The answer is to reduce `per_device_train_batch_size` to 4. This hyperparameter change directly lowers the number of samples processed per GPU step, which decreases the memory footprint for activations, gradients, and optimizer states—the primary cause of a CUDA out of memory error during fine-tuning. On the AWS Certified AI Practitioner AIF-C01 exam, this question tests your understanding of how GPU memory constraints interact with training hyperparameters; a common trap is to mistakenly adjust learning rate or number of epochs, which do not free GPU memory. Remember that batch size is the memory lever—cutting it halves the immediate memory demand, while other parameters affect convergence or overfitting, not capacity. A useful memory tip: “Batch size breaks the bank—shrink it first.”
AIF-C01 Applications of Foundation Models Practice Question
This AIF-C01 practice question tests your understanding of applications of foundation models. 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.
Refer to the exhibit. The training job is failing with an error 'CUDA out of memory'. Which hyperparameter change is MOST likely to resolve the issue?
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
✓
Reduce per_device_train_batch_size to 4
The 'CUDA out of memory' error indicates that the GPU memory is exhausted during training. Reducing `per_device_train_batch_size` directly decreases the number of samples processed simultaneously per GPU, which lowers memory consumption for activations, gradients, and optimizer states. This is the most direct and effective hyperparameter change to resolve an out-of-memory condition.
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.
✗
Increase the number of epochs to 10
Why it's wrong here
Increasing epochs does not reduce memory footprint; it increases training time.
✗
Increase learning_rate to 5e-4
Why it's wrong here
Learning rate does not affect memory usage.
✓
Reduce per_device_train_batch_size to 4
Why this is correct
Smaller batch size uses less GPU memory.
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.
✗
Increase max_seq_length to 1024
Why it's wrong here
Longer sequences consume more memory, worsening the OOM error.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that increasing epochs or learning rate can fix resource exhaustion errors, when in fact only adjustments that reduce per-step memory usage (like batch size or sequence length) are effective.
Detailed technical explanation
How to think about this question
GPU memory is primarily consumed by model parameters, optimizer states (e.g., Adam stores two additional values per parameter), and intermediate activations. The batch size directly multiplies the memory required for activations, as each sample in the batch retains its own forward-pass tensors until backpropagation completes. Techniques like gradient accumulation can simulate larger batch sizes without increasing peak memory, but reducing the per-device batch size is the immediate fix for a CUDA OOM error.
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.
Applications of Foundation Models — This question tests Applications of Foundation Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Reduce per_device_train_batch_size to 4 — The 'CUDA out of memory' error indicates that the GPU memory is exhausted during training. Reducing `per_device_train_batch_size` directly decreases the number of samples processed simultaneously per GPU, which lowers memory consumption for activations, gradients, and optimizer states. This is the most direct and effective hyperparameter change to resolve an out-of-memory condition.
What should I do if I get this AIF-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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