2026 Microsoft - DP-100 - Designing and Implementing a Data Science Solution on Azure Real Dump

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Microsoft DP-100 Exam Syllabus Topics:

SectionWeightObjectives
Explore data and run experiments20-25%- Implement pipelines
  • 1. Schedule and monitor pipelines
  • 2. Build reusable components
  • 3. Pass data between steps
  • 4. Create and publish pipelines
- Run experiments
  • 1. Use automated machine learning
  • 2. Define parameters and configurations
  • 3. Track runs with MLflow
  • 4. Configure experiment runs
- Explore and visualize data
  • 1. Profile and validate data
  • 2. Detect anomalies and outliers
  • 3. Identify features and relationships
Train and deploy models25-30%- Train models
  • 1. Apply responsible AI principles
  • 2. Run training scripts
  • 3. Use HyperDrive for hyperparameter tuning
  • 4. Configure jobs and environments
- Deploy models
  • 1. Configure compute and scaling
  • 2. Deploy to online endpoints
  • 3. Secure endpoints and manage access
  • 4. Deploy to batch endpoints
- Manage models
  • 1. Register and version models
  • 2. Interpret models and explain predictions
  • 3. Package and validate models
- Monitor and maintain models
  • 1. Monitor performance and data drift
  • 2. Implement MLOps practices
  • 3. Update and retrain models
Design and prepare a machine learning solution20-25%- Manage Azure Machine Learning workspace
  • 1. Use developer tools and CLI
  • 2. Work with registries
  • 3. Set up Git integration
  • 4. Create and configure workspace
- Design a machine learning solution
  • 1. Determine dataset structure and format
  • 2. Define compute specifications for workloads
  • 3. Select development approach
  • 4. Plan model deployment requirements
- Manage data assets
  • 1. Select storage services
  • 2. Register and manage datastores
  • 3. Create and maintain data assets
- Manage compute resources
  • 1. Create and configure compute targets
  • 2. Select environments
  • 3. Attach and monitor compute
Optimize language models for AI applications25-30%- Evaluate and improve models
  • 1. Test and evaluate responses
  • 2. Optimize for accuracy and safety
  • 3. Apply responsible generative AI
- Implement generative AI solutions
  • 1. Use Azure AI Foundry
  • 2. Apply prompt engineering
  • 3. Build prompt flows
- Optimize with Retrieval Augmented Generation
  • 1. Create vector stores and indexes
  • 2. Prepare and process data
  • 3. Configure Azure AI Search

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q399-Q404):

NEW QUESTION # 399
You manage an Azure Machine Learning workspace.
You choose the uri_folder data type as an output of a pipeline component.
You need to define the data access mode that is supported by your configuration.
Which mode should you define?

Answer: D

Explanation:
This mode allows the output directory to be a mounted directory, which is suitable for uri_folder data type outputs. It provides read-write access to the output location, enabling the component to write its output directly to the mounted directory.


NEW QUESTION # 400
You create an Azure Machine Learning workspace.
You must create a custom role named DataScientist 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?

Answer: B

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.
MachineLearningServices/workspaces/<workspace_name>"
]
}
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-assign-roles


NEW QUESTION # 401
You are developing a machine learning solution by using the Azure Machine Learning designer.
You need to create a web service that applications can use to submit data feature values and retrieve a predicted label.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:

1 - Create and run a training pipeling
2 - Deploy a service to an inference cluster.
3 - Create and run a real-time inference pipeline.


NEW QUESTION # 402
You have an Azure Machine Learning workspace named Workspace 1 Workspace! has a registered Mlflow model named model 1 with PyFunc flavor You plan to deploy model1 to an online endpoint named endpointl without egress connectivity by using Azure Machine learning Python SDK vl You have the following code:

You need to add a parameter to the ManagedOnllneDeployment object to ensure the model deploys successfully Solution: Add the with_package parameter.
Does the solution meet the goal?

Answer: B


NEW QUESTION # 403
You create a multi-class image classification deep learning model.
The model must be retrained monthly with the new image data fetched from a public web portal. You create an Azure Machine Learning pipeline to fetch new data, standardize the size of images, and retrain the model.
You need to use the Azure Machine Learning SDK to configure the schedule for the pipeline.
Which four actions should you perform in sequence. To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:


NEW QUESTION # 404
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