DP-100 Microsoft Exam Questions and Free Practice Test

Question 67

- (Exam Topic 3)
You use an Azure Machine Learning workspace.
You have a trained model that must be deployed as a web service. Users must authenticate by using Azure Active Directory.
What should you do?

A. Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the token_auth_enabled parameter of the target configuration object to true

B. Deploy the model to Azure Container Instance

C. During deployment, set the auch_enabled parameter of the target configuration object to true

D. Deploy the model to Azure Container Instance

E. During deployment, set the coken_auch_enabled parameter of the target configuration object to true

F. Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the auc

G. enabled parameter of the target configuration object to true

Correct Answer:A
To control token authentication, use the token_auth_enabled parameter when you create or update a deployment
Token authentication is disabled by default when you deploy to Azure Kubernetes Service.
Note: The model deployments created by Azure Machine Learning can be configured to use one of two authentication methods:
key-based: A static key is used to authenticate to the web service.
token-based: A temporary token must be obtained from the Azure Machine Learning workspace (using Azure Active Directory) and used to authenticate to the web service.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-authenticate-web-service

Question 68

- (Exam Topic 3)
You create a multi-class image classification deep learning model that uses the PyTorch deep learning framework.
You must configure Azure Machine Learning Hyperdrive to optimize the hyperparameters for the classification model.
You need to define a primary metric to determine the hyperparameter values that result in the model with the best accuracy score.
Which three actions must you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. Set the primary_metric_goal of the estimator used to run the bird_classifier_train.py script to maximize.

B. Add code to the bird_classifier_train.py script to calculate the validation loss of the model and log it as a float value with the key loss.

C. Set the primary_metric_goal of the estimator used to run the bird_classifier_train.py script to minimize.

D. Set the primary_metric_name of the estimator used to run the bird_classifier_train.py script to accuracy.

E. Set the primary_metric_name of the estimator used to run the bird_classifier_train.py script to loss.

F. Add code to the bird_classifier_train.py script to calculate the validation accuracy of the model and log it as a float value with the key accuracy.

Correct Answer:ADF
AD:
primary_metric_name="accuracy", primary_metric_goal=PrimaryMetricGoal.MAXIMIZE
Optimize the runs to maximize "accuracy". Make sure to log this value in your training script. Note:
primary_metric_name: The name of the primary metric to optimize. The name of the primary metric needs to exactly match the name of the metric logged by the training script.
primary_metric_goal: It can be either PrimaryMetricGoal.MAXIMIZE or PrimaryMetricGoal.MINIMIZE and determines whether the primary metric will be maximized or minimized when evaluating the runs.
F: The training script calculates the val_accuracy and logs it as "accuracy", which is used as the primary metric.