AIF-C01 Applications of Foundation Models Practice Question
Exhibit
Refer to the exhibit.
SageMaker Training Job Configuration:
{
"AlgorithmSpecification": {
"TrainingImage": "763104351884.dkr.ecr.us-west-2.amazonaws.com/huggingface-pytorch-training:1.13.1-transformers4.26.0-gpu-py39-cu117-ubuntu20.04",
"TrainingInputMode": "File"
},
"HyperParameters": {
"epochs": "3",
"per_device_train_batch_size": "8",
"learning_rate": "2e-5",
"max_seq_length": "512"
},
"InputDataConfig": [
{
"ChannelName": "train",
"DataSource": {
"S3DataSource": {
"S3DataType": "S3Prefix",
"S3Uri": "s3://my-bucket/train/"
}
},
"ContentType": "text/csv"
}
],
"OutputDataConfig": {
"S3OutputPath": "s3://my-bucket/output/"
},
"ResourceConfig": {
"InstanceType": "ml.p3.2xlarge",
"InstanceCount": 1,
"VolumeSizeInGB": 50
},
"RoleArn": "arn:aws:iam::123456789012:role/SageMakerRole",
"StoppingCondition": {
"MaxRuntimeInSeconds": 86400
}
}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?
⚠ Common exam trap
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.
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
✓
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.
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.
- ✗
Increase max_seq_length to 1024
Why it's wrong here
Longer sequences consume more memory, worsening the OOM error.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.