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Exam DP-100 Tests & DP-100 Instant Access

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Posted on: 06/05/25

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DP-100 Exam topics

Candidates must know the exam topics before they start of preparation. Because it will really help them in hitting the core. Our DP-100 exam dumps will include the following topics:

  • Prepare data for modeling 25-30%
  • Perform feature engineering 15-20%
  • Define and prepare the development environment 15-20%
  • Develop models 40-45%

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Achieving the Microsoft DP-100 Certification is a valuable accomplishment for data professionals, as it demonstrates their competency in working with data on the Azure platform. It is also a valuable asset for organizations looking to hire data professionals, as it shows that the candidate has the necessary skills and expertise to design and implement data science solutions using Azure tools and services.

Microsoft DP-100 certification exam is designed to test the skills and knowledge required to design and implement a data science solution on Azure. DP-100 Exam is aimed at professionals who are involved in building and implementing data science solutions, including data scientists, data engineers, and machine learning engineers. Passing the exam demonstrates that the candidate has the skills and knowledge required to design and implement a data science solution on Azure.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q431-Q436):

NEW QUESTION # 431
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 # 432
You create an Azure Machine Learning workspace.
You must create a custom role named that meets the following requirements:
Role members must not be able to delete the workspace.
Role members must not be able to create, update, or delete compute resource in the workspace.
Role members must not be able to add new users to the workspace.
You need to create a JSON file for the DataScientist role in the Azure Machine Learning workspace.
The custom role must enforce the restrictions specified by the IT Operations team.
Which JSON code segment should you use?

  • A.
  • B.
  • C.
  • D.

Answer: A

Explanation:
Explanation
The following custom role can do everything in the workspace except for the following actions:
It can't create or update a compute resource.
It can't delete a compute resource.
It can't add, delete, or alter role assignments.
It can't delete the workspace.
To create a custom role, first construct a role definition JSON file that specifies the permission and scope for the role. The following example defines a custom role named "Data Scientist Custom" scoped at a specific workspace level:
data_scientist_custom_role.json :
{
"Name": "Data Scientist Custom",
"IsCustom": true,
"Description": "Can run experiment but can't create or delete compute.",
"Actions": ["*"],
"NotActions": [
"Microsoft.MachineLearningServices/workspaces/*/delete",
"Microsoft.MachineLearningServices/workspaces/write",
"Microsoft.MachineLearningServices/workspaces/computes/*/write",
"Microsoft.MachineLearningServices/workspaces/computes/*/delete",
"Microsoft.Authorization/*/write"
],
"AssignableScopes": [
"/subscriptions/<subscription_id>/resourceGroups/<resource_group_name>/providers/Microsoft.MachineLearni
]
}
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-assign-roles


NEW QUESTION # 433
You create a multi-class image classification deep learning model that uses the PyTorch deep learning framework.
You must configure Azure Machine Learning Hyperdrive to optimize the hyperparameters for the classification model.
You need to define a primary metric to determine the hyperparameter values that result in the model with the best accuracy score.
Which three actions must you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

  • A. Set the primary_metric_name of the estimator used to run the bird_classifier_train.py script to loss.
  • B. Set the primary_metric_goal of the estimator used to run the bird_classifier_train.py script to maximize.
  • C. Set the primary_metric_name of the estimator used to run the bird_classifier_train.py script to accuracy.
  • D. Add code to the bird_classifier_train.py script to calculate the validation accuracy of the model and log it as a float value with the key accuracy.
  • E. Set the primary_metric_goal of the estimator used to run the bird_classifier_train.py script to minimize.
  • F. Add code to the bird_classifier_train.py script to calculate the validation loss of the model and log it as a float value with the key loss.

Answer: B,C,D

Explanation:
AD:
primary_metric_name="accuracy",
primary_metric_goal=PrimaryMetricGoal.MAXIMIZE
Optimize the runs to maximize "accuracy". Make sure to log this value in your training script.
Note:
primary_metric_name: The name of the primary metric to optimize. The name of the primary metric needs to exactly match the name of the metric logged by the training script.
primary_metric_goal: It can be either PrimaryMetricGoal.MAXIMIZE or
PrimaryMetricGoal.MINIMIZE and determines whether the primary metric will be maximized or minimized when evaluating the runs.
F: The training script calculates the val_accuracy and logs it as "accuracy", which is used as the primary metric.


NEW QUESTION # 434
You use Azure Machine Learning studio to analyze an mltable data asset containing a decimal column named column1. You need to verify that the column1 values are normally distributed.
Which statistic should you use?

  • A. Mean
  • B. Profile
  • C. Type
  • D. Max

Answer: B


NEW QUESTION # 435
You manage an Azure Al Foundry project.
You develop a Prompt flow that includes a large language model (LLM) node and an upstream node with a single output. You need to link the LLM node input with the output of the upstream node by using a YAML flow configuration. Which flow configuration should you use?

  • A. (% upstream node_name,output%}
  • B. <#upstream_node_nameoutput#>
  • C. {{upstream.node.nameoutput})
  • D. $(upstream_node_name.output)

Answer: B


NEW QUESTION # 436
......

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