The answer is to reduce the batch size. This directly resolves the out of memory error during deep learning training on SageMaker because each training step loads a fixed number of samples into GPU memory; halving the batch size roughly halves the memory required for activations and gradients, fitting within the 16 GB GPU limit of the ml.p3.2xlarge instance. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of GPU memory bottlenecks versus system memory—a common trap is confusing gradient accumulation (which simulates larger batches without reducing per-step memory) with actual batch size reduction. Since mixed precision is already enabled, the simplest fix is lowering the batch size, not increasing the instance type or adding model parallelism. Memory tip: think of the batch size as the number of plates on a tray—fewer plates means less weight per trip, avoiding a crash.
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 deep learning model on Amazon SageMaker using a custom TensorFlow container. The training job fails with an OutOfMemory error. The instance type is ml.p3.2xlarge with 16 GB GPU memory and 61 GB system memory. The model uses mixed precision training. Which step should the data scientist take to resolve the issue without changing the instance type?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Reduce the batch size
Option D is correct because reducing the batch size lowers memory usage during training. Option A (increase instance type) increases cost and may not be necessary. Option B (use gradient accumulation) simulates larger batch sizes without increasing memory footprint, but does not reduce memory usage per step. Option C (enable automatic mixed precision) is already in use. Option E (use model parallelism) is complex and may not be applicable for a single model fitting in memory with batch size reduction.
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 batch size
Why this is correct
Smaller batch size reduces memory usage.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use gradient accumulation to simulate a larger batch size
Why it's wrong here
Gradient accumulation does not reduce memory per step.
✗
Use model parallelism across multiple GPUs
Why it's wrong here
Model parallelism is more complex and may not be needed.
✗
Enable automatic mixed precision (AMP)
Why it's wrong here
AMP is already in use.
✗
Increase the instance type to ml.p3.8xlarge
Why it's wrong here
This increases cost and is not the only solution.
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 MLS-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.
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Reduce the batch size — Option D is correct because reducing the batch size lowers memory usage during training. Option A (increase instance type) increases cost and may not be necessary. Option B (use gradient accumulation) simulates larger batch sizes without increasing memory footprint, but does not reduce memory usage per step. Option C (enable automatic mixed precision) is already in use. Option E (use model parallelism) is complex and may not be applicable for a single model fitting in memory with batch size reduction.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-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 MLS-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 MLS-C01 exam.
Question Discussion
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