Free PDF 2026 DP-100: Designing and Implementing a Data Science Solution on Azure–High Pass-Rate Exam Vce

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| Section | Objectives |
|---|
| Topic 1: Train machine learning models | - Train models using Azure Machine Learning - Tune hyperparameters and evaluate models
|
| Topic 2: Deploy and consume models | - Deploy models to endpoints - Monitor deployed models and endpoints
|
| Topic 3: Design and prepare a machine learning solution | - Manage compute and data assets - Select appropriate Azure services for machine learning workloads - Plan and configure Azure Machine Learning workspace
|
| Topic 4: Optimize and manage models | - Improve model performance - Track experiments and manage model lifecycle
|
| Topic 5: Explore and analyze data | - Perform exploratory data analysis - Ingest and prepare data for modeling
|
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2026 Efficient DP-100: Exam Designing and Implementing a Data Science Solution on Azure Vce
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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q428-Q433):
NEW QUESTION # 428
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. View the log files for the experiment run in the experiment folder.
- B. Use the get_output() method of the run object to retrieve the experiment run logs.
- C. Use the get_metrics() method of the run object to retrieve the experiment run logs.
- D. View the logs for the experiment run in Azure Machine Learning studio.
- E. Use the get_details_with_logs() method of the run object to display the experiment run logs.
Answer: D,E
Explanation:
Use get_details_with_logs() to fetch the run details and logs created by the run.
You can monitor Azure Machine Learning runs and view their logs with the Azure Machine Learning studio.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.steprun
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-monitor-view-training-logs
NEW QUESTION # 429
You need to select a feature extraction method.
Which method should you use?
- A. Mutual information
- B. Pearson's correlation
- C. Mann-Whitney test
- D. Spearman correlation
Answer: D
Explanation:
Spearman's rank correlation coefficient assesses how well the relationship between two variables can be described using a monotonic function.
Note: Both Spearman's and Kendall's can be formulated as special cases of a more general correlation coefficient, and they are both appropriate in this scenario.
Scenario: The MedianValue and AvgRoomsInHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/feature-selection-modules
NEW QUESTION # 430
You need to obtain the output from the pipeline execution. Where will you find the output?
- A. the digitjdentification.py script
- B. the Inference Clusters tab in Machine Learning studio
- C. the debug log
- D. a file named parallel_run_step.txt located in the output folder
- E. the Activity Log in the Azure portal for the Machine Learning workspace
Answer: E
NEW QUESTION # 431
You create an Azure Machine Learning workspace named workspace1. You assign a custom role to a user of workspace1.
The custom role has the following JSON definition:

Instructions: 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:

Explanation:

Box 1: No
The actions listed in NotActions are prohibited.
If the roles include Actions that have a wildcard (*), the effective permissions are computed by subtracting the NotActions from the allowed Actions.
Box 2: No
Deleting compute resources in the workspace is in the NotActions list.
Box 3: Yes
Writing metrics is not listed in NotActions.
Reference:
https://docs.microsoft.com/en-us/azure/role-based-access-control/overview#how-azure-rbac-determines-if-a- user-has-access-to-a-resource
NEW QUESTION # 432
You create an Azure Machine Learning workspace named workspace1. You assign a custom role to a user of workspace1.
The custom role has the following JSON definition:

Instructions: 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:

Explanation:

Box 1: No
The actions listed in NotActions are prohibited.
If the roles include Actions that have a wildcard (*), the effective permissions are computed by subtracting the NotActions from the allowed Actions.
Box 2: No
Deleting compute resources in the workspace is in the NotActions list.
Box 3: Yes
Writing metrics is not listed in NotActions.
Reference:
https://docs.microsoft.com/en-us/azure/role-based-access-control/overview#how-azure-rbac-determines-if-a- user-has-access-to-a-resource
NEW QUESTION # 433
......
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