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This exam measures your ability to accomplish the following technical tasks: manage Azure resources for machine learning; run experiments and train models; deploy and operationalize machine learning solutions; and implement responsible machine learning.
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Candidates for the DP-100 certification exam should have experience with Azure services and tools, as well as a strong understanding of data science concepts and methodologies. It is recommended that you have at least one year of experience working with data science projects before attempting DP-100 Exam. With the DP-100 certification, you can demonstrate your expertise to potential employers and advance your career in the field of data science.
Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q126-Q131):
NEW QUESTION # 126
Hotspot Question
You are using hyperparameter tuning in Azure Machine Learning Python SDK v2 to train a model.
You configure the hyperparameter tuning experiment by running the following code:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

NEW QUESTION # 127
Hotspot Question
You create an Azure Machine Learning workspace. You train a classification model by using automated machine learning (automated ML) in Azure Machine Learning studio. The training data contains multiple classes that have significantly different numbers of samples.
You must use a metric type to avoid labeling negative samples as positive and an averaging method that will minimize the class imbalance.
You need to configure the metric type and the averaging method.
Which configurations 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 # 128
You are implementing a machine learning model to predict stock prices.
The model uses a PostgreSQL database and requires GPU processing.
You need to create a virtual machine that is pre-configured with the required tools.
What should you do?
- A. Create a Data Science Virtual Machine (DSVM) Linux edition.
Incorrect Answers:
A, C: PostgreSQL (CentOS) is only available in the Linux Edition.
B: The Azure Geo AI Data Science VM (Geo-DSVM) delivers geospatial analytics capabilities from Microsoft's Data Science VM. Specifically, this VM extends the AI and data science toolkits in the Data Science VM by adding ESRI's market-leading ArcGIS Pro Geographic Information System.
D: DLVM is a template on top of DSVM image. In terms of the packages, GPU drivers etc are all there in the DSVM image. Mostly it is for convenience during creation where we only allow DLVM to be created on GPU VM instances on Azure. - B. Create a Deep Learning Virtual Machine (DLVM) Linux edition.
- C. Create a Deep Learning Virtual Machine (DLVM) Windows edition.
- D. Create a Data Science Virtual Machine (DSVM) Windows edition.
- E. Create a Geo Al Data Science Virtual Machine (Geo-DSVM) Windows edition.
Answer: A
Explanation:
References:
https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview
NEW QUESTION # 129
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 # 130
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 explanations for the registered model in Azure ML studio.
- B. Enable Azure Application Insights for the service endpoint and view logged data in the Azure portal.
- C. Create an ML Flow tracking URI that references the endpoint, and view the data logged by ML Flow.
- D. View the log files generated by the experiment used to train the model.
Answer: B
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 # 131
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