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Databricks Certified Machine Learning Professional Exam
- Topic 1: Identify the requirements for tracking nested runs/ Describe an MLflow flavor and the benefits of using MLflow flavors
- 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: 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 4: 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 5: Describe the advantages of using the pyfunc MLflow flavor/ Manually log parameters, models, and evaluation metrics using MLflow
- Topic 6: Identify live serving benefits of querying precomputed batch predictions/ Describe Structured Streaming as a common processing tool for ETL pipelines
- Topic 7: Describe concept drift and its impact on model efficacy/ Describe summary statistic monitoring as a simple solution for numeric feature drift
- 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
- Topic 10: 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 11: Identify JIT feature values as a need for real-time deployment/ Describe how to list all webhooks and how to delete a webhook
- Topic 12: Describe model serving deploys and endpoint for every stage/ Identify scenarios in which feature drift and/or label drift are likely to occur
Free Databricks Databricks Certified Machine Learning Professional Exam Actual Questions
The questions for Databricks Certified Machine Learning Professional were last updated On Apr. 20, 2024
A machine learning engineer has created a webhook with the following code block:
Which of the following code blocks will trigger this webhook to run the associate job?
A)
B)
C)
D)
E)
A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client. At the same time, they would like to archive any model versions that are already in the Production stage.
Which of the following code blocks can they use to accomplish the task?
A)
B)
C)
D)
A machine learning engineer has created a webhook with the following code block:
Which of the following code blocks will trigger this webhook to run the associate job?
A)
B)
C)
D)
E)
A machine learning engineer is manually refreshing a model in an existing machine learning pipeline. The pipeline uses the MLflow Model Registry model "project". The machine learning engineer would like to add a new version of the model to "project".
Which of the following MLflow operations can the machine learning engineer use to accomplish this task?
A machine learning engineer has created a webhook with the following code block:
Which of the following code blocks will trigger this webhook to run the associate job?
A)
B)
C)
D)
E)
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