The correct answer is to enable activation checkpointing and reduce the sequence length. Activation checkpointing trades compute for memory by discarding intermediate activations during the forward pass and recomputing them during backpropagation, which drastically lowers peak memory usage. Reducing the sequence length directly cuts the memory footprint of the attention layers, which scale quadratically with sequence length. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of SageMaker’s model parallelism library and common memory optimization strategies. A frequent trap is confusing activation checkpointing with gradient accumulation or pipeline parallelism—remember that checkpointing saves memory at the cost of extra computation, while reducing sequence length is a direct memory lever. Memory tip: “Checkpoint to drop, shorten to stop”—activation checkpointing drops stored activations, and shortening the sequence stops the quadratic memory growth.
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 data scientist is training a large transformer model using SageMaker's model parallelism library. The training job is failing with an out-of-memory (OOM) error. Which two actions can help resolve the OOM error? (Choose two.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Reduce the sequence length
Options C and E are correct. Activation checkpointing (C) trades compute for memory by recomputing activations during backpropagation rather than storing them. Reducing sequence length (E) directly decreases memory usage for attention layers. Option A (decrease pipeline parallelism degree) can increase per-stage memory. Option B (increase batch size) increases memory. Option D (smaller instance) reduces available memory, worsening OOM.
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.
✓
Reduce the sequence length
Why this is correct
Shorter sequences directly reduce memory usage for attention and hidden states.
Related concept
Read the scenario before looking for a memorised answer.
✓
Enable activation checkpointing
Why this is correct
Activation checkpointing reduces memory at the cost of recomputation.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the batch size per GPU
Why it's wrong here
Larger batch sizes increase memory consumption.
✗
Switch to a smaller instance type
Why it's wrong here
Smaller instances have less memory, making OOM more likely.
✗
Decrease the pipeline parallelism degree
Why it's wrong here
Decreasing the pipeline degree puts more layers per stage, increasing memory usage.
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: Reduce the sequence length — Options C and E are correct. Activation checkpointing (C) trades compute for memory by recomputing activations during backpropagation rather than storing them. Reducing sequence length (E) directly decreases memory usage for attention layers. Option A (decrease pipeline parallelism degree) can increase per-stage memory. Option B (increase batch size) increases memory. Option D (smaller instance) reduces available memory, worsening OOM.
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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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
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