The answer is to select the appropriate distributed training strategy, use EFA (Elastic Fabric Adapter), and choose GPU-optimized instances. These three steps directly address the core bottlenecks in large-scale distributed training on SageMaker: network latency, scaling efficiency, and compute throughput. EFA provides a low-latency, high-bandwidth network interface that is critical for synchronizing gradients across multiple nodes, while GPU instances like p4d or p5 deliver the raw compute power needed for massive model parallelism or data parallelism. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of infrastructure choices versus storage or inference services—a common trap is confusing EBS volume attachment (which aids data loading, not inter-node communication) or batch transform (an inference feature) with training optimization. Remember the mnemonic "GES": GPU, EFA, Strategy—these are the three pillars that make distributed training fly.
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
Which THREE steps should be taken to optimize a large-scale distributed training job on SageMaker? (Choose 3.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use GPU instances with high bandwidth and memory (e.g., ml.p4d.24xlarge).
Options A, C, and D are correct. Using EFA (Elastic Fabric Adapter) reduces network latency, choosing the right distribution strategy (e.g., data parallelism vs model parallelism) improves scaling, and using GPU-optimized instances provides high compute. Option B is wrong because attaching additional EBS volumes does not directly help distributed training performance. Option E is wrong because batch transform is for inference, not training.
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.
✗
Attach multiple EBS volumes with throughput provisioning.
Why it's wrong here
EBS enhancements affect storage I/O, not network communication.
✓
Use GPU instances with high bandwidth and memory (e.g., ml.p4d.24xlarge).
Why this is correct
GPU instances are necessary for large model training.
Related concept
Read the scenario before looking for a memorised answer.
✗
Enable batch transform for offline inference after training.
Why it's wrong here
Batch transform is for inference, not training optimization.
✓
Use Elastic Fabric Adapter (EFA) for low-latency inter-node communication.
Why this is correct
EFA improves network performance for distributed deep learning.
Related concept
Read the scenario before looking for a memorised answer.
✓
Select the appropriate distributed training strategy (e.g., Horovod, SageMaker data parallel, or model parallel).
Why this is correct
Choosing the right strategy maximizes efficiency.
Related concept
Read the scenario before looking for a memorised answer.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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.
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use GPU instances with high bandwidth and memory (e.g., ml.p4d.24xlarge). — Options A, C, and D are correct. Using EFA (Elastic Fabric Adapter) reduces network latency, choosing the right distribution strategy (e.g., data parallelism vs model parallelism) improves scaling, and using GPU-optimized instances provides high compute. Option B is wrong because attaching additional EBS volumes does not directly help distributed training performance. Option E is wrong because batch transform is for inference, not training.
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.
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Question Discussion
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