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Microsoft DP-100 Exam Syllabus Topics:

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

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q299-Q304):

NEW QUESTION # 299
You create an Azure Machine Learning workspace and a new Azure DevOps organization. You register a model in the workspace and deploy the model to the target environment.
All new versions of the model registered in the workspace must automatically be deployed to the target environment.
You need to configure Azure Pipelines to deploy the model.
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:

Explanation:

Step 1: Create an Azure DevOps project
Step 2: Create a release pipeline
Sign in to your Azure DevOps organization and navigate to your project.
Go to Pipelines, and then select New pipeline.
Step 3: Install the Machine Learning extension for Azure Pipelines
You must install and configure the Azure CLI and ML extension.
Step 4: Create a service connection
How to set up your service connection

Select AzureMLWorkspace for the scope level, then fill in the following subsequent parameters.

Note: How to enable model triggering in a release pipeline
Go to your release pipeline and add a new artifact. Click on AzureML Model artifact then select the appropriate AzureML service connection and select from the available models in your workspace.
Enable the deployment trigger on your model artifact as shown here. Every time a new version of that model is registered, a release pipeline will be triggered.
Reference:
https://marketplace.visualstudio.com/items?itemName=ms-air-aiagility.vss-services-azureml
https://docs.microsoft.com/en-us/azure/devops/pipelines/targets/azure-machine-learning


NEW QUESTION # 300
You have machine learning models produce unfair predictions across sensitive features.
You must use a post-processing technique to apply a constraint to the models to mitigate their unfairness.
You need to select a post-processing technique and model type.
What should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:
See below image


NEW QUESTION # 301
You register a file dataset named csvjolder that references a folder. The folder includes multiple com ma- separated values (CSV) files in an Azure storage blob container. You plan to use the following code to run a script that loads data from the file dataset. You create and instantiate the following variables:

You have the following code:


You need to pass the dataset to ensure that the script can read the files it references. Which code segment should you insert to replace the code comment?

Answer: C

Explanation:
Example:
from azureml.train.estimator import Estimator
script_params = {
# to mount files referenced by mnist dataset
'--data-folder': mnist_file_dataset.as_named_input('mnist_opendataset').as_mount(),
'--regularization': 0.5
}
est = Estimator(source_directory=script_folder,
script_params=script_params,
compute_target=compute_target,
environment_definition=env,
entry_script='train.py')
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-train-models-with-aml


NEW QUESTION # 302
You use Azure Machine Learning to train and register a model.
You must deploy the model into production as a real-time web service to an inference cluster named service-compute that the IT department has created in the Azure Machine Learning workspace.
Client applications consuming the deployed web service must be authenticated based on their Azure Active Directory service principal.
You need to write a script that uses the Azure Machine Learning SDK to deploy the model. The necessary modules have been imported.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-azure-kubernetes-service
https://docs.microsoft.com/en-us/azure/databricks/dev-tools/api/latest/aad/service-prin-aad-token


NEW QUESTION # 303
You have an existing GitHub repository containing Azure Machine Learning project files.
You need to clone the repository to your Azure Machine Learning shared workspace file system.
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.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

Answer:

Explanation:

1 - Fro the terminal window in the Azure Machine Learning interface, run the git clone command.
2 - From the terminal window in the Azure Machine Learning interface, run the cat~/.ssh/id_rsa.pub command.
3 - From the terminal window in the Azure Machine Learning interface, run the ssh-keygen command.
4 - Add a public key to the GitHub account.


NEW QUESTION # 304
......

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