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Databricks Databricks-Machine-Learning-Professional Prüfungsplan:

Thema	Einzelheiten
Thema 1	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
Thema 2	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Thema 3	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
Thema 4	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Thema 5	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Thema 6	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Thema 7	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments

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Databricks-Machine-Learning-Professional Prüfungen - Databricks-Machine-Learning-Professional Zertifizierungsantworten

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Databricks Certified Machine Learning Professional Databricks-Machine-Learning-Professional Prüfungsfragen mit Lösungen (Q135-Q140):

135. Frage
Which component manages model versions?

A. Spark ML
B. MLflow Tracking
C. Model Registry
D. Feature Store

Antwort: C

Begründung:
Model Registry handles:
versioning
stage transitions
governance.

136. Frage
A data scientist is utilizing MLflow to track their machine learning experiments. After completing a series of runs for the experiment with experiment ID exp_id, the data scientist wants to programmatically work with the experiment run data in a Spark DataFrame. They have an active MLflow Client client and an active Spark session spark.
Which of the following lines of code can be used to obtain run-level results for exp_id in a Spark DataFrame?

A. spark.read.format("delta").load(exp_id)
B. spark.read.format("mlflow-experiment").load(exp_id)
C. There is no way to programmatically return row-level results from an MLflow Experiment.
D. client.list_run_infos(exp_id)
E. mlflow.search_runs(exp_id)

Antwort: A

137. Frage
A machine learning engineer has registered a Spark ML model in the MLflow Model Registry using the Spark ML model flavor with UI model_uri. Which operation can be used to load the model as a Spark ML object for batch deployment?

A. mlflow.sklearn.load_model(model_uri)
B. mlflow.spark.load_model(model_uri)
C. mlflow.pyfunc.load_model(model_uri)
D. mlflow.spark.read_model(model_uri)

Antwort: B

Begründung:
To load a model that was logged using the Spark ML flavor, the correct operation is mlflow.spark.load_model(model_uri). This restores the model as a Spark ML object, preserving its original structure and functionality for batch inference or further processing in Spark environments.

138. Frage
A data scientist has created a Python function compute_features that returns a Spark DataFrame with the following schema:

The resulting DataFrame is assigned to the features_df variable. The data scientist wants to create a Feature Store table using features_df.
Which of the following code blocks can they use to create and populate the Feature Store table using the Feature Store Client fs?

A.
B. features_df.write.mode("fs").path("new_table")
C.
D. features_df.write.mode("feature").path("new_table")
E.

Antwort: A

139. Frage
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 code block can they use to accomplish the task?

Antwort: A

Begründung:
The correct method for transitioning a model version to a new stage using the MLflow Client is client.transition_model_version_stage(name, version, stage). This updates the stage of a specific model version in the Model Registry, such as moving it from "Staging" to "Production". The stage parameter specifies the target stage only -- there is no from argument.

140. Frage
......

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