AIF-C01 Fundamentals of AI and ML Practice Question
A data scientist wants to perform automatic model tuning (hyperparameter optimization) on SageMaker. They need to find the best hyperparameters for a gradient boosting model. Which strategy is BEST for this task?
⚠ Common exam trap
The AIF-C01 exam often tests the misconception that exhaustive or grid search is the most thorough and therefore best approach, but the trap is that they ignore the practical constraints of compute cost and time, making Bayesian optimization the superior choice for automatic model tuning in SageMaker.
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
✓
Bayesian optimization
Bayesian optimization is the best strategy for automatic model tuning on SageMaker because it builds a probabilistic model of the objective function and uses it to select the most promising hyperparameters to evaluate next. This approach is far more sample-efficient than random or grid search, making it ideal for expensive-to-evaluate models like gradient boosting, where each training run consumes significant time and compute resources.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Random search
Why it's wrong here
Better than grid but still less efficient than Bayesian optimization.
- ✗
Grid search
Why it's wrong here
Exhaustive but computationally expensive; not the best for efficiency.
- ✗
Exhaustive search
Why it's wrong here
Same as grid search, inefficient.
- ✓
Bayesian optimization
Why this is correct
Uses a probabilistic model to select hyperparameters, achieving better results with fewer iterations.
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