The answer is to enable automatic mixed precision (AMP) training. This is the correct fix for a CUDA out of memory error in SageMaker because AMP reduces GPU memory usage by storing tensors in half-precision (FP16) where possible, while keeping critical operations in full precision (FP32), effectively cutting memory consumption nearly in half without altering the model architecture or batch size. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of SageMaker’s memory optimization techniques, often appearing as a trap where candidates mistakenly try to reduce batch size or switch to a larger instance—even when the current instance and batch size are already appropriate. The key insight is that AMP directly addresses GPU memory pressure by leveraging mixed precision, which is especially useful on instances like ml.p3.2xlarge with 16 GB GPU memory. Remember the mnemonic: “AMP halves the memory, no need to swap the instance.”
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 company is using Amazon SageMaker to train a deep learning model. The training job is failing with an error 'CUDA out of memory'. The training instance is an ml.p3.2xlarge with 16 GB GPU memory. The model architecture and batch size are appropriate for this instance size. What is the most likely cause of this error?
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
✓
Enable automatic mixed precision (AMP) training to reduce memory usage.
Option C is correct because enabling automatic mixed precision (AMP) training reduces GPU memory usage by storing tensors in half-precision (FP16) where possible, while keeping critical operations in full precision (FP32). This directly addresses the 'CUDA out of memory' error on an ml.p3.2xlarge instance (16 GB GPU memory) without changing the model architecture or batch size, which are already appropriate.
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 number of epochs.
Why it's wrong here
Epochs do not affect per-step memory usage; they affect training time.
✗
Increase the number of GPUs by using a distributed training instance type.
Why it's wrong here
Adding more GPUs may not help if the memory per GPU is the same; the error is per GPU.
✓
Enable automatic mixed precision (AMP) training to reduce memory usage.
Why this is correct
AMP uses FP16 where possible, cutting memory usage roughly in half, which often resolves out-of-memory errors.
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.
✗
Use a smaller instance type to force lower memory usage.
Why it's wrong here
A smaller instance has even less memory, making the problem worse.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may incorrectly assume the solution is to reduce epochs (Option A) or scale out to more GPUs (Option B), when the root cause is memory exhaustion per GPU, which is best addressed by mixed precision training to halve the memory footprint without altering the model or batch size.
Detailed technical explanation
How to think about this question
Automatic mixed precision (AMP) works by using a loss scaling technique to prevent underflow in FP16 gradients, and it leverages NVIDIA's Tensor Cores on Volta and later architectures (including the V100 GPU in ml.p3 instances) for faster FP16 matrix multiplications. In practice, AMP can reduce GPU memory usage by up to 50% for deep learning models, as activations and gradients are stored in FP16, while master weights remain in FP32 for numerical stability. This is a standard optimization in SageMaker's built-in frameworks like PyTorch and TensorFlow, often enabled via the `sagemaker.debugger` or framework-specific APIs.
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
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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Enable automatic mixed precision (AMP) training to reduce memory usage. — Option C is correct because enabling automatic mixed precision (AMP) training reduces GPU memory usage by storing tensors in half-precision (FP16) where possible, while keeping critical operations in full precision (FP32). This directly addresses the 'CUDA out of memory' error on an ml.p3.2xlarge instance (16 GB GPU memory) without changing the model architecture or batch size, which are already appropriate.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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Question Discussion
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