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Home > Microsoft > Microsoft Certifications > DP-100
Microsoft DP-100 Exam Questions & Answers
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Microsoft DP-100 Exam Questions & Answers


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  • Vendor: Microsoft

    Exam Code: DP-100

    Exam Name: Designing and Implementing a Data Science Solution on Azure

    Certification Provider: Microsoft

    Total Questions: 617 Q&A ( View Details)

    Updated on: May 26, 2026

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DP-100 Online Practice Questions and Answers

Questions 1

HOTSPOT

You create a binary classification model to predict whether a person has a disease.

You need to detect possible classification errors.

Which error type should you choose for each description? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

Hot Area:

Show Answer

Correct Answer:

Box 1: True Positive

A true positive is an outcome where the model correctly predicts the positive class

Box 2: True Negative

A true negative is an outcome where the model correctly predicts the negative class.

Box 3: False Positive

A false positive is an outcome where the model incorrectly predicts the positive class.

Box 4: False Negative

A false negative is an outcome where the model incorrectly predicts the negative class.

Note: Let's make the following definitions:

"Wolf" is a positive class.

"No wolf" is a negative class.

We can summarize our "wolf-prediction" model using a 2x2 confusion matrix that depicts all four possible outcomes:

Reference:

https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative

Questions 2

You have been tasked with evaluating your model on a partial data sample via k-fold cross-validation.

You have already configured a k parameter as the number of splits. You now have to configure the k parameter for the cross-validation with the usual value choice.

Recommendation: You configure the use of the value k=10.

Will the requirements be satisfied?

A. Yes

B. No

Show Answer

Correct Answer: A

Leave One Out (LOO) cross-validation

Setting K = n (the number of observations) yields n-fold and is called leave-one out cross-validation (LOO), a special case of the K-fold approach.

LOO CV is sometimes useful but typically doesn't shake up the data enough. The estimates from each fold are highly correlated and hence their average can have high variance.

This is why the usual choice is K=5 or 10. It provides a good compromise for the bias-variance tradeoff.

Questions 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_enabledparameter of the target configuration object to true

B. Deploy the model to Azure Container Instances. During deployment, set the auth_enabledparameter of the target configuration object to true

C. Deploy the model to Azure Container Instances. During deployment, set the token_auth_enabledparameter of the target configuration object to true

D. Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the auth.enabledparameter of the target configuration object to true

Show Answer More Questions

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.

Incorrect Answers:

C: Token authentication isn't supported when you deploy to Azure Container Instances.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-authenticate-web-service

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