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

Topic	Details
Topic 1	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
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 that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
Topic 4	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 5	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 6	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
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 which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry

>> Databricks-Machine-Learning-Professional Exam Details <<

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Databricks Certified Machine Learning Professional Sample Questions (Q74-Q79):

NEW QUESTION # 74
A Machine Learning Engineer needs to build a time series model. In Databricks, they have created isolated environments in different workspaces for development, staging, and production.
To manage this model, they are planning on using a "deploy code" strategy. They are concerned that the model trained in development will not remain consistent across environments, due to differences in the dependencies installed. What can they do to ensure that the model is trained with the same packages?

A. Use Databricks Repos to store the model code and install the required libraries in the notebook.
B. Use MLflow to track the model parameters and metrics in development, then export the same training conditions in staging and production.
C. Use custom init scripts that install the necessary packages as part of the job.
D. Use the same Databricks Runtime (DBR) version across environments and define dependencies in a lock file that is installed as part of the job.

Answer: D

Explanation:
Using the same Databricks Runtime across environments ensures a consistent base environment, and installing dependencies from a lock file guarantees identical package versions during training. This combination follows best practices for deploy-code workflows by making the training environment reproducible and preventing dependency drift between development, staging, and production.

NEW QUESTION # 75
A Machine Learning Engineer wants to use on-demand features to train a model. They have a Python UDF, which relies on data fetched from a FeatureLookup. However, in online serving, the pipeline falls when the lookup ID is not found. Which solution will resolve this issue?

A. Log the model correctly via the FeatureEngineeringClient, so it automatically evaluates on- demand features.
B. Update the UDF so that it handles both NaNs and None values.
C. Add a default ID when defining the FeatureLookup, so it does not return None values.
D. Register the UDF to Unity Catalog, so None values are handled automatically.

Answer: B

Explanation:
When an on-demand feature relies on a FeatureLookup, missing lookup keys can result in None values during online serving. Updating the Python UDF to explicitly handle None and NaN values ensures the feature computation is robust to missing lookups, preventing runtime failures while preserving correct behavior during both training and online inference.

NEW QUESTION # 76
Which of the following is a probable response to identifying drift in a machine learning application?

A. All of these responses
B. Sunsetting the machine learning application
C. Rebuilding the machine learning application with a new label variable
D. Retraining and deploying a model on more recent data
E. None of these responses

Answer: E

NEW QUESTION # 77
Why is Delta Lake time travel useful in ML pipelines?

A. Model tuning
B. Reproducible training datasets
C. Smaller datasets
D. Faster model inference

Answer: B

Explanation:
Time travel allows training on exact historical datasets.

NEW QUESTION # 78
A machine learning engineer and data scientist are working together to convert a batch deployment to an always-on streaming deployment. The machine learning engineer has expressed that rigorous data tests must be put in place as a part of their conversion to account for potential changes in data formats.
Which of the following describes why these types of data type tests and checks are particularly important for streaming deployments?

A. Because the streaming deployment is always on, there is no practitioner to debug poor model performance
B. Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale
C. Because the streaming deployment is always on, all types of data must be handled without producing an error
D. All of these statements
E. None of these statements

Answer: B

NEW QUESTION # 79
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