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Microsoft DP-100 Exam Syllabus Topics:

Section	Objectives
Topic 1: Design and prepare a machine learning solution	- Manage compute and data assets - Plan and configure Azure Machine Learning workspace - Select appropriate Azure services for machine learning workloads
Topic 2: Deploy and consume models	- Monitor deployed models and endpoints - Deploy models to endpoints
Topic 3: Train machine learning models	- Train models using Azure Machine Learning - Tune hyperparameters and evaluate models
Topic 4: Optimize and manage models	- Improve model performance - Track experiments and manage model lifecycle
Topic 5: Explore and analyze data	- Ingest and prepare data for modeling - Perform exploratory data analysis

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q27-Q32):

NEW QUESTION # 27
You deploy a real-time inference service for a trained model.
The deployed model supports a business-critical application, and it is important to be able to monitor the data submitted to the web service and the predictions the data generates.
You need to implement a monitoring solution for the deployed model using minimal administrative effort.
What should you do?

A. View the log files generated by the experiment used to train the model.
B. View the explanations for the registered model in Azure ML studio.
C. Enable Azure Application Insights for the service endpoint and view logged data in the Azure portal.
D. Create an ML Flow tracking URI that references the endpoint, and view the data logged by ML Flow.

Answer: C

Explanation:
Configure logging with Azure Machine Learning studio
You can also enable Azure Application Insights from Azure Machine Learning studio. When you're ready to deploy your model as a web service, use the following steps to enable Application Insights:
1. Sign in to the studio at https://ml.azure.com.
2. Go to Models and select the model you want to deploy.
3. Select +Deploy.
4. Populate the Deploy model form.
5. Expand the Advanced menu.
6. Select Enable Application Insights diagnostics and data collection.

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-enable-app-insights

NEW QUESTION # 28
You are implementing hyperparameter tuning for a model training from a notebook. The notebook is in an Azure Machine Learning workspace. You add code that imports all relevant Python libraries.
You must configure Bayesian sampling over the search space for the num_hidden_layers and batch_size hyperparameters.
You need to complete the following Python code to configure Bayesian sampling.
Which code segments should you use? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 29
You use the Azure Machine Learning SDK to run a training experiment that trains a classification model and calculates its accuracy metric.
The model will be retrained each month as new data is available.
You must register the model for use in a batch inference pipeline.
You need to register the model and ensure that the models created by subsequent retraining experiments are registered only if their accuracy is higher than the currently registered model.
What are two possible ways to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. Specify a property named accuracy with the accuracy metric as a value when registering the model, and only register subsequent models if their accuracy is higher than the accuracy property value of the currently registered model.
B. Specify a tag named accuracy with the accuracy metric as a value when registering the model, and only register subsequent models if their accuracy is higher than the accuracy tag value of the currently registered model.
C. Register the model with the same name each time regardless of accuracy, and always use the latest
D. Specify a different name for the model each time you register it.
E. Specify the model framework version when registering the model, and only register subsequent models if this value is higher.

Answer: B,E

Explanation:
E: Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric.
Reference:
https://notebooks.azure.com/xavierheriat/projects/azureml-getting-started/html/how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb

NEW QUESTION # 30
You deploy a real-time inference service for a trained model.
The deployed model supports a business-critical application, and it is important to be able to monitor the data submitted to the web service and the predictions the data generates.
You need to implement a monitoring solution for the deployed model using minimal administrative effort.
What should you do?

A. View the log files generated by the experiment used to train the model.
B. View the explanations for the registered model in Azure ML studio.
C. Enable Azure Application Insights for the service endpoint and view logged data in the Azure portal.
D. Create an ML Flow tracking URI that references the endpoint, and view the data logged by ML Flow.

Answer: C

NEW QUESTION # 31
You create a binary classification model using Azure Machine Learning Studio.
You must use a Receiver Operating Characteristic (RO C) curve and an F1 score to evaluate the model.
You need to create the required business metrics.
How should you complete the experiment? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 32
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