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

Topic	Details
Topic 1	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
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	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 5	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 6	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook

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Databricks Certified Machine Learning Professional Sample Questions (Q87-Q92):

NEW QUESTION # 87
Which MLflow operation can be used to log a plot that was generated during an MLflow run?

A. mlflow.log_metric
B. mlflow.log_figure
C. mlflow.log_visualization
D. None of these operations can accomplish the task.

Answer: B

Explanation:
The mlflow.log_figure() operation is used to log plots or figures generated during an MLflow run. It takes a Matplotlib, Plotly, or similar figure object and saves it as an artifact in the MLflow tracking server, enabling visualization and later retrieval from the MLflow UI.

NEW QUESTION # 88
Which deployment paradigm can centrally compute predictions for a single record with exceedingly fast results?

A. Streaming
B. None of these strategies will accomplish the task.
C. Real-time
D. Batch
E. Edge/on-device

Answer: C

NEW QUESTION # 89
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. Which of the following code blocks can they use to accomplish the task?

Answer: B

NEW QUESTION # 90
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 MLflow operation can the machine learning engineer use to accomplish this task?

A. The machine learning engineer needs to create an entirely new MLflow Model Registry model
B. MlflowClient.get_model_version
C. MlflowClient.update_registered_model
D. mlflow.register_model
E. mlflow.add_model_version

Answer: C

NEW QUESTION # 91
How can you save a trained Spark ML PipelineModel?

A. pipeline.store()
B. pipeline.persist()
C. pipeline.save()
D. pipelineModel.write().save()

Answer: D

Explanation:
Example:
pipelineModel.write().overwrite().save("/model")

NEW QUESTION # 92
......

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