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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:
| Topic | Details |
|---|
| Topic 1 | - 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 2 | - 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 3 | - 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 4 | - Describe model serving deploys and endpoint for every stage
- Identify scenarios in which feature drift and
- or label drift are likely to occur
|
| Topic 5 | - Identify live serving benefits of querying precomputed batch predictions
- Describe Structured Streaming as a common processing tool for ETL pipelines
|
| Topic 6 | - 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 7 | - Describe the advantages of using the pyfunc MLflow flavor
- Manually log parameters, models, and evaluation metrics using MLflow
|
| Topic 8 | - Identify which code block will trigger a shown webhook
- Describe the basic purpose and user interactions with Model Registry
|
| Topic 9 | - Identify less performant data storage as a solution for other use cases
- Describe why complex business logic must be handled in streaming deployments
|
Reference: https://www.databricks.com/learn/certification/machine-learning-professional
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