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Microsoft DP-100 Exam Syllabus Topics:

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

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q106-Q111):

NEW QUESTION # 106
You create an Azure Machine Learning workspace named ML-workspace. You also create an Azure Databricks workspace named DB-workspace. DB-workspace contains a cluster named DB-cluster.
You must use DB-cluster to run experiments from notebooks that you import into DB-workspace.
You need to use ML-workspace to track MLflow metrics and artifacts generated by experiments running on DB-cluster. The solution must minimize the need for custom code.
What should you do?

Answer: C

Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-azure-databricks


NEW QUESTION # 107
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 using Azure Machine Learning to run an experiment that trains a classification model.
You want to use Hyperdrive to find parameters that optimize the AUC metric for the model. You configure a HyperDriveConfig for the experiment by running the following code:

You plan to use this configuration to run a script that trains a random forest model and then tests it with validation data. The label values for the validation data are stored in a variable named y_test variable, and the predicted probabilities from the model are stored in a variable named y_predicted.
You need to add logging to the script to allow Hyperdrive to optimize hyperparameters for the AUC metric.
Solution: Run the following code:

Does the solution meet the goal?

Answer: B

Explanation:
Explanation
Use a solution with logging.info(message) instead.
Note: Python printing/logging example:
logging.info(message)
Destination: Driver logs, Azure Machine Learning designer
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipelines


NEW QUESTION # 108
You are using the Azure Machine Learning designer to transform a dataset by using an Execute Python Script component and custom code.
You need to define the method signature for the Execute Python Script component and return value type.
What should you define? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 109
You create an Azure Machine Learning workspace.
You must implement dedicated compute for model training in the workspace by using Azure Synapse compute resources. The solution must attach the dedicated compute and start an Azure Synapse session.
You need to implement the compute resources.
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 a linked service by using Azure Machine Learning studio.
2 - Create an Azure Synapse workspace by using the Azure portal.
3 - Create an Apache Spark pool by using the Azure portal.


NEW QUESTION # 110
You must store data in Azure Blob Storage to support Azure Machine Learning.
You need to transfer the data into Azure Blob Storage.
What are three possible ways to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

Answer: B,C,D

Explanation:
You can move data to and from Azure Blob storage using different technologies:
* Azure Storage-Explorer
* AzCopy
* Python
* SSIS
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/move-azure-blob


NEW QUESTION # 111
......

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