AIF-C01 Fundamentals of AI and ML Practice Question
This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
Exhibit
{
"TrainingJobName": "my-training-job-1",
"TrainingJobStatus": "Failed",
"FailureReason": "AlgorithmError: OutOfMemoryError: CUDA out of memory. Tried to allocate 4.00 GiB (GPU 0; 8.00 GiB total capacity; 3.95 GiB already allocated; 2.50 GiB free; 4.00 GiB reserved in total by PyTorch)"
}
Refer to the exhibit. A data scientist ran a training job on Amazon SageMaker. The job failed with the error shown. What is the most likely cause?
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.
{
"TrainingJobName": "my-training-job-1",
"TrainingJobStatus": "Failed",
"FailureReason": "AlgorithmError: OutOfMemoryError: CUDA out of memory. Tried to allocate 4.00 GiB (GPU 0; 8.00 GiB total capacity; 3.95 GiB already allocated; 2.50 GiB free; 4.00 GiB reserved in total by PyTorch)"
}
A
The S3 input path is incorrect
Why wrong: An incorrect S3 path would result in a 'NoSuchKey' or 'AccessDenied' error.
B
The IAM role does not have permission to access S3
Why wrong: Permission errors would appear as 'AccessDenied' before training starts.
C
The training code has a syntax error
Why wrong: Syntax errors would produce a different error message (e.g., NameError, SyntaxError).
D
The batch size is too large for the instance's GPU memory
The error shows CUDA out of memory, typically due to batch size or model size exceeding GPU memory.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The batch size is too large for the instance's GPU memory
The error message indicates a CUDA out-of-memory error, which occurs when the GPU memory is insufficient for the requested batch size. Option D is correct because increasing the batch size beyond the GPU's memory capacity causes the training job to fail with this specific error.
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 S3 input path is incorrect
Why it's wrong here
An incorrect S3 path would result in a 'NoSuchKey' or 'AccessDenied' error.
✗
The IAM role does not have permission to access S3
Why it's wrong here
Permission errors would appear as 'AccessDenied' before training starts.
✗
The training code has a syntax error
Why it's wrong here
Syntax errors would produce a different error message (e.g., NameError, SyntaxError).
✓
The batch size is too large for the instance's GPU memory
Why this is correct
The error shows CUDA out of memory, typically due to batch size or model size exceeding 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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between infrastructure errors (S3, IAM) and runtime errors (CUDA memory), where candidates mistakenly attribute a GPU memory error to a misconfiguration in data access or code syntax.
Detailed technical explanation
How to think about this question
CUDA out-of-memory errors occur when the batch size multiplied by the per-sample memory footprint (including activations, gradients, and optimizer states) exceeds the GPU's VRAM. SageMaker training jobs log these errors in CloudWatch, and the solution involves reducing the batch size, using gradient accumulation, or switching to a larger instance type like ml.p3.16xlarge with 16 GB GPU memory.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
Fundamentals of AI and ML — This question tests Fundamentals of AI and ML — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The batch size is too large for the instance's GPU memory — The error message indicates a CUDA out-of-memory error, which occurs when the GPU memory is insufficient for the requested batch size. Option D is correct because increasing the batch size beyond the GPU's memory capacity causes the training job to fail with this specific error.
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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