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Microsoft DP-100 Exam Syllabus Topics:

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

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Microsoft Designing and Implementing a Data Science Solution on Azure DP-100 Prüfungsfragen mit Lösungen (Q143-Q148):

143. Frage
You are performing clustering by using the K-means algorithm.
You need to define the possible termination conditions.
Which three conditions can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

Antwort: A,B,C


144. Frage
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.

Antwort:

Begründung:

Explanation:

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)


145. Frage
You are tuning a hyperparameter for an algorithm. The following table shows a data set with different hyperparameter, training error, and validation errors.

Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.

Antwort:

Begründung:

Explanation

Box 1: 4
Choose the one which has lower training and validation error and also the closest match.
Minimize variance (difference between validation error and train error).
Box 2: 5
Minimize variance (difference between validation error and train error).
Reference:
https://medium.com/comet-ml/organizing-machine-learning-projects-project-management-guidelines-2d2b85651


146. Frage
You create an Azure Machine Learning workspace.
You plan to write an Azure Machine Learning SDK for Python v2 script that logs an image for an experiment.
The logged image must be available from the images tab in Azure Machine Learning Studio.
You need to complete the script.
Which code segments should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Antwort:

Begründung:

Explanation:


147. Frage
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 use Azure Machine Learning designer to load the following datasets into an experiment:

You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.
Solution: Use the Execute Python Script module.
Does the solution meet the goal?

Antwort: A


148. Frage
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