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Microsoft DP-100 Exam Syllabus Topics:

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

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2026 DP-100 Practice Braindumps - Valid Microsoft Designing and Implementing a Data Science Solution on Azure - DP-100 Online Bootcamps

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q141-Q146):

NEW QUESTION # 141
You create an Azure Machine Learning workspace and a dataset. The dataset includes age values for a large group of diabetes patients. You use the dp.mean function from the SmartNoise library to calculate the mean of the age value. You store the value in a variable named age.mean.
You must output the value of the interval range of released mean values that will be returned 95 percent of the time.
You need to complete the code.
Which code values should you use? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 142
You have an Azure Machine Learning workspace. You are running an experiment on your local computer.
You need to use MLflow Tracking to store metrics and artifacts from your local experiment runs in the workspace.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:

Explanation


NEW QUESTION # 143
You manage an Azure Machine Learning workspace named workspace1 with a compute instance named compute1. You connect to compute! by using a terminal window from wofkspace1. You create a file named " requirements.txt " containing Python dependencies to include Jupyler.
You need to add a new Jupyter kernel to compute1.
Which four commands should you use? 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:


NEW QUESTION # 144
You need to modify the inputs for the global penalty event model to address the bias and variance issue.
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 - Select the behavior data.
2 - Add a K-Means clustering module with 10 clusters
3 - Perform a primary Component Analysis (PCA)


NEW QUESTION # 145
You create a training pipeline using the Azure Machine Learning designer. You upload a CSV file that contains the data from which you want to train your model.
You need to use the designer to create a pipeline that includes steps to perform the following tasks:
Select the training features using the pandas filter method.
Train a model based on the naive_bayes.GaussianNB algorithm.
Return only the Scored Labels column by using the query SELECT [Scored Labels] FROM t1; Which modules should you use? To answer, drag the appropriate modules to the appropriate locations. Each module name may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 146
......

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