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
Random search samples hyperparameter combinations without using prior results, so it explores inefficiently and may miss the optimum within a fixed budget. It is correct for cheap, high-dimensional or poorly understood search spaces. Bayesian optimisation, which models prior evaluations to guide the next trial, is best here.
- ✗
Grid search
Why it's wrong here
Grid search evaluates every combination on a fixed discrete grid, so continuous hyperparameters such as learning rate are quantised and cost scales multiplicatively with each added dimension. It suits small, low-dimensional spaces where exhaustive coverage is affordable; SageMaker's Bayesian strategy instead models prior evaluations to target promising regions.
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Exhaustive search
Why it's wrong here
Exhaustive search enumerates the entire specified space, so runtime grows exponentially with the number of hyperparameters and it cannot exploit information from completed trials. It fits tiny search spaces with few dimensions; SageMaker's Bayesian tuning uses a regressor to pick each next configuration from prior results.
- ✓
Bayesian optimization
Why this is correct
Bayesian optimisation builds a probabilistic surrogate model of the objective function, then uses an acquisition function to pick each next hyperparameter combination, converging on strong configurations in far fewer training jobs than random or grid search. This directly satisfies the stem's requirement for efficient automatic model tuning on SageMaker.
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