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Microsoft DP-100 Exam Syllabus Topics:

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

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q63-Q68):

NEW QUESTION # 63
You define a datastore named ml-data for an Azure Storage blob container. In the container, you have a folder named train that contains a file named data.csv. You plan to use the file to train a model by using the Azure Machine Learning SDK.
You plan to train the model by using the Azure Machine Learning SDK to run an experiment on local compute.
You define a DataReference object by running the following code:

You need to load the training data.
Which code segment should you use?

Answer: D

Explanation:
Example:
data_folder = args.data_folder
# Load Train and Test data
train_data = pd.read_csv(os.path.join(data_folder, 'data.csv'))
Reference:
https://www.element61.be/en/resource/azure-machine-learning-services-complete-toolbox-ai


NEW QUESTION # 64
You create an Azure Machine Learning workspace.
You plan to write an Azure Machine Learning SDK for Python v2 script that logs an image for an experiment.
The logged image must be available from the images tab in Azure Machine Learning Studio.
You need to complete the script.
Which code segments should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 65
You load data from a notebook in an Azure Machine Learning workspace into a pandas dataframe named df.
The data contains 10.000 patient records. Each record includes the Age property for the corresponding patient.
You must identify the mean age value from the differentially private data generated by SmartNoise SDK.
You need to complete the Python code that will generate the mean age value from the differentially private data.
Which code segments should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 66
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 # 67
You are developing a hands-on workshop to introduce Docker for Windows to attendees.
You need to ensure that workshop attendees can install Docker on their devices.
Which two prerequisite components should attendees install on the devices? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

Answer: A,E

Explanation:
Explanation
C: Make sure your Windows system supports Hardware Virtualization Technology and that virtualization is enabled.
Ensure that hardware virtualization support is turned on in the BIOS settings. For example:

E: To run Docker, your machine must have a 64-bit operating system running Windows 7 or higher.
References:
https://docs.docker.com/toolbox/toolbox_install_windows/
https://blogs.technet.microsoft.com/canitpro/2015/09/08/step-by-step-enabling-hyper-v-for-use-on-windows-10/


NEW QUESTION # 68
......

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