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| Section | Weight | Objectives |
|---|
| Topic 1: Train and deploy models | 25-30% | - Manage models
- 1. Register and version models
- 2. Package and validate models
- 3. Interpret models and explain predictions
- Train models
- 1. Use HyperDrive for hyperparameter tuning
- 2. Apply responsible AI principles
- 3. Configure jobs and environments
- 4. Run training scripts
- Deploy models
- 1. Secure endpoints and manage access
- 2. Configure compute and scaling
- 3. Deploy to batch endpoints
- 4. Deploy to online endpoints
- Monitor and maintain models
- 1. Update and retrain models
- 2. Implement MLOps practices
- 3. Monitor performance and data drift
|
| Topic 2: Design and prepare a machine learning solution | 20-25% | - Manage Azure Machine Learning workspace
- 1. Work with registries
- 2. Create and configure workspace
- 3. Use developer tools and CLI
- 4. Set up Git integration
- Design a machine learning solution
- 1. Plan model deployment requirements
- 2. Select development approach
- 3. Define compute specifications for workloads
- 4. Determine dataset structure and format
- Manage compute resources
- 1. Attach and monitor compute
- 2. Select environments
- 3. Create and configure compute targets
- Manage data assets
- 1. Register and manage datastores
- 2. Select storage services
- 3. Create and maintain data assets
|
| Topic 3: Explore data and run experiments | 20-25% | - Explore and visualize data
- 1. Detect anomalies and outliers
- 2. Identify features and relationships
- 3. Profile and validate data
- Run experiments
- 1. Define parameters and configurations
- 2. Use automated machine learning
- 3. Track runs with MLflow
- 4. Configure experiment runs
- Implement pipelines
- 1. Schedule and monitor pipelines
- 2. Pass data between steps
- 3. Build reusable components
- 4. Create and publish pipelines
|
| Topic 4: Optimize language models for AI applications | 25-30% | - 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. Build prompt flows
- 2. Use Azure AI Foundry
- 3. Apply prompt engineering
- Evaluate and improve models
- 1. Apply responsible generative AI
- 2. Test and evaluate responses
- 3. Optimize for accuracy and safety
|
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Microsoft DP-100 Questions - Easy way to Prepare for Exam
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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q65-Q70):
NEW QUESTION # 65
You create an Azure Machine Learning workspace. You are training a classification model with no-code AutoML in Azure Machine Learning studio.
The model must predict if a client of a financial institution will subscribe to a fixed-term deposit. You must identify the feature that has the most influence on the predictions of the model for the second highest scoring algorithm. You must minimize the effort and time to identify the feature.
You need to complete the identification.
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 teh second algotithm on the list..
2 - Select the Explain model option.
3 - Display the aggregate feature importance chart.
NEW QUESTION # 66
You create a binary classification model using Azure Machine Learning Studio.
You must use a Receiver Operating Characteristic (RO C) curve and an F1 score to evaluate the model.
You need to create the required business metrics.
How should you complete the experiment? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.


Answer:
Explanation:

NEW QUESTION # 67
You create a pipeline in designer to train a model that predicts automobile prices.
Because of non-linear relationships in the data, the pipeline calculates the natural log (Ln) of the prices in the training data, trains a model to predict this natural log of price value, and then calculates the exponential of the scored label to get the predicted price.
The training pipeline is shown in the exhibit. (Click the Training pipeline tab.) Training pipeline

You create a real-time inference pipeline from the training pipeline, as shown in the exhibit. (Click the Real-time pipeline tab.) Real-time pipeline

You need to modify the inference pipeline to ensure that the web service returns the exponential of the scored label as the predicted automobile price and that client applications are not required to include a price value in the input values.
Which three modifications must you make to the inference pipeline? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. Remove the Apply Math Operation module that replaces price with its natural log from the data flow.
- B. Connect the output of the Apply SQL Transformation to the Web Service Output module.
- C. Remove the Apply SQL Transformation module from the data flow.
- D. Replace the Web Service Input module with a data input that does not include the price column.
- E. Add a Select Columns module before the Score Model module to select all columns other than price.
- F. Replace the training dataset module with a data input that does not include the price column.
Answer: A,B,E
NEW QUESTION # 68
You have a Jupyter Notebook that contains Python code that is used to train a model.
You must create a Python script for the production deployment. The solution must minimize code maintenance.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. Refactor the Jupyter Notebook code into functions
- B. Define a main() function in the Python script
- C. B. Save each function to a separate Python file
- D. Remove all comments and functions from the Python script
Answer: A,B
Explanation:
Reference:
https://www.guru99.com/learn-python-main-function-with-examples-understand-main.html
https://towardsdatascience.com/from-jupyter-notebook-to-deployment-a-straightforward-example-
1838c203a437
NEW QUESTION # 69
You train a machine learning model by using Aunt Machine Learning.
You use the following training script m Python to log an accuracy value.

You must use a Python script to define a sweep job.
You need to provide the primary metric and goal you want hyper parameter tuning to optimize.
How should you complete the Python script? To answer select the appropriate options in the answer area NOTE: Each correct selection is worth one point.

Answer:
Explanation:

NEW QUESTION # 70
......
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