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Microsoft DP-100 Exam Syllabus Topics:

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

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q104-Q109):

NEW QUESTION # 104
You manage an Azure Machine Learning workspace. The development environment is configured with a Serverless Spark compute in Azure Machine Learning Notebooks.
You perform interactive data wrangling to clean up the Titanic dataset and store it as a new dataset (Line numbers are used for reference only.)

For each of the following statements, select Yes if the statement is true Otherwise, select No NOTE: Bach correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 105
You have an Azure Machine Learning workspace that contains a training cluster and an inference cluster.
You plan to create a classification model by using the Azure Machine Learning designer.
You need to ensure that client applications can submit data as HTTP requests and receive predictions as responses.
Which three 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:

1 - Create a pipeline that trains a classification model and run the pipeline on the compute cluster.
2 - Create a batch inference pipeline and run the pipeline on the compute cluster.
3 - Deploy a service to the inference cluster.


NEW QUESTION # 106
You have an Azure Machine Learning workspace. You are running an experiment on your local computer.
You need to use MLflow Tracking to store metrics and artifacts from your local experiment runs in the workspace.
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:

Explanation


NEW QUESTION # 107
You manage an Azure Machine Learning workspace. You create an experiment named experiment1 by using the Azure Machine Learning Python SDK v2 and MLflow.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

Answer:

Explanation:

Explanation:


NEW QUESTION # 108
You register a model that you plan to use in a batch inference pipeline.
The batch inference pipeline must use a ParallelRunStep step to process files in a file dataset. The script has the ParallelRunStep step runs must process six input files each time the inferencing function is called.
You need to configure the pipeline.
Which configuration setting should you specify in the ParallelRunConfig object for the PrallelRunStep step?

Answer: B

Explanation:
node_count is the number of nodes in the compute target used for running the ParallelRunStep.
Incorrect Answers:
A: process_count_per_node
Number of processes executed on each node. (optional, default value is number of cores on node.) C: mini_batch_size For FileDataset input, this field is the number of files user script can process in one run() call. For TabularDataset input, this field is the approximate size of data the user script can process in one run() call.
Example values are 1024, 1024KB, 10MB, and 1GB.
D: error_threshold
The number of record failures for TabularDataset and file failures for FileDataset that should be ignored during processing. If the error count goes above this value, then the job will be aborted.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/ azureml.contrib.pipeline.steps.parallelrunconfig?view=azure-ml-py


NEW QUESTION # 109
......

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