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Microsoft DP-100 Exam Syllabus Topics:

SectionWeightObjectives
Optimize language models for AI applications25-30%- Implement generative AI solutions
  • 1. Apply prompt engineering
  • 2. Use Azure AI Foundry
  • 3. Build prompt flows
- Evaluate and improve models
  • 1. Test and evaluate responses
  • 2. Apply responsible generative AI
  • 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
Train and deploy models25-30%- 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. Run training scripts
  • 4. Configure jobs and environments
- Deploy models
  • 1. Deploy to online endpoints
  • 2. Configure compute and scaling
  • 3. Deploy to batch endpoints
  • 4. Secure endpoints and manage access
- Monitor and maintain models
  • 1. Implement MLOps practices
  • 2. Update and retrain models
  • 3. Monitor performance and data drift
Design and prepare a machine learning solution20-25%- Manage Azure Machine Learning workspace
  • 1. Use developer tools and CLI
  • 2. Work with registries
  • 3. Create and configure workspace
  • 4. Set up Git integration
- Design a machine learning solution
  • 1. Select development approach
  • 2. Define compute specifications for workloads
  • 3. Plan model deployment requirements
  • 4. Determine dataset structure and format
- Manage data assets
  • 1. Select storage services
  • 2. Register and manage datastores
  • 3. Create and maintain data assets
- Manage compute resources
  • 1. Select environments
  • 2. Attach and monitor compute
  • 3. Create and configure compute targets
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
- Implement pipelines
  • 1. Schedule and monitor pipelines
  • 2. Pass data between steps
  • 3. Create and publish pipelines
  • 4. Build reusable components
- Run experiments
  • 1. Define parameters and configurations
  • 2. Configure experiment runs
  • 3. Use automated machine learning
  • 4. Track runs with MLflow

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q263-Q268):

NEW QUESTION # 263
You need to implement source control for scripts in an Azure Machine Learning workspace. You use a terminal window in the Azure Machine Learning Notebook tab You must authenticate your Git account with SSH.
You need to generate a new SSH key.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them m the correct order.

Answer:

Explanation:

1 - Run the ssh-keygen command.
2 - Press Enter when prompted to enter a file in which to save the key.
3 - Verify that the default location is '/home/azureuser/.ssh' and press Enter.
4 - Type a secure passphrase.


NEW QUESTION # 264
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.
An IT department creates the following Azure resource groups and resources:

The IT department creates an Azure Kubernetes Service (AKS)-based inference compute target named aks-cluster in the Azure Machine Learning workspace.
You have a Microsoft Surface Book computer with a GPU. Python 3.6 and Visual Studio Code are installed.
You need to run a script that trains a deep neural network (DNN) model and logs the loss and accuracy metrics.
Solution: Install the Azure ML SDK on the Surface Book. Run Python code to connect to the workspace and then run the training script as an experiment on local compute.

Answer: B

Explanation:
Explanation
Need to attach the mlvm virtual machine as a compute target in the Azure Machine Learning workspace.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target


NEW QUESTION # 265
You use Azure Machine Learning Designer lo load the following datasets into an experiment:
Dataset1:

Dataset2:

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 Add Rows component.
Does the solution meet the goal?

Answer: B


NEW QUESTION # 266
You configure a Deep Learning Virtual Machine for Windows.
You need to recommend tools and frameworks to perform the following:
* Build deep neural network (DNN) models
* Perform interactive data exploration and visualization
Which tools and frameworks should you recommend? To answer, drag the appropriate tools to the correct tasks. Each tool 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:
Box 1: Vowpal Wabbit
Use the Train Vowpal Wabbit Version 8 module in Azure Machine Learning Studio (classic), to create a machine learning model by using Vowpal Wabbit.
Box 2: PowerBI Desktop
Power BI Desktop is a powerful visual data exploration and interactive reporting tool BI is a name given to a modern approach to business decision making in which users are empowered to find, explore, and share insights from data across the enterprise.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/train-vowpal-wabbit-version-8-model
https://docs.microsoft.com/en-us/azure/architecture/data-guide/scenarios/interactive-data-exploration


NEW QUESTION # 267
You plan to preprocess text from CSV files. You load the Azure Machine Learning Studio default stop words list.
You need to configure the Preprocess Text module to meet the following requirements:
Ensure that multiple related words from a single canonical form.
Remove pipe characters from text.
Remove words to optimize information retrieval.
Which three options should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/preprocess-text


NEW QUESTION # 268
......

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