DP-100 Reliable Test Topics, Examinations DP-100 Actual Questions

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Microsoft DP-100 Exam Syllabus Topics:

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

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q446-Q451):

NEW QUESTION # 446
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 creating a model to predict the price of a student's artwork depending on the following variables: the student's length of education, degree type, and art form.
You start by creating a linear regression model.
You need to evaluate the linear regression model.
Solution: Use the following metrics: Relative Squared Error, Coefficient of Determination, Accuracy, Precision, Recall, F1 score, and AUC.
Does the solution meet the goal?

Answer: A

Explanation:
Relative Squared Error, Coefficient of Determination are good metrics to evaluate the linear regression model, but the others are metrics for classification models.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model


NEW QUESTION # 447
You create a binary classification model. You use the Fairlearn package to assess model fairness. You must eliminate the need to retrain the model. You need to implement the Fair learn package. Which algorithm should you use?

Answer: B


NEW QUESTION # 448
You create a multi-class image classification deep learning model that uses a set of labeled images. You create a script file named train.py that uses the PyTorch 1.3 framework to train the model.
You must run the script by using an estimator. The code must not require any additional Python libraries to be installed in the environment for the estimator. The time required for model training must be minimized.
You need to define the estimator that will be used to run the script.
Which estimator type should you use?

Answer: A

Explanation:
Explanation
For PyTorch, TensorFlow and Chainer tasks, Azure Machine Learning provides respective PyTorch, TensorFlow, and Chainer estimators to simplify using these frameworks.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-ml-models


NEW QUESTION # 449
You have the following Azure subscriptions and Azure Machine Learning service workspaces:

You need to obtain a reference to the ml-project workspace.
Solution: Run the following Python code:

Does the solution meet the goal?

Answer: A


NEW QUESTION # 450
You create an Azure Machine Learning dataset containing automobile price data. The dataset includes 10.000 rows and 10 columns. You use the Azure Machine Learning designer to transform the dataset by using an Execute Python Script component and custom code.
The code must combine three columns to create a new column.
You need to configure the code function.
Which configurations 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 # 451
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