- A
Increase the learning rate to converge faster
Why wrong: Learning rate adjustments do not address memory constraints and may harm convergence.
- B
Enable gradient accumulation in the training script
Gradient accumulation reduces per-step memory by splitting the batch into micro-batches, allowing training on limited GPU memory.
- C
Switch to a CPU-based instance
Why wrong: CPU instances would be significantly slower and unlikely to solve memory issues for large models.
- D
Reduce the number of attention heads in the model
Why wrong: This modifies the model architecture, which the team wants to avoid.
Question 762 of 1,000
AI0-001 AI Infrastructure and Technologies Practice Question
This AI0-001 practice question tests your understanding of ai infrastructure and technologies. 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 AWS SageMaker to train a large language model. The training job fails with an out-of-memory error. The team is already using the largest available GPU instance. Which step should the team take to resolve the issue without modifying the model architecture?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable gradient accumulation in the training script
Gradient accumulation allows the model to simulate a larger batch size by accumulating gradients over several forward/backward passes before performing an optimizer step. This reduces per-step memory usage because the gradients are stored and averaged rather than requiring the entire batch to be loaded into GPU memory at once. Since the team cannot change the instance type or model architecture, enabling gradient accumulation is the correct approach to resolve the out-of-memory 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.
- ✗
Increase the learning rate to converge faster
Why it's wrong here
Learning rate adjustments do not address memory constraints and may harm convergence.
- ✓
Enable gradient accumulation in the training script
Why this is correct
Gradient accumulation reduces per-step memory by splitting the batch into micro-batches, allowing training on limited GPU memory.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Switch to a CPU-based instance
Why it's wrong here
CPU instances would be significantly slower and unlikely to solve memory issues for large models.
- ✗
Reduce the number of attention heads in the model
Why it's wrong here
This modifies the model architecture, which the team wants to avoid.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that memory issues can be solved by adjusting hyperparameters like learning rate or by switching to a less powerful instance, rather than recognizing that gradient accumulation is a standard technique to fit large models into limited GPU memory without altering the architecture.
Detailed technical explanation
How to think about this question
Gradient accumulation works by performing multiple forward and backward passes with micro-batches, accumulating gradients in the model's parameter gradients buffer, and only applying the optimizer update after a specified number of accumulation steps. This effectively increases the batch size without increasing peak GPU memory usage, as each micro-batch is processed sequentially and the gradients are summed. In practice, the accumulation steps must be tuned to match the desired effective batch size, and the learning rate may need to be scaled linearly with the effective batch size to maintain training dynamics.
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 practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
Visual reference
What to study next
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Infrastructure and Technologies — This question tests AI Infrastructure and Technologies — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable gradient accumulation in the training script — Gradient accumulation allows the model to simulate a larger batch size by accumulating gradients over several forward/backward passes before performing an optimizer step. This reduces per-step memory usage because the gradients are stored and averaged rather than requiring the entire batch to be loaded into GPU memory at once. Since the team cannot change the instance type or model architecture, enabling gradient accumulation is the correct approach to resolve the out-of-memory error.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.
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