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Microsoft DP-100 Exam Syllabus Topics:

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

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Quiz Microsoft - DP-100 - High-quality Best Designing and Implementing a Data Science Solution on Azure Preparation Materials

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q410-Q415):

NEW QUESTION # 410
You use Azure Machine Learning to deploy a model as a real-time web service.
You need to create an entry script for the service that ensures that the model is loaded when the service starts and is used to score new data as it is received.
Which functions should you include in the script? To answer, drag the appropriate functions to the correct actions. Each function 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:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-existing-model


NEW QUESTION # 411
You are performing a classification task in Azure Machine Learning Studio.
You must prepare balanced testing and training samples based on a provided data set.
You need to split the data with a 0.75:0.25 ratio.
Which value should you use for each parameter? 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/split-data


NEW QUESTION # 412
You create a Python script named train.py and save it in a folder named scripts. The script uses the scikit-learn framework to train a machine learning model.
You must run the script as an Azure Machine Learning experiment on your local workstation.
You need to write Python code to initiate an experiment that runs the train.py script.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation
Graphical user interface, text, application, table, Word Description automatically generated

Box 1: source_directory
source_directory: A local directory containing code files needed for a run.
Box 2: script
Script: The file path relative to the source_directory of the script to be run.
Box 3: environment
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.scriptrunconfig


NEW QUESTION # 413
You create a multi-class image classification deep learning model.
The model must be retrained monthly with the new image data fetched from a public web portal. You create an Azure Machine Learning pipeline to fetch new data, standardize the size of images, and retrain the model.
You need to use the Azure Machine Learning SDK to configure the schedule for the pipeline.
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 in the correct order.

Answer:

Explanation:

Explanation:

Step 1: Publish the pipeline.
To schedule a pipeline, you'll need a reference to your workspace, the identifier of your published pipeline, and the name of the experiment in which you wish to create the schedule.
Step 2: Retrieve the pipeline ID.
Needed for the schedule.
Step 3: Create a ScheduleRecurrence..
To run a pipeline on a recurring basis, you'll create a schedule. A Schedule associates a pipeline, an experiment, and a trigger.
First create a schedule. Example: Create a Schedule that begins a run every 15 minutes:
recurrence = ScheduleRecurrence(frequency="Minute", interval=15)
Step 4: Define an Azure Machine Learning pipeline schedule..
Example, continued:
recurring_schedule = Schedule.create(ws, name="MyRecurringSchedule",
description="Based on time",
pipeline_id=pipeline_id,
experiment_name=experiment_name,
recurrence=recurrence)
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-schedule-pipelines


NEW QUESTION # 414
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 # 415
......

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