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

Topic	Details
Topic 1	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 2	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 3	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 4	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 5	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 6	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors

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Databricks Certified Machine Learning Professional Sample Questions (Q105-Q110):

NEW QUESTION # 105
A Machine Learning Engineer has trained a credit scoring model and needs to evaluate fairness metrics across different customer segments while maintaining different levels of granularity for business reporting. They need to compute metrics like precision, recall, and demographic parity at the individual feature level (credit_score_range, income_bracket) as well as intersectional slices (combinations of features). The model outputs are stored in a Delta table with prediction probabilities and actual default labels. The engineer wants to systematically evaluate model performance across these various feature slices and granularities. Which approach will do this?

A. Create Lakehouse Monitoring with slicing expressions for individual features and intersection conditions.
B. Build separate Spark DataFrames for each slice and compute metrics using standard DataFrame operations.
C. Implement custom MLflow evaluation functions that iterate through all possible feature combinations.
D. Use Unity Catalog metric views with dimensions defined for each feature and measures for the fairness metrics.

Answer: A

Explanation:
Lakehouse Monitoring supports defining slicing expressions on one or more columns, allowing metrics such as precision, recall, and fairness indicators to be computed at both individual feature levels and intersectional combinations. This provides a systematic, scalable, and governed way to evaluate model performance and fairness across multiple granularities directly from Delta tables without custom metric pipelines.

NEW QUESTION # 106
A machine learning engineer is migrating a machine learning pipeline to use Data bricks Machine Learning. The pipeline needs to automatically refresh its model each time it runs.
They are using the following code block as part of their solution:

Assuming that this is the first time that the model is being run, which statement describes the impact of the registered_model_name=model_name parameter?

A. It registers the new version of the existing model_name model in the MLflow Model Registry.
B. It registers a new model called model_name in the MLflow Model Registry.
C. It avoids the need to specify the model name in the subsequent required call to mflow.register_model.
D. It identifies the name of the logged model in the MLflow Experiment.

Answer: B

Explanation:
The parameter registered_model_name=model_name in mlflow.pyfunc.log_model() automatically creates and registers a new model in the MLflow Model Registry if it doesn't already exist. Since this is the first time the model is being run, MLflow will register a new model named model_name and log the current model as version 1 of that registered model.

NEW QUESTION # 107
A Machine Learning Engineer needs a continuous deployment pipeline for their models hosted on Databricks Model Serving. The deployment automation should execute after a model is trained and registered using MLflow. The goal of the automation is to deploy the latest version of the model from the MLflow Model Registry only if the model can meet the company's strict latency requirements (P95 < 300ms) while serving production traffic. How can the engineer validate that new models meet their latency requirements when served in production?

A. Use the MLflow Get Run API to retrieve the model metrics from MLflow Tracking and verify that the inference_latency metric is less than 300ms.
B. Use the MLflow Get Run API to retrieve the model metrics from MLflow Tracking and verify that the model_latency metric is less than 300ms.
C. A/B test the latest model with Databricks Model Serving so that the latest model receives 5% of production traffic and the current model receives the rest. Use inference tables to calculate P95 latency and verify it is less than 300ms.
D. Serve the latest model on Databricks Model Serving and use a load testing client to generate requests at the production request rate. Use the load testing client to calculate P95 latency and verify it is less than 300ms.

Answer: C

Explanation:
Validating latency requirements must be done under real production serving conditions. Routing a small percentage of live production traffic to the new model using Databricks Model Serving allows accurate measurement of end-to-end serving latency. Inference tables capture request latency metrics, enabling calculation of the P95 latency and ensuring it meets the strict production threshold before full rollout.

NEW QUESTION # 108
A machine learning engineer has registered a sklearn model in the MLflow Model Registry using the sklearn model flavor with UI model_uri. Which operation can be used to load the model as an sklearn object for batch deployment?

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

Answer: B

NEW QUESTION # 109
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. Prediction drift
C. Concept drift
D. Feature 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 # 110
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