DP-100 Day 5_6

What type of object should you use to pass data between pipeline steps?

• Datastore

• Dataset

• OutputFileDatasetConfig

You plan to use the Schedule. create method to create a schedule for a published pipeline. What kind of object must you create first to configure how frequently the pipeline runs?

• ScheduleRecurrance

• Datastore

• PipelineParameter

You need to create an environment from a Conda configuration (.yml) file. Which method of the Environment class should you use?

• create

• create_from_conda_specification

• create_from_existing_conda_environment

You need to run a training script on compute that scales on-demand from 0 to 3 GPU-based nodes. Which kind of compute target should you create?

• Compute Instance

• Compute Cluster

• Inference Cluster

Which ScriptRunConfig parameter causes the script to run on a compute cluster named train-cluster?

• arguments=['--AmlCluster', 'train-cluster']

• environment='train-cluster'

• compute_target='train-cluster'

You are creating a pipeline that includes two steps.

Step 1 preprocesses some data, and step 2 uses the preprocessed data to train a model.

What type of object should you use to pass data from step 1 to step 2 and create a dependency between these steps?

• Datastore

• PipelineData

• Data Reference

You have used the Python SDK for Azure Machine Learning to create a pipeline that trains a model.

What do you need to do so that a client application can invoke the pipeline through an HTTP REST endpoint?

• Rename the pipeline to pipeline_name-production.

• Publish the pipeline.

• Create an inference cluster compute target.

You want to create an explainer that applies the most appropriate SHAP model explanation algorithm based on the type of model.

What kind of explainer should you create?

• Mimic

• Tabular

• Permutation Feature Importance

You want to include model explanations in the logged details of your training experiment.

What must you do in your training script?

• Use the Run.log_table method to log feature importance for each feature.

• Use the ExplanationClient.upload_model_explanation method to upload the explanation created by an Explainer.

• Save the the explanation created by an Explainer in the ./outputs folder.

You have published a pipeline as a real-time service on an Azure Kubernetes Services (AKS) cluster.

An application developer plans to call the service from a REST-based client.

What information does the application developer require?

• The name of the inference pipeline in designer.

• The endpoint URL and key for the published service.

• The name of the AKS compute target in the workspace.