Microsoft DP-100 Practice Test - Questions Answers, Page 3

Box 1: ds-workstation notebook VM

An authenticated connection must not be required for testing.

On a Microsoft Azure virtual machine (VM), including a Data Science Virtual Machine (DSVM), you create local user accounts while provisioning the VM. Users then authenticate to the VM by using these credentials.

Box 2: gpu-compute cluster

Image classification is well suited for GPU compute clusters

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-common-identity

https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/ai/training-deep-learning

Box 1: from azureml.core.webservice import Webservice

The following code shows how to use the SDK to update the model, environment, and entry script for a web service to Azure Container Instances:

from azureml.core import Environment

from azureml.core.webservice import Webservice

from azureml.core.model import Model, InferenceConfig

Box 2: predictions = service.run(input_json)

Example: The following code demonstrates sending data to the service:

import json

test_sample = json.dumps({'data': [

[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],

[10, 9, 8, 7, 6, 5, 4, 3, 2, 1]

]})

test_sample = bytes(test_sample, encoding='utf8')

prediction = service.run(input_data=test_sample)

print(prediction)

Reference:

https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/how-to-deploy-azure-container-instance

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment

Box 1: AksCompute

Example:

aks_target = AksCompute(ws,"myaks")

# If deploying to a cluster configured for dev/test, ensure that it was created with enough

# cores and memory to handle this deployment configuration. Note that memory is also used by

# things such as dependencies and AML components.

deployment_config = AksWebservice.deploy_configuration(cpu_cores = 1, memory_gb = 1)

service = Model.deploy(ws, "myservice", [model], inference_config, deployment_config, aks_target)

Box 2: AksWebservice

Box 3: token_auth_enabled=Yes

Whether or not token auth is enabled for the Webservice.

Note: A Service principal defined in Azure Active Directory (Azure AD) can act as a principal on which authentication and authorization policies can be enforced in Azure Databricks.

The Azure Active Directory Authentication Library (ADAL) can be used to programmatically get an Azure AD access token for a user.

Incorrect Answers:

auth_enabled (bool): Whether or not to enable key auth for this Webservice. Defaults to True.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-azure-kubernetes-service

https://docs.microsoft.com/en-us/azure/databricks/dev-tools/api/latest/aad/service-prin-aad-token

Box 1: init()

The entry script has only two required functions, init() and run(data). These functions are used to initialize the service at startup and run the model using request data passed in by a client. The rest of the script handles loading and running the model(s).

Box 2: run()

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-existing-model

By default, the Web Service Input will expect the same data schema as the module output data which connects to the same downstream port as it. You can remove the target variable column in the inference pipeline using Select Columns in Dataset module. Make sure that the output of Select Columns in Dataset removing target variable column is connected to the same port as the output of the Web Service Intput module.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-deploy

You need to create an inferencing cluster.

Deploy the real-time endpoint

After your AKS service has finished provisioning, return to the real-time inferencing pipeline to complete deployment.

1. Select Deploy above the canvas.

2. Select Deploy new real-time endpoint.

3. Select the AKS cluster you created.

4. Select Deploy.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-deploy

Connect your Azure Databricks and Azure Machine Learning workspaces:

Linking your ADB workspace to your Azure Machine Learning workspace enables you to track your experiment data in the Azure Machine Learning workspace.

To link your ADB workspace to a new or existing Azure Machine Learning workspace

1. Sign in to Azure portal.

2. Navigate to your ADB workspace's Overview page.

3. Select the Link Azure Machine Learning workspace button on the bottom right.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-azure-databricks

Box 1: create_from_datasets

The create_from_datasets method creates a new DataDriftDetector object from a baseline tabular dataset and a target time series dataset.

Box 2: backfill

The backfill method runs a backfill job over a given specified start and end date.

Syntax: backfill(start_date, end_date, compute_target=None, create_compute_target=False)

Incorrect Answers:

List and update do not have datetime parameters.

Reference:

https://docs.microsoft.com/en-us/python/api/azureml-datadrift/azureml.datadrift.datadriftdetector(class)

azureml.core.Model.properties:

Dictionary of key value properties for the Model. These properties cannot be changed after registration, however new key value pairs can be added.

Reference:

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model