MLS-C01 Data Engineering Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✓

  Define the objective metric, then define the hyperparameter search space, then configure resource limits, then create the tuning job, then monitor and select the best model.

  Why this is correct

  This order follows the standard SageMaker Automatic Model Tuning workflow: first define the metric to optimize, then specify the hyperparameter ranges, then set limits on jobs, then launch the tuning job, and finally review results to pick the best model.

• ✗

  Define the hyperparameter search space, then configure resource limits, then define the objective metric, then create the tuning job, then monitor and select the best model.

  Why it's wrong here

  This order is incorrect because the objective metric must be defined before setting resource limits and creating the tuning job, as it determines how the tuning evaluates trials.

• ✗

  Select the best model, then define the objective metric, then define the hyperparameter search space, then configure resource limits, then create the tuning job.

  Why it's wrong here

  This order is incorrect because selecting the best model occurs after the tuning job completes, not before any configuration. You cannot select a model before defining the search space and running trials.

• ✗

  Create the tuning job, then define the objective metric, then define the hyperparameter search space, then configure resource limits, then monitor and select the best model.

  Why it's wrong here

  This order is incorrect because you cannot create a tuning job without first defining the objective metric, hyperparameter ranges, and resource limits. The job creation requires these as input parameters.

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