Hyperparameter Tuning for Linear Learner Algorithm

Answers: B, C, E.

Option A is incorrect.

The correct name of the value you use to pass your JSON object is HyperParameterTuningJobConfig, and the name of the job is CreateHyperParameterTuningJob.

Option B is correct.

To specify the hyperparameter settings for your hyperparameter tuning job, you pass a JSON object as the HyperParameterTuningJobConfig parameter to the job named CreateHyperParameterTuningJob.

Option C is correct.You specify the ranges of the hyperparameters you want to tune in the ParameterRanges section of the HyperParameterTuningJobConfig.

Option D is incorrect.

You specify the ranges of the hyperparameters you want to tune in the ParameterRanges section of the HyperParameterTuningJobConfig, not the limits of the hyperparameters.

Option E is correct.

In the HyperParameterTuningJobObjective section of the HyperParameterTuningJobConfig, you set MetricName to the objective metric for the hyperparameter tuning job.

Option F is incorrect.

There is no MaxSequentialTrainingJobs parameter in the ResourceLimits section of the HyperParameterTuningJobConfig.

Reference:

Please see the Amazon SageMaker developer guide titled Automatic Model Tuning, and the Amazon SageMaker developer guide titled Configure and Launch a Hyperparameter Tuning Job.

To pass the tuning job settings into your hyperparameter tuning job in SageMaker, you would need to follow these steps:

Step 1: Choose the algorithm and set the objective metric for tuning Since the team has already selected the Linear Learner algorithm and decided to use SageMaker's automatic model tuning, the next step would be to define the objective metric for the tuning job. In this case, the objective is set to validation precision. This means that the tuning job will optimize the model's performance based on the precision of its predictions on the validation dataset.

Step 2: Define a JSON object To pass the tuning job settings into your hyperparameter tuning job, you need to define a JSON object that contains the configuration settings for the tuning job. The JSON object can be created using any programming language that supports JSON.

Step 3: Pass the JSON object as a parameter Once the JSON object is defined, you need to pass it as a parameter to the hyperparameter tuning job. This can be done using the HyperParameterTuningJobConfig parameter when creating the tuning job.

Step 4: Specify the hyperparameter ranges and limits Within the JSON object, you need to specify the ranges and limits for the hyperparameters that you want to tune. This can be done using the HyperParameterRanges parameter.

Step 5: Specify the objective metric In the JSON object, you also need to specify the objective metric that you want to optimize for the tuning job. This can be done using the ObjectiveMetric parameter.

Step 6: Specify the resource limits Finally, you may want to specify the resource limits for the tuning job, such as the maximum number of training jobs to run concurrently. This can be done using the ResourceLimits parameter in the JSON object.

Based on the above explanation, the correct options to pass the tuning job settings into your hyperparameter tuning job are:

A. Define a JSON object and pass it as the value of the HyperParameterConfig to the HyperParameterTuningJob. B. Define a JSON object and pass it as the value of the HyperParameterTuningJobConfig to the CreateHyperParameterTuningJob. E. In the JSON object specify the objective metric for the hyperparameter tuning job.

Options C, D, and F are incorrect because they suggest specifying the ranges, limits, and maximum sequential training jobs parameter in the JSON object, respectively, which is not entirely accurate. Option C is partially correct, but it only mentions specifying the ranges of the hyperparameters, whereas the complete JSON object would include additional parameters like the ObjectiveMetric and ResourceLimits.