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

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Designing and Implementing a Data Science Solution on Azure (DP-100) practice test helps you to assess yourself as its tracker records all your results for future use. We design and update our Microsoft practice test questions after receiving feedback from professionals worldwide. There is no need for installation and any other plugins to access Microsoft DP-100 Practice Test. We also ensure that our support team and the core team of Microsoft Certified Professionals provide 24/7 services to resolve all your issues. There is a high probability that you will be successful in the Microsoft DP-100 exam on the first attempt after buying our prep material.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q482-Q487):

NEW QUESTION # 482
You need to replace the missing data in the AccessibilityToHighway columns.
How should you configure the Clean Missing Data module? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: Replace using MICE
Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method described in the statistical literature as "Multivariate Imputation using Chained Equations" or "Multiple Imputation by Chained Equations". With a multiple imputation method, each variable with missing data is modeled conditionally using the other variables in the data before filling in the missing values.
Scenario: The AccessibilityToHighway column in both datasets contains missing values. The missing data must be replaced with new data so that it is modeled conditionally using the other variables in the data before filling in the missing values.
Box 2: Propagate
Cols with all missing values indicate if columns of all missing values should be preserved in the output.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data


NEW QUESTION # 483
You create an Azure Machine learning workspace. The workspace contains a folder named src. The folder contains a Python script named script 1 .py.
You use the Azure Machine Learning Python SDK v2 to create a control script. You must use the control script to run script l.py as part of a training job.
You need to complete the section of script that defines the job parameters.
How should you complete the script? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 484
You have a dataset that contains over 150 features. You use the dataset to train a Support Vector Machine (SVM) binary classifier.
You need to use the Permutation Feature Importance module in Azure Machine Learning Studio to compute a set of feature importance scores for the dataset.
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:

1 - Add a Two-Class Support Vector Machine module to initialize the SVM classifier.
2 - Add a dataset to the experiment.
3 - Add a Split Data module to create training and test dataset.
4 - Add a Permutation Feature Importance module and connect to the trained model and test dataset.
5 - Set the Metric for measuring performance property to Classification - Accuracy and then run the experiment.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-support-vector-machine
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature-importance


NEW QUESTION # 485
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:

1 - conda create -n "python_env"
2 - conda activate "python_env"
3 - conda_install -r "requirements.txt"
4 - ipython kernel install...


NEW QUESTION # 486
You are working on a classification task. You have a dataset indicating whether a student would like to play soccer and associated attributes. The dataset includes the following columns:

You need to classify variables by type.
Which variable should you add to each category? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

References:
https://www.edureka.co/blog/classification-algorithms/


NEW QUESTION # 487
......

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