DP-100 Exam Questions & Answers

Correct Answer:

Step 1: Add a Two-Class Support Vector Machine module to initialize the SVM classifier.

Step 2: Add a dataset to the experiment

Step 3: Add a Split Data module to create training and test dataset.

To generate a set of feature scores requires that you have an already trained model, as well as a test dataset.

Step 4: Add a Permutation Feature Importance module and connect to the trained model and test dataset.

Step 5: Set the Metric for measuring performance property to Classification - Accuracy and then run the experiment.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-support-vector-machine

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature-importance

Correct Answer:

Step 1: Install Docker on the workstation

Prerequisites include having a working Docker installation on your local system.

Build or download the dockerfile to the compute node.

Step 2: Create an AksWebservice deployment configuration and deploy the model to it

To deploy a model to Azure Kubernetes Service, create a deployment configuration that describes the compute resources needed.

# 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)

service.wait_for_deployment(show_output = True)

print(service.state)

print(service.get_logs())

Step 3: Create a LocalWebservice deployment configuration for the service and deploy the model to it

To deploy locally, modify your code to use LocalWebservice.deploy_configuration() to create a deployment configuration. Then use Model.deploy() to deploy the service.

Step 4: Debug and modify the scoring script as necessary. Use the reload() method of the service after each modification.

During local testing, you may need to update the score.py file to add logging or attempt to resolve any problems that you've discovered. To reload changes to the score.py file, use reload(). For example, the following code reloads the script for

the service, and then sends data to it.

Incorrect Answers:

AciWebservice: The types of web services that can be deployed are LocalWebservice, which will deploy a model locally, and AciWebservice and AksWebservice, which will deploy a model to Azure Container Instances (ACI) and Azure

Kubernetes Service (AKS), respectively.

Reference:

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

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

Correct Answer: C

An Azure Logic App allows for more complex triggering logic or behavior.

To use an Azure Logic App to trigger a Machine Learning pipeline, you'll need the REST endpoint for a published Machine Learning pipeline. Create and publish your pipeline. Then find the REST endpoint of your PublishedPipeline by using

the pipeline ID.

# You can find the pipeline ID in Azure Machine Learning studio

published_pipeline = PublishedPipeline.get(ws, id="")

published_pipeline.endpoint

Note: Azure Logic Apps is a cloud platform where you can create and run automated workflows with little to no code. By using the visual designer and selecting from prebuilt operations, you can quickly build a workflow that integrates and

manages your apps, data, services, and systems.

Azure Logic Apps simplifies the way that you connect legacy, modern, and cutting-edge systems across cloud, on premises, and hybrid environments and provides low-code-no-code tools for you to develop highly scalable integration

solutions for your enterprise and business-to-business (B2B) scenarios.

Reference: https://learn.microsoft.com/en-us/azure/machine-learning/v1/how-to-trigger-published-pipeline

https://learn.microsoft.com/en-us/azure/logic-apps/logic-apps-overview