Question 12

Explanation

Correct options:

Provide the Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf

You must provide the Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that SageMaker can successfully complete model training. You can use the mandatory RoleArn parameter to configure this option.

Specify the resources, ML compute instances, and ML storage volumes to deploy for model training

You need to specify the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance. ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want SageMaker to use the ML storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1. You can use the mandatory ResourceConfig parameter to configure this option.

Specify the output path on an Amazon S3 bucket where the trained model will persist

Specifies the path to the S3 location where you want to store model artifacts. SageMaker creates subfolders for the artifacts. You can use the mandatory OutputDataConfig parameter to configure this option.

Training a model with SageMaker:

Incorrect options:

Identify the training dataset and the Amazon S3, EFS, or FSx location where it is stored

Specify the validation channel identifying the location of validation data on an Amazon S3 bucket

Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, training_data and validation_data. The configuration for each channel provides the S3, EFS, or FSx location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format. SageMaker uses the InputDataConfig parameter to Identify the training dataset and the Amazon S3, EFS, or FSx location where it is stored. This parameter is not mandatory, so both these options are incorrect.

Specify the hyperparameters in a JSON array as documented for the algorithm - Hyperparameters are the algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. SageMaker uses the HyperParameters parameter for this option and it is not mandatory.

References:

https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html

https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-mkt-algo-train.html