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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	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 3	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 4	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 5	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 6	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 7	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
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	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 10	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry

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Databricks Certified Machine Learning Professional Sample Questions (Q130-Q135):

NEW QUESTION # 130
A Machine Learning Engineer wants to deploy a new change to their existing model training pipeline for a social media app. Currently, they use the Databricks Feature Store to create a training set to train their LightGBM model, which is then used in a Pandas UDF for batch inference to suggest potential friends to users. They have updated the feature engineering pipeline to include an additional feature function, which now computes the number of mutual friends a user has. Which test should they add to quickly inform them if something has broken in the test environment?

A. Add a unit test that verifies the new mutual friends feature function correctly calculates expected values on live data.
B. Add a unit test that verifies the new mutual friends feature function correctly calculates expected values on a fake dataset.
C. Implement an integration test that runs the feature engineering pipeline in the production environment.
D. Implement an integration test that runs the entire pipeline from feature engineering to model inference in the production environment.

Answer: B

Explanation:
A unit test targeting the new feature function provides the fastest and most reliable signal that the recent change behaves correctly. By validating the mutual friends calculation on a controlled, fake dataset in the test environment, the engineer can quickly detect logic errors without the cost, risk, or latency of running full pipeline or production-based tests.

NEW QUESTION # 131
In order to connect an MLflow Model Registry Webhook to a Databricks Job, the Job ID must be provided to the code block used to create the webhook. Which approach can be used to obtain a Databricks Job ID?

A. The Job ID field in the Job details section of the specific Job page in the UI
B. All of these approaches
C. The Job ID field returned in a query using the Jobs API
D. The Job ID field in the Jobs page in the UI

Answer: B

Explanation:
A Databricks Job ID can be obtained in multiple ways - it is displayed directly in the Jobs page, in the Job details section of a specific Job, and can also be retrieved programmatically through the Databricks Jobs API. Any of these methods can be used to supply the Job ID when configuring an MLflow Model Registry Webhook.

NEW QUESTION # 132
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. Feature drift
C. Concept 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 # 133
A data scientist has written a function to track the runs of their random forest model. The data scientist is changing the number of trees in the forest across each run. Which of the following MLflow operations is designed to log single values like the number of trees in a random forest?

A. mlflow.log_artifact
B. There is no way to store values like this.
C. mlflow.log_model
D. mlflow.log_metric
E. mlflow.log_param

Answer: D

NEW QUESTION # 134
A machine learning engineer is in the process of implementing a concept drift monitoring solution.
They are planning to use the following steps:
1. Deploy a model to production and compute predicted values
2. Obtain the observed (actual) label values
3. _____
4. Run a statistical test to determine if there are changes over time
Which of the following should be completed as Step #3?

A. Compute the evaluation metric using the observed and predicted values
B. Retrain the model
C. Obtain the observed values (actual) feature values
D. None of these should be completed as Step #3
E. Measure the latency of the prediction time

Answer: D

NEW QUESTION # 135
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