- A
Reduce the sequence length
Shorter sequences directly reduce memory usage for attention and hidden states.
- B
Enable activation checkpointing
Activation checkpointing reduces memory at the cost of recomputation.
- C
Increase the batch size per GPU
Why wrong: Larger batch sizes increase memory consumption.
- D
Switch to a smaller instance type
Why wrong: Smaller instances have less memory, making OOM more likely.
- E
Decrease the pipeline parallelism degree
Why wrong: Decreasing the pipeline degree puts more layers per stage, increasing memory usage.
Question 288 of 1,000
hardMultiple SelectObjective-mapped
MLA-C01 Practice Question: A data scientist is training a large transformer…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 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
✓
Reduce the sequence length
Reducing the sequence length decreases the memory footprint of the attention mechanism, which scales quadratically with sequence length in transformer models. This directly reduces the peak memory usage per GPU, helping to avoid out-of-memory errors during training with SageMaker's model parallelism.
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
The trap here is that candidates may confuse pipeline parallelism with tensor parallelism, assuming decreasing pipeline degree reduces memory, when in fact it increases per-GPU memory load due to fewer stages.
Detailed technical explanation
How to think about this question
Activation checkpointing (option B) trades compute for memory by not storing intermediate activations for all layers during forward pass; instead, they are recomputed during backward pass, reducing peak memory usage. In SageMaker's model parallelism, this is particularly effective because it reduces the memory bottleneck in the forward pass, allowing larger models to fit on available GPUs without increasing compute cost proportionally.
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.
TExam Day Tips
- 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.
Visual reference
What to study next
Got this wrong? Here's your next step.
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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: Reduce the sequence length — Reducing the sequence length decreases the memory footprint of the attention mechanism, which scales quadratically with sequence length in transformer models. This directly reduces the peak memory usage per GPU, helping to avoid out-of-memory errors during training with SageMaker's model parallelism.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 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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