MLS-C01 Question #367: Real Exam Question with Answer & Explanation

Question

A tourism company uses a machine learning (ML) model to make recommendations to customers. The company uses an Amazon SageMaker environment and set hyperparameter tuning completion criteria to MaxNumberOfTrainingJobs. An ML specialist wants to change the hyperparameter tuning completion criteria. The ML specialist wants to stop tuning immediately after an internal algorithm determines that tuning job is unlikely to improve more than 1% over the objective metric from the best training job. Which completion criteria will meet this requirement?

Options

• AMaxRuntimeInSeconds
• BTargetObjectiveMetricValue
• CCompleteOnConvergence
• DMaxNumberOfTrainingJobsNotImproving

Explanation

To stop a SageMaker hyperparameter tuning job when an internal algorithm determines that further improvement over the objective metric is unlikely (e.g., less than 1%), the ML specialist should set the completion criteria to CompleteOnConvergence.

Common mistakes.

• A. MaxRuntimeInSeconds stops the tuning job after a specified maximum time duration, irrespective of the improvement or convergence of the objective metric.
• B. TargetObjectiveMetricValue stops the tuning job when a predefined absolute target value for the objective metric is reached, not when the rate of improvement becomes negligible.
• D. MaxNumberOfTrainingJobsNotImproving stops the tuning job after a specified number of consecutive training jobs fail to improve the objective metric, which is a simpler rule than the sophisticated convergence detection described.

Concept tested. SageMaker Hyperparameter Tuning completion criteria

Reference. https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-stopping-criteria.html#automatic-model-tuning-complete-on-convergence

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