AIF-C01 Fundamentals of AI and ML Practice Question
A data scientist is using Amazon SageMaker to train a deep learning model for image classification. The training job is taking too long. The dataset consists of 100,000 images stored in Amazon S3. Which action can the data scientist take to reduce training time without modifying the model architecture?
⚠ Common exam trap
Watch out — candidates often confuse checkpointing (which helps with recovery, not speed) or reducing epochs (which changes training duration but also model performance) with legitimate performance optimizations, while overlooking that GPU acceleration directly addresses the computational bottleneck without altering the model or dataset.
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
✓
Use a GPU instance type for training.
GPU instances are specifically designed for parallel processing of matrix operations, which are fundamental to deep learning training. By switching to a GPU instance type (e.g., p3 or p4d families) in SageMaker, the data scientist can significantly accelerate the training of the image classification model without altering the model architecture, as the dataset of 100,000 images benefits from GPU's massive parallelism for forward and backward passes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convert images to CSV format before training.
Why it's wrong here
CSV is not an efficient format for image data and may increase loading time.
- ✓
Use a GPU instance type for training.
Why this is correct
GPUs are optimized for parallel matrix operations common in deep learning, significantly reducing training time.
- ✗
Enable checkpointing to save intermediate models.
Why it's wrong here
Checkpointing adds storage overhead and does not reduce training time.
- ✗
Reduce the number of training epochs.
Why it's wrong here
Reducing epochs may lead to underfitting and lower accuracy.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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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.