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Microsoft DP-100 Exam Syllabus Topics:

SectionWeightObjectives
Deploy and retrain models10-15%- Monitor deployed models
  • 1. Track data drift
  • 2. Monitor model performance
- Implement retraining pipelines
  • 1. Create scheduled retraining workflows
  • 2. Manage ML pipelines
Prepare a model for deployment20-25%- Deploy machine learning models
  • 1. Deploy batch inference pipelines
  • 2. Deploy real-time inference endpoints
- Manage deployment assets
  • 1. Create inference configurations
  • 2. Register models
Explore data and train models35-40%- Run experiments and train models
  • 1. Track experiments
  • 2. Perform hyperparameter tuning
  • 3. Use automated machine learning
- Optimize model performance
  • 1. Evaluate models
  • 2. Improve accuracy and performance
- Prepare data for modeling
  • 1. Manage datasets and datastores
  • 2. Ingest and transform data
Design and prepare a machine learning solution20-25%- Prepare development environments
  • 1. Use SDKs and notebooks
  • 2. Configure environments
- Design an Azure Machine Learning workspace
  • 1. Configure workspace resources
  • 2. Manage compute resources
  • 3. Configure security and access

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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:

Explanation:
Box 1: init()
The entry script has only two required functions, init() and run(data). These functions are used to initialize the service at startup and run the model using request data passed in by a client. The rest of the script handles loading and running the model(s).
Box 2: run()
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-existing-model


NEW QUESTION # 411

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 # 412
You create an Azure Machine Learning workspace. You train an MLflow-formatted regression model by using tabular structured data.
You must use a Responsible Al dashboard to assess the model.
You need to use the Azure Machine Learning studio Ul to generate the Responsible A dashboard.
What should you do first?

Answer: B


NEW QUESTION # 413
You need to define an evaluation strategy for the crowd sentiment models.
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:

Explanation

Scenario:
Experiments for local crowd sentiment models must combine local penalty detection data.
Crowd sentiment models must identify known sounds such as cheers and known catch phrases. Individual crowd sentiment models will detect similar sounds.
Note: Evaluate the changed in correlation between model error rate and centroid distance In machine learning, a nearest centroid classifier or nearest prototype classifier is a classification model that assigns to observations the label of the class of training samples whose mean (centroid) is closest to the observation.
References:
https://en.wikipedia.org/wiki/Nearest_centroid_classifier
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/sweep-clustering


NEW QUESTION # 414
You create a multi-class image classification deep learning experiment by using the PyTorch framework. You plan to run the experiment on an Azure Compute cluster that has nodes with GPU's.
You need to define an Azure Machine Learning service pipeline to perform the monthly retraining of the image classification model. The pipeline must run with minimal cost and minimize the time required to train the model.
Which three pipeline steps should you run 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:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-pytorch


NEW QUESTION # 415
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