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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:
| Topic | Details |
|---|
| Topic 1 | - Describe concept drift and its impact on model efficacy
- Describe summary statistic monitoring as a simple solution for numeric feature drift
|
| Topic 2 | - Identify live serving benefits of querying precomputed batch predictions
- Describe Structured Streaming as a common processing tool for ETL pipelines
|
| Topic 3 | - Describe the advantages of using the pyfunc MLflow flavor
- Manually log parameters, models, and evaluation metrics using MLflow
|
| 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 | - Identify the requirements for tracking nested runs
- Describe an MLflow flavor and the benefits of using MLflow flavors
|
Reference: https://www.databricks.com/learn/certification/machine-learning-professional