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

Topic	Details
Topic 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
Topic 2	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
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 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 concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 6	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 7	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 8	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 9	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 10	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 11	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

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Databricks Certified Machine Learning Professional Sample Questions (Q111-Q116):

NEW QUESTION # 111
A Machine Learning Engineer is responsible for maintaining a fraud detection model deployed on Databricks. They want to implement a retraining pipeline that automatically starts when the model's F1 score drops below a threshold or when input feature distributions change significantly.
Which two actions should the engineer take to implement this automated retraining? (Choose two.)

A. Configure these alerts to send webhook notifications that trigger the model training job.
B. Use MLflow to manually log metrics and retrain the model offline.
C. Set up a manual retraining schedule to run every week regardless of alerts.
D. Schedule a recurring query on the Lakehouse monitoring table.
E. Use Databricks SQL to create alerts on model performance and data drift metrics stored in Delta tables.

Answer: A,E

Explanation:
Databricks Lakehouse Monitoring stores model performance and data drift metrics in Delta tables, which can be monitored using Databricks SQL alerts. By creating alerts on F1 score degradation or significant feature drift and configuring those alerts to send webhook notifications, the engineer can automatically trigger a retraining job whenever predefined conditions are met, enabling event-driven, automated retraining aligned with MLOps best practices.

NEW QUESTION # 112
A machine learning engineer is using the following code block as part of a batch deployment pipeline:

Which of the following changes needs to be made so this code block will work when the inference table is a stream source?

A. Replace "inference" with the path to the location of the Delta table
B. Replace spark.read with spark.readStream
C. Replace schema(schema) with option("maxFilesPerTriqqer", 1}
D. Replace predict with a stream-friendly prediction function
E. Replace formatfdelta") with format("stream")

Answer: C

NEW QUESTION # 113
Which of the following statements about built-in library-specific MLflow Model flavors is true?

A. Built-in library-specific flavors allow models to be used with any library/span>
B. Built-in library-specific flavors allow models to be exported as library objects
C. Built-in library-specific flavors can only be used for logging models
D. Built-in library-specific flavors are required for model signature use

Answer: B

Explanation:
Built-in library-specific MLflow model flavors (e.g., mlflow.sklearn, mlflow.xgboost) allow models to be exported and later loaded as native library objects, enabling seamless reuse with the original libraries for inference or further training.

NEW QUESTION # 114
A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. They have programmatically identified the best run from an MLflow Experiment and stored its URI in the model_uri variable and its Run ID in the run_id variable. They have also determined that the model was logged with the name "model". Now, the machine learning engineer wants to register that model in the MLflow Model Registry with the name "best_model".
Which of the following lines of code can they use to register the model to the MLflow Model Registry?

A. mlflow.register_model(model_uri, "model")
B. mlflow.register_model(run_id, "best_model")
C. mlflow.register_model(f"runs:/{run_id}/model")
D. mlflow.register_model(model_uri, "best_model")
E. mlflow.register_model(f"runs:/{run_id}/best_model", "model")

Answer: A

NEW QUESTION # 115
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.
C. features_df.write.mode("fs").path("new_table")
D.
E. features_df.write.mode("feature").path("new_table")

Answer: D

NEW QUESTION # 116
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