The answer is to increase the instance type to ml.p3.16xlarge. The OutOfMemoryError occurs because the ml.p3.2xlarge instance provides only 16 GB of GPU memory, which is insufficient to hold both the 2 GB model and the 50 GB dataset during training, especially with standard data loading that keeps the entire dataset in memory. By scaling up to the ml.p3.16xlarge, you gain 64 GB of GPU memory, directly resolving the resource constraint. On the AWS Certified AI Practitioner AIF-C01 exam, this scenario tests your understanding of SageMaker instance sizing and memory bottlenecks—a common trap is to assume a larger dataset always requires distributed training, but here a single larger instance is the simpler fix. Remember the memory tip: “Model plus dataset must fit in GPU RAM; if not, scale up, not out.”
AIF-C01 Fundamentals of AI and ML Practice Question
This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. 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 training a large language model using Amazon SageMaker. The training job fails with the error 'OutOfMemory'. They are using a single ml.p3.2xlarge instance. The dataset is 50GB and the model is 2GB. The training script uses standard data loading. Which action should they take to resolve the issue?
The error indicates the instance memory is insufficient. Upgrading to a larger instance directly addresses the out-of-memory issue.
B
Train the model using Spot instances
Why wrong: Spot instances do not increase memory; they only reduce cost.
C
Reduce the batch size
Why wrong: Reducing batch size can lower memory usage per step, but if the dataset loading loads entire data into memory, the error persists. The dataset size (50GB) is likely the cause.
D
Use SageMaker's Pipe mode for data loading
Why wrong: Pipe mode streams data from S3, reducing memory footprint for dataset, but the model (2GB) plus pipeline may still cause OOM. The direct fix is larger instance.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the instance type to ml.p3.16xlarge
The error 'OutOfMemory' indicates that the ml.p3.2xlarge instance (with 16 GB GPU memory) cannot hold both the 2 GB model and the 50 GB dataset during training. Increasing the instance type to ml.p3.16xlarge provides 64 GB GPU memory, which is sufficient to accommodate the model and dataset without memory pressure. This directly resolves the resource constraint.
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 instance type to ml.p3.16xlarge
Why this is correct
The error indicates the instance memory is insufficient. Upgrading to a larger instance directly addresses the out-of-memory issue.
Related concept
Read the scenario before looking for a memorised answer.
✗
Train the model using Spot instances
Why it's wrong here
Spot instances do not increase memory; they only reduce cost.
✗
Reduce the batch size
Why it's wrong here
Reducing batch size can lower memory usage per step, but if the dataset loading loads entire data into memory, the error persists. The dataset size (50GB) is likely the cause.
✗
Use SageMaker's Pipe mode for data loading
Why it's wrong here
Pipe mode streams data from S3, reducing memory footprint for dataset, but the model (2GB) plus pipeline may still cause OOM. The direct fix is larger instance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that reducing batch size or using Pipe mode can solve out-of-memory errors caused by insufficient GPU memory, when the real fix is to use a larger instance with more GPU memory.
Detailed technical explanation
How to think about this question
The ml.p3.2xlarge instance has 16 GB of GPU memory (NVIDIA V100), while the ml.p3.16xlarge has 64 GB. In deep learning, the model weights, optimizer states, and activations all reside in GPU memory; with a 2 GB model, typical memory overhead from gradients and optimizer states can double or triple that, leaving insufficient room for even a single batch of a 50 GB dataset. Pipe mode is designed for datasets that exceed local disk capacity, not for GPU memory constraints—it streams data to the instance's local disk or memory, but the GPU memory bottleneck remains.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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: Increase the instance type to ml.p3.16xlarge — The error 'OutOfMemory' indicates that the ml.p3.2xlarge instance (with 16 GB GPU memory) cannot hold both the 2 GB model and the 50 GB dataset during training. Increasing the instance type to ml.p3.16xlarge provides 64 GB GPU memory, which is sufficient to accommodate the model and dataset without memory pressure. This directly resolves the resource constraint.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
This AIF-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 AIF-C01 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.