Practice Free DP-100 Exam Online Questions

Explanation:

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.

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

Explanation:

Step 1: Implement a K-Means Clustering model

Step 2: Use the cluster as a feature in a Decision jungle model.

Decision jungles are non-parametric models, which can represent non-linear decision boundaries.

Step 3: Use the raw score as a feature in a Score Matchbox Recommender model

The goal of creating a recommendation system is to recommend one or more "items" to "users" of the system. Examples of an item could be a movie, restaurant, book, or song. A user could be a person, group of persons, or other entity with item preferences.

Scenario:

Ad response rated declined.

Ad response models must be trained at the beginning of each event and applied during the sporting event.

Market segmentation models must optimize for similar ad response history.

Ad response models must support non-linear boundaries of features.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/multiclass-decision-jungle

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/score-matchbox-recommender

Explanation:

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

Explanation:

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