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MLA-C01 Practice Question: An ML engineer is using Amazon SageMaker…
An ML engineer is using Amazon SageMaker Automatic Model Tuning (AMT) to optimize hyperparameters for a gradient boosting model. The tuning job is taking a long time and has completed many training jobs. The engineer wants to stop training jobs that are unlikely to improve the objective metric. What should they configure?
⚠ Common exam trap
Many exam-takers confuse early stopping (which stops individual training jobs) with reducing the search space or changing the search strategy, which only affect the overall tuning job configuration without addressing the need to terminate underperforming trials mid-execution.
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
✓
Enable early stopping in the hyperparameter tuning job
Enabling early stopping in the Amazon SageMaker Automatic Model Tuning (AMT) job allows the tuning job to automatically stop training jobs that are unlikely to improve the objective metric based on intermediate results. This reduces the total time and compute cost by terminating poorly performing trials early, which directly addresses the engineer's goal of stopping unpromising training jobs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the number of hyperparameter ranges
Why it's wrong here
Narrowing hyperparameter ranges restricts the search space but does not stop running jobs that are unlikely to improve the objective. It is tempting because a tighter range reduces wasted trials, and it would be correct when prior knowledge already bounds sensible values before the tuning job starts.
- ✗
Use a random search strategy instead of Bayesian
Why it's wrong here
Random search explores hyperparameters without modelling past results, so it cannot identify and terminate unpromising trials. Early stopping is the mechanism that halts poor-performing jobs mid-training. Random search suits very high-dimensional spaces where Bayesian overhead outweighs gains, not the goal of cutting wasted compute.
- ✗
Increase the maximum number of training jobs
Why it's wrong here
Raising the maximum number of training jobs lets more jobs run, worsening the runtime problem rather than halting unpromising ones. It is tempting because a larger budget can improve the final objective metric, and it would be correct when tuning converges too early with too few candidates explored.
- ✓
Enable early stopping in the hyperparameter tuning job
Why this is correct
Early stopping in automatic model tuning halts underperforming trials once their objective metric cannot beat the best completed trial, freeing capacity for promising configurations. This directly addresses the long-running tuning job by terminating trials unlikely to improve the objective.
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