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

Topic	Details
Topic 1	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 2	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 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
Topic 4	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 5	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 6	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 7	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 8	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 9	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

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Databricks Certified Machine Learning Professional Sample Questions (Q132-Q137):

NEW QUESTION # 132
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 predict with a stream-friendly prediction function
C. Replace formatfdelta") with format("stream")
D. Replace schema(schema) with option("maxFilesPerTriqqer", 1}
E. Replace spark.read with spark.readStream

Answer: D

NEW QUESTION # 133
A machine learning engineer is attempting to create a webhook that will trigger a Databricks Job job_id when a model version for model model transitions into any MLflow Model Registry stage.
They have the following incomplete code block:

Which of the following lines of code can be used to fill in the blank so that the code block accomplishes the task?

A. "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"
B. "MODEL_VERSION_TRANSITIONED_STAGE"
C. "MODEL_VERSION_TRANSITIONED_TO_STAGING"
D. "MODEL_VERSION_TRANSITIONED_TO_STAGING", "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"
E. "MODEL_VERSION_CREATED"

Answer: C

NEW QUESTION # 134
Which of the following deployment paradigms can centrally compute predictions for a single record with exceedingly fast results?

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

Answer: A

NEW QUESTION # 135
Feature drift occurs when there is a change in which element?

A. The distribution of the predicted target given by the model
B. The distribution of a target variable
C. The relationship between input variables and target variables
D. The distribution of an input variable

Answer: D

Explanation:
Feature drift refers to a change in the distribution of one or more input features over time, even if the target variable and model relationship remain stable. This can degrade model performance because the model was trained on a different feature distribution than it encounters during inference.

NEW QUESTION # 136
A Data Scientist is training a complex gradient-boosted model for fraud detection. The model uses dynamic threshold tuning during training and generates custom visualizations of feature drift. To ensure reproducibility and collaboration, they need to programmatically track:
- Custom metrics (e g., adjusted_f1 for threshold variations)
- Hyperparameters from nested configuration files
- Drift visualization plots as PDFs
Which approach implements this tracking in MLflow?

A. Store hyperparameters in a YAML file, save metrics to a CSV, and use mlflow.log_artifact() to upload both to the tracking server as artifacts.
B. Use mlflow.log_metric("adjusted_f1", value),mlflow.log_params(nested_config), and mlflow.log_artifact("drift_plot.pdf") within an active MLflow run context, while enabling mlflow.autolog() to capture model-specific metadata automatically.
C. Enable mlflow.autolog() at the beginning of the code and rely on it to automatically detect and log custom metrics, parameters, and visualization files.
D. Write all custom metrics and parameters to stdout during training and configure MLflow's tracking server to scrape these outputs.

Answer: B

Explanation:
Explicitly using mlflow.log_metric, mlflow.log_params, and mlflow.log_artifact within an active MLflow run provides precise, programmatic control over tracking custom metrics, complex hyperparameter configurations, and generated artifacts such as PDF visualizations. Enabling mlflow.autolog alongside this captures standard model metadata automatically while still allowing full flexibility for advanced, custom tracking needs.

NEW QUESTION # 137
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