue can improve parallelism and reduce job runtime, but it increases cost. Using G.1X worker type with more memory per worker can improve performance without increasing DPU count, offering better resource utilization. Switching to CSV may degrade performance. Using partition pruning on the source data can reduce data scanned but may not address resource consumption.
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 the G.1X worker type, which provides more memory per worker compared to the Standard worker type.
Why this is correct
G.1X offers more memory, reducing memory-related bottlenecks without increasing DPU count.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use partition pruning on the source data to reduce the amount of data processed.
Why it's wrong here
Partition pruning reduces data scanned but does not directly improve resource consumption efficiency.
✗
Switch the output format from Parquet to CSV to reduce processing overhead.
Why it's wrong here
CSV is not columnar and may increase I/O and storage costs.
✗
Use a larger instance type for the Glue job by increasing the number of DPUs.
Why it's wrong here
More DPUs increase parallelism but also cost; may not be cost-effective.
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 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.
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the G.1X worker type, which provides more memory per worker compared to the Standard worker type. — Increasing the number of DPUs (Data Processing Units) in AWS Glue can improve parallelism and reduce job runtime, but it increases cost. Using G.1X worker type with more memory per worker can improve performance without increasing DPU count, offering better resource utilization. Switching to CSV may degrade performance. Using partition pruning on the source data can reduce data scanned but may not address resource consumption.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.