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Microsoft DP-100 Exam Syllabus Topics:

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

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q19-Q24):

NEW QUESTION # 19
You create a multi-class image classification deep learning experiment by using the PyTorch framework. You plan to run the experiment on an Azure Compute cluster that has nodes with GPU's.
You need to define an Azure Machine Learning service pipeline to perform the monthly retraining of the image classification model. The pipeline must run with minimal cost and minimize the time required to train the model.
Which three pipeline steps should you run 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:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-pytorch


NEW QUESTION # 20
You are using Azure Machine Learning to train machine learning models. You need a compute target on which to remotely run the training script. You run the following Python code:

Answer:

Explanation:

Explanation:

Box 1: Yes
The compute is created within your workspace region as a resource that can be shared with other users.
Box 2: Yes
It is displayed as a compute cluster.
View compute targets
1. To see all compute targets for your workspace, use the following steps:
2. Navigate to Azure Machine Learning studio.
3. Under Manage, select Compute.
4. Select tabs at the top to show each type of compute target.

Box 3: Yes
min_nodes is not specified, so it defaults to 0.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute.
amlcomputeprovisioningconfiguration
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio


NEW QUESTION # 21
You are analyzing a dataset by using Azure Machine Learning Studio.
YOU need to generate a statistical summary that contains the p value and the unique value count for each feature column.
Which two modules can you users? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

Answer: B,D


NEW QUESTION # 22
You create an Azure Machine Learning workspace.
You need to detect data drift between a baseline dataset and a subsequent target dataset by using the DataDriftDetector class.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation
Graphical user interface, text, application, Word Description automatically generated

Box 1: create_from_datasets
The create_from_datasets method creates a new DataDriftDetector object from a baseline tabular dataset and a target time series dataset.
Box 2: backfill
The backfill method runs a backfill job over a given specified start and end date.
Syntax: backfill(start_date, end_date, compute_target=None, create_compute_target=False) Reference:
https://docs.microsoft.com/en-us/python/api/azureml-datadrift/azureml.datadrift.datadriftdetector(class)


NEW QUESTION # 23
You have an Azure Machine Learning workspace named Workspaces
You plan to train an image object detection model by using Automated ML in Workspace1.
You need to complete the provided Azure Machine Learning Python SDK v2 code to start an image object detection job.
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:

Explanation:


NEW QUESTION # 24
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