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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:

TopicDetails
Topic 1
  • Describe model serving deploys and endpoint for every stage
  • Identify scenarios in which feature drift and
  • or label drift are likely to occur
Topic 2
  • Test whether the updated model performs better on the more recent data
  • Identify when retraining and deploying an updated model is a probable solution to drift
Topic 3
  • Identify JIT feature values as a need for real-time deployment
  • Describe how to list all webhooks and how to delete a webhook
Topic 4
  • Identify that data can arrive out-of-order with structured streaming
  • Identify how model serving uses one all-purpose cluster for a model deployment
Topic 5
  • Identify a use case for HTTP webhooks and where the Webhook URL needs to come
  • Identify advantages of using Job clusters over all-purpose clusters
Topic 6
  • Describe concept drift and its impact on model efficacy
  • Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 7
  • Identify which code block will trigger a shown webhook
  • Describe the basic purpose and user interactions with Model Registry
Topic 8
  • Describe the advantages of using the pyfunc MLflow flavor
  • Manually log parameters, models, and evaluation metrics using MLflow
Topic 9
  • Create, overwrite, merge, and read Feature Store tables in machine learning workflows
  • View Delta table history and load a previous version of a Delta table
Topic 10
  • Identify the requirements for tracking nested runs
  • Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 11
  • Identify live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines

Reference: https://www.databricks.com/learn/certification/machine-learning-professional

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