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2. Train Models & Run Experiments (25-30%):

  • Model training process automation: The individuals need the relevant skills in running pipelines, passing data within steps in pipelines, monitoring pipeline runs, and creating pipelines with the use of SDK.
  • Training scripts run within Azure ML workspaces: The students should have the expertise in creating and running experiments utilizing Azure ML SDK as well configuring run settings for the scripts. This subject area also requires their skills in data consumption from datasets for an experiment using Azure ML SDK.
  • Models creation with Azure ML Designer: This domain covers the examinees’ skills in using custom code modules within the design and using designer modules for the definition of pipeline data flows. It also requires one’s competence in ingesting data within designer pipelines and creating training pipelines utilizing ML Designer.
  • Metrics generation from experiment runs: The candidates must be able to use logs for troubleshooting errors in experiment runs, log metrics from experiment run, and view and retrieve experiment outputs.

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4. Models Deployment and Consumption (20-25%):

  • Designer pipeline publishing as a web service: The candidates should show their knowledge of target compute resources creation, inference pipelines configuration, and deployed endpoints consuming.
  • Creation of pipelines for batch inferencing: This subject area covers your competence in running batch inferencing pipelines and obtaining outputs as well as publishing batch inferencing pipelines.
  • Model-as-a-Service deployment: This subtopic will measure the individuals’ expertise in configuring deployment settings, troubleshooting issues with deployment containers, and consuming deployed services.
  • Production computes targets creation: The test takers should perform their skills in compute options evaluation for deployment and consideration of security for deployed services.

Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx

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Certification Path

The Microsoft Certified Azure Data Scientist Associate Certification includes only one DP-100 Exam.

Step 1: Reviewing the Exam Topics

The best way to start the preparation process is to visit the official website of the certification exam. The official site is the most reliable source for the right information regarding the test. It is recommended that you download a detailed list of the exam objectives from the webpage and thoroughly study it before attempting the actual test.

Microsoft DP-100 Korean Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Design and prepare a machine learning solution20-25%- Prepare development environments
  • 1. Configure environments
  • 2. Use SDKs and notebooks
- Design an Azure Machine Learning workspace
  • 1. Configure workspace resources
  • 2. Configure security and access
  • 3. Manage compute resources
Topic 2: Deploy and retrain models10-15%- Monitor deployed models
  • 1. Monitor model performance
  • 2. Track data drift
- Implement retraining pipelines
  • 1. Create scheduled retraining workflows
  • 2. Manage ML pipelines
Topic 3: 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. Register models
  • 2. Create inference configurations
Topic 4: Explore data and train models35-40%- Prepare data for modeling
  • 1. Manage datasets and datastores
  • 2. Ingest and transform data
- Run experiments and train models
  • 1. Track experiments
  • 2. Use automated machine learning
  • 3. Perform hyperparameter tuning
- Optimize model performance
  • 1. Improve accuracy and performance
  • 2. Evaluate models

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