AIF-C01 Fundamentals of AI and ML Practice Question
Which TWO factors should be considered when choosing between a CPU-based instance and a GPU-based instance for training a machine learning model on Amazon SageMaker? (Choose two.)
⚠ Common exam trap
The AIF-C01 exam often tests the misconception that model architecture alone (e.g., number of layers) dictates hardware choice, when in fact the dataset size and model type (e.g., CNN vs. linear regression) are the key factors that determine whether GPU parallelism provides a meaningful advantage.
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
✓
The size of the dataset
The size of the dataset directly impacts whether a GPU's parallel processing capabilities are beneficial. GPU instances excel at performing many matrix operations simultaneously, which is critical for large datasets where mini-batch gradient descent can be parallelized. For smaller datasets, the overhead of transferring data to GPU memory may negate the performance gains, making CPU instances more cost-effective.
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 number of layers in the model
Why it's wrong here
Number of layers is less important than the type of operations.
- ✗
The AWS Region
Why it's wrong here
Region does not affect CPU vs GPU decision.
- ✓
The size of the dataset
Why this is correct
Large datasets can leverage GPU parallelism.
- ✗
The choice of hyperparameter optimizer
Why it's wrong here
Optimizer choice is independent of hardware.
- ✓
The type of model architecture (e.g., CNN vs. linear regression)
Why this is correct
CNNs and deep learning models benefit from GPUs.
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