- A
Enable Elastic Fabric Adapter (EFA) for faster inter-node connectivity.
Why wrong: Network is already near max; EFA may help but the root cause is communication frequency, not bandwidth.
- B
Increase the batch size to improve GPU utilization.
Why wrong: Larger batch sizes may not fit into GPU memory and can degrade model quality.
- C
Increase the number of instances from 4 to 8 to add more GPUs.
Why wrong: More instances increase all-reduce overhead and may not improve GPU utilization if communication is bottleneck.
- D
Switch to SageMaker model parallel library with pipeline parallelism to reduce communication overhead.
Model parallelism partitions the model across devices, reducing communication volume and improving utilization.
Question 680 of 1,000
hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A financial services company is training a large…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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.
A financial services company is training a large natural language processing (NLP) model using PyTorch on a SageMaker distributed training job. The cluster consists of 4 ml.p3.16xlarge instances (8 GPUs each). The training job runs successfully but takes 72 hours, exceeding the allotted 48-hour window. The team must reduce training time without sacrificing model quality. The model architecture has 1.5 billion parameters and currently uses the SageMaker data parallel library with Horovod for all-reduce. Observing CloudWatch metrics, the team notices that GPU utilization averages only 45% and network throughput is near maximum. Which action will most effectively reduce training time?
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
✓
Switch to SageMaker model parallel library with pipeline parallelism to reduce communication overhead.
The correct answer is D. The problem is communication overhead due to frequent all-reduce of large gradients in data parallelism, leading to low GPU utilization (45%) and saturated network throughput. Switching to SageMaker model parallel library with pipeline parallelism reduces communication overhead by splitting the model across GPUs, decreasing the size of gradients that need to be synchronized. This allows GPUs to spend more time computing, improving utilization and reducing training time. Option A (EFA) improves network speed, but the network is already near maximum; the bottleneck is the frequency of communication, not speed. Option B (increasing batch size) may improve GPU utilization but risks memory overflow and increases communication overhead. Option C (adding more instances) adds GPUs but also increases all-reduce traffic, likely worsening the bottleneck.
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.
- ✗
Enable Elastic Fabric Adapter (EFA) for faster inter-node connectivity.
Why it's wrong here
Network is already near max; EFA may help but the root cause is communication frequency, not bandwidth.
- ✗
Increase the batch size to improve GPU utilization.
Why it's wrong here
Larger batch sizes may not fit into GPU memory and can degrade model quality.
- ✗
Increase the number of instances from 4 to 8 to add more GPUs.
Why it's wrong here
More instances increase all-reduce overhead and may not improve GPU utilization if communication is bottleneck.
- ✓
Switch to SageMaker model parallel library with pipeline parallelism to reduce communication overhead.
Why this is correct
Model parallelism partitions the model across devices, reducing communication volume and improving utilization.
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
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Switch to SageMaker model parallel library with pipeline parallelism to reduce communication overhead. — The correct answer is D. The problem is communication overhead due to frequent all-reduce of large gradients in data parallelism, leading to low GPU utilization (45%) and saturated network throughput. Switching to SageMaker model parallel library with pipeline parallelism reduces communication overhead by splitting the model across GPUs, decreasing the size of gradients that need to be synchronized. This allows GPUs to spend more time computing, improving utilization and reducing training time. Option A (EFA) improves network speed, but the network is already near maximum; the bottleneck is the frequency of communication, not speed. Option B (increasing batch size) may improve GPU utilization but risks memory overflow and increases communication overhead. Option C (adding more instances) adds GPUs but also increases all-reduce traffic, likely worsening the bottleneck.
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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Last reviewed: Jun 23, 2026
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.
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