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

Topic	Details
Topic 1	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 2	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
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 that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
Topic 5	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 6	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow

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Databricks Certified Machine Learning Professional Sample Questions (Q169-Q174):

NEW QUESTION # 169
A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client. Which code block can they use to accomplish the task?

Answer: D

Explanation:
The correct method for transitioning a model version to a new stage using the MLflow Client is client.transition_model_version_stage(name, version, stage). This updates the stage of a specific model version in the Model Registry, such as moving it from "Staging" to "Production". The stage parameter specifies the target stage only -- there is no from argument.

NEW QUESTION # 170
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 lines of code can be used to fill in the blank so that the code block accomplishes the task?

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

Answer: E

NEW QUESTION # 171
A machine learning engineer is monitoring categorical input variables for a production machine learning application. The engineer believes that missing values are becoming more prevalent in more recent data for a particular value in one of the categorical input variables. Which of the following tools can the machine learning engineer use to assess their theory?

A. Kolmogorov-Smirnov (KS) test
B. One-way Chi-squared Test
C. Jenson-Shannon distance
D. Two-way Chi-squared Test
E. None of these

Answer: B

NEW QUESTION # 172
Which of the following is an obstacle related to streaming machine learning applications?

A. Out-of-order data
B. End-to-end fault tolerance
C. None of these
D. All of these

Answer: D

Explanation:
Streaming machine learning applications face multiple challenges, including end-to-end fault tolerance (ensuring recovery from failures without data loss) and out-of-order data (handling events that arrive late or out of sequence). Both are common obstacles in building reliable real- time ML systems.

NEW QUESTION # 173
A Machine Learning Engineer has deployed a customer churn prediction model to production three months ago. The model serves real-time predictions via a Databricks endpoint with inference logging enabled. They notice declining model accuracy in recent weeks and suspect data drift in customer demographics. They need to implement monitoring to track model performance degradation and input feature drift over time. Which monitoring profile type should they use?

A. Custom profile - Create a hybrid monitoring approach combining snapshot and time series profiles to capture both baseline comparisons and temporal trends.
B. Time series profile - Monitor the inference table using timestamp-based windows to track model performance and input drift over time.
C. Snapshot profile - Monitor the complete inference table to compare current model performance against the baseline established at deployment.
D. Inference profile - Monitor the inference table to track model performance metrics, prediction drift, and input feature drift across time windows.

Answer: D

Explanation:
The inference profile is designed specifically to monitor production model behavior using inference logs. It tracks model performance metrics, prediction distributions, and input feature drift across time windows, enabling detection of performance degradation and demographic data drift after deployment.

NEW QUESTION # 174
......

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