The correct choice is to enable mixed precision training because the model’s 50 million parameters in FP32 precision require roughly 200 MB per million parameters, totaling about 10 GB of GPU memory—exceeding the 8 GB available on an ml.p3.2xlarge instance. Mixed precision training uses FP16 for most operations, cutting memory usage nearly in half and fitting within the GPU’s capacity. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of GPU memory budgeting and the trade-offs between precision and resource constraints; a common trap is immediately suggesting a larger instance or reducing batch size, when mixed precision is often the most efficient first step. Remember the memory tip: “FP32 is heavy, FP16 is ready”—if you hit CUDA out of memory errors during mixed precision training, verify that your model and optimizer are actually using FP16 gradients and that loss scaling is enabled to prevent underflow.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. 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 Docker container. The training job fails with an error 'OutOfMemoryError: CUDA out of memory'. The instance type is ml.p3.2xlarge (8 GB GPU memory). The model has 50 million parameters. What is the most likely cause and solution?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The model uses FP32 precision; enable mixed precision training
Option B is correct because 50M parameters likely exceed GPU memory when using full precision. Mixed precision (FP16) reduces memory usage. Option A (batch size) could help but is secondary. Option C (instance type) may be unnecessary if mixed precision works. Option D (checkpointing) doesn't address memory during 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.
✗
The instance type is insufficient; switch to ml.p3.8xlarge
Why it's wrong here
Upgrading instance is costly; mixed precision is a better first step.
✗
The batch size is too large; reduce batch size
Why it's wrong here
Reducing batch size helps but may not be enough; mixed precision is more effective.
✗
Enable gradient checkpointing to reduce memory
Why it's wrong here
Gradient checkpointing trades compute for memory, but mixed precision directly reduces memory footprint.
✓
The model uses FP32 precision; enable mixed precision training
Why this is correct
Mixed precision (FP16) halves memory usage, fitting the model into 8 GB.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The model uses FP32 precision; enable mixed precision training — Option B is correct because 50M parameters likely exceed GPU memory when using full precision. Mixed precision (FP16) reduces memory usage. Option A (batch size) could help but is secondary. Option C (instance type) may be unnecessary if mixed precision works. Option D (checkpointing) doesn't address memory during training.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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