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

Topic	Details
Topic 1	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 2	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 3	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 4	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 5	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 6	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 7	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment

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Databricks Certified Machine Learning Professional Sample Questions (Q151-Q156):

NEW QUESTION # 151
A data scientist has developed a model to predict whether or not it will rain using the expected temperature and expected cloud coverage. However, the proportion of days where it actually rains has increased dramatically from the proportion in the data on which the model was trained.
Which type of drift is present in the above scenario?

A. Label drift
B. Concept drift
C. Feature drift
D. Prediction drift

Answer: A

Explanation:
Label drift occurs when the distribution of the target variable (label) changes over time while the relationship between features and the label remains the same. In this scenario, the proportion of days when it rains (the label) has changed significantly compared to the training data, indicating label drift.

NEW QUESTION # 152
A data scientist has developed a scikit-learn random forest model model, but they have not yet logged model with MLflow. They want to obtain the input schema and the output schema of the model so they can document what type of data is expected as input.
Which of the following MLflow operations can be used to perform this task?

A. mlflow.models.signature.infer_signature
B. mlflow.models.Model.get_input_schema
C. There is no way to obtain the input schema and the output schema of an unlogged model.
D. mlflow.models.Model.signature
E. mlflow.models.schema.infer_schema

Answer: E

NEW QUESTION # 153
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 predict with a stream-friendly prediction function
B. Replace spark.read with spark.readStream
C. Replace schema(schema) with option("maxFilesPerTriqqer", 1}
D. Replace "inference" with the path to the location of the Delta table
E. Replace formatfdelta") with format("stream")

Answer: C

NEW QUESTION # 154
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. Set up a manual retraining schedule to run every week regardless of alerts.
C. Use MLflow to manually log metrics and retrain the model offline.
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 # 155
A machine learning engineer is converting a Hyperopt-based hyperparameter tuning process from manual MLflow logging to MLflow Autologging. They notice that not all details and objects are automatically logged, and they will need to manually log some things. Which of the following will need to be manually logged when performing nested runs with Hyperopt and MLflow Autologging?

A. Trial models
B. Evaluation metrics
C. Hyperparameter values
D. Trial status
E. Best trial evaluation metric

Answer: E

Explanation:
When using MLflow Autologging with Hyperopt and nested runs, the best trial evaluation metric is not automatically logged and must be logged manually. Autologging captures trial-level details like hyperparameters and evaluation metrics, but summarizing and logging the overall best trial's result is a manual responsibility of the engineer.

NEW QUESTION # 156
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