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Microsoft DP-100 Exam Syllabus Topics:

SectionWeightObjectives
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. Pass data between steps
  • 2. Build reusable components
  • 3. Schedule and monitor pipelines
  • 4. Create and publish pipelines
- Run experiments
  • 1. Define parameters and configurations
  • 2. Configure experiment runs
  • 3. Track runs with MLflow
  • 4. Use automated machine learning
Design and prepare a machine learning solution20-25%- Manage Azure Machine Learning workspace
  • 1. Use developer tools and CLI
  • 2. Set up Git integration
  • 3. Create and configure workspace
  • 4. Work with registries
- Manage data assets
  • 1. Select storage services
  • 2. Register and manage datastores
  • 3. Create and maintain data assets
- Design a machine learning solution
  • 1. Plan model deployment requirements
  • 2. Define compute specifications for workloads
  • 3. Select development approach
  • 4. Determine dataset structure and format
- Manage compute resources
  • 1. Attach and monitor compute
  • 2. Create and configure compute targets
  • 3. Select environments
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. 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
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
- Monitor and maintain models
  • 1. Update and retrain models
  • 2. Monitor performance and data drift
  • 3. Implement MLOps practices
- Manage models
  • 1. Register and version models
  • 2. Interpret models and explain predictions
  • 3. Package and validate models
- Train models
  • 1. Configure jobs and environments
  • 2. Apply responsible AI principles
  • 3. Use HyperDrive for hyperparameter tuning
  • 4. Run training scripts

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q421-Q426):

NEW QUESTION # 421
You create and register a model in an Azure Machine Learning workspace.
You must use the Azure Machine Learning SDK to implement a batch inference pipeline that uses a ParallelRunStepto score input data using the model. You must specify a value for the ParallelRunConfig compute_targetsetting of the pipeline step.
You need to create the compute target.
Which class should you use?

Answer: C

Explanation:
Compute target to use for ParallelRunStep. This parameter may be specified as a compute target object or the string name of a compute target in the workspace.
The compute_target target is of AmlCompute or string.
Note: An Azure Machine Learning Compute (AmlCompute) is a managed-compute infrastructure that allows you to easily create a single or multi-node compute. The compute is created within your workspace region as a resource that can be shared with other users Reference:
https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-
steps/azureml.contrib.pipeline.steps.parallelrunconfig
https://docs.microsoft.com/en-us/python/api/azureml-
core/azureml.core.compute.amlcompute(class)


NEW QUESTION # 422
You arc I mating a deep learning model to identify cats and dogs. You have 25,000 color images.
You must meet the following requirements:
* Reduce the number of training epochs.
* Reduce the size of the neural network.
* Reduce over-fitting of the neural network.
You need to select the image modification values.
Which value should you use? To answer, select the appropriate Options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 423
You use Data Science Virtual Machines (DSVMs) for Windows and Linux in Azure.
You need to access the DSVMs.
Which utilities should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 424

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

Answer:

Explanation:


NEW QUESTION # 425
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.
You create an Azure Machine Learning service datastore in a workspace. The datastore contains the following files:
* /data/2018/Q1 .csv
* /data/2018/Q2.csv
* /data/2018/Q3.csv
* /data/2018/Q4.csv
* /data/2019/Q1.csv
All files store data in the following format:
id,M,f2,l
1,1,2,0
2,1,1,1
32,10
You run the following code:

You need to create a dataset named training_data and load the data from all files into a single data frame by using the following code:

Solution: Run the following code:

Does the solution meet the goal?

Answer: A


NEW QUESTION # 426
......

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