AIF-C01 Applications of Foundation Models Practice Question
Which TWO techniques can reduce the cost of running a fine-tuned foundation model on Amazon SageMaker? (Choose TWO.)
⚠ Common exam trap
AWS often tests the distinction between techniques that reduce inference cost (pruning, quantization) versus those that improve training speed or accuracy, leading candidates to mistakenly select options that increase resource usage or are irrelevant to inference cost.
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
✓
Implement structured pruning to remove less important model parameters.
Structured pruning reduces the number of parameters in the model by removing entire neurons, channels, or layers that contribute little to the output. This directly shrinks the model size and computational requirements, leading to lower memory usage and faster inference on SageMaker, which reduces cost.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement structured pruning to remove less important model parameters.
Why this is correct
Pruning creates a smaller model that is cheaper to run.
- ✗
Use larger instance types with more GPUs to speed up inference.
Why it's wrong here
Larger instances are more expensive; they may reduce latency but not cost.
- ✓
Apply model quantization to reduce precision from FP32 to FP16 or INT8.
Why this is correct
Lower precision reduces memory usage and speeds up inference, lowering cost.
- ✗
Store the model parameters in FP32 to maintain accuracy during inference.
Why it's wrong here
FP32 uses more memory and compute, increasing cost without significant accuracy benefit.
- ✗
Increase the number of training epochs to achieve higher accuracy.
Why it's wrong here
More epochs increase training cost and do not reduce inference cost.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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