Free Microsoft DP-100 Exam Questions

Question 1

You develop and train a machine learning model to predict fraudulent transactions for a hotel booking website. Traffic to the site varies considerably. The site experiences heavy traffic on Monday and Friday and much lower traffic on other days. Holidays are also high web traffic days. You need to deploy the model as an Azure Machine Learning real-time web service endpoint on compute that can dynamically scale up and down to support demand. Which deployment compute option should you use?

A : attached Azure Databricks cluster

B : Azure Container Instance (ACI)

C : Azure Kubernetes Service (AKS) inference cluster

D : Azure Machine Learning Compute Instance

E : attached virtual machine in a different region

Answer: E

Question 2

You manage an Azure Machine Learning workspace. The development environment tor managing the workspace is configured to use Python SDK v2 in Azure Machine Learning Notebooks A Synapse Spark Compute is currently attached and uses system-assigned identity You need to use Python code to update the Synapse Spark Compute 10 use a user-assigned identity. Solution: Configure the IdentityConfiguration class with the appropriate identity type. Does the solution meet the goal?

A : Yes

B : No

Answer: A

Question 3

You use the Azure Machine Learning SDK in a notebook to run an experiment using a script file in an experiment folder. The experiment fails. You need to troubleshoot the failed experiment. What are two possible ways to achieve this goal? Each correct answer presents a complete solution.

A : Use the get_metrics() method of the run object to retrieve the experiment run logs.

B : Use the get_details_with_logs() method of the run object to display the experiment run logs.

C : View the log files for the experiment run in the experiment folder.

D : View the logs for the experiment run in Azure Machine Learning studio.

E : Use the get_output() method of the run object to retrieve the experiment run logs.

Answer: B,D

Question 4

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 different name for the model each time you register it.

B : Register the model with the same name each time regardless of accuracy, and always use the latest version of the model in the batch inferencing pipeline

C : Specify the model framework version when registering the model, and only register subsequent models if this value is higher.

D : 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.

E : 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.

Answer: D,E

Question 5

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You are analyzing a numerical dataset which contains missing values in several columns. You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set. You need to analyze a full dataset to include all values. Solution: Replace each missing value using the Multiple Imputation by Chained Equations (MICE) method. Does the solution meet the goal?

A : Yes

B : No

Answer: A