The correct actions are gradient accumulation, mixed precision training, and reducing the batch size. Gradient accumulation works by aggregating gradients over several smaller forward passes before performing a weight update, effectively simulating a larger batch size without increasing peak GPU memory usage. Mixed precision training uses both float16 and float32 data types to halve memory consumption for tensors, while reducing batch size directly lowers the memory footprint per training step. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to diagnose and fix GPU out-of-memory errors in SageMaker, a common scenario when training deep learning models on memory-constrained instances like the ml.p3.2xlarge. A frequent trap is confusing batch size with epochs—increasing epochs does not reduce per-step memory, and increasing batch size only worsens the error. Remember the mnemonic “GBR”: Gradient accumulation, Batch size reduction, and mixed pRecision to quickly recall the three fixes.
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 company uses Amazon SageMaker to train a deep learning model using TensorFlow. The training job is failing with an 'OutOfMemory' error. The instance type is ml.p3.2xlarge with 16 GB GPU memory. The model has 10 million parameters. Which THREE actions should be taken to resolve the memory issue? (Choose THREE.)
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
Options B, C, and E are correct. Gradient accumulation simulates larger batch sizes without increasing memory usage. Mixed precision training reduces memory footprint. Reducing batch size directly decreases memory usage. Option A is wrong because increasing batch size exacerbates memory issue. Option D is wrong because increasing epochs does not affect memory per step.
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 directly reduces memory usage.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of epochs
Why it's wrong here
Number of epochs does not affect per-step memory.
✓
Enable mixed precision training
Why this is correct
Mixed precision reduces memory usage by storing tensors in half-precision.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the batch size
Why it's wrong here
Larger batch size increases memory consumption.
✓
Use gradient accumulation
Why this is correct
Gradient accumulation allows effective larger batch size without increasing memory per step.
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: Reduce the batch size — Options B, C, and E are correct. Gradient accumulation simulates larger batch sizes without increasing memory usage. Mixed precision training reduces memory footprint. Reducing batch size directly decreases memory usage. Option A is wrong because increasing batch size exacerbates memory issue. Option D is wrong because increasing epochs does not affect memory per step.
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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