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

Topic	Details
Topic 1	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 2	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 3	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
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 the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 6	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
Topic 7	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

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Databricks Certified Machine Learning Professional Sample Questions (Q64-Q69):

NEW QUESTION # 64
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 custom init scripts that install the necessary packages as part of the job.
B. Use Databricks Repos to store the model code and install the required libraries in the notebook.
C. Use the same Databricks Runtime (DBR) version across environments and define dependencies in a lock file that is installed as part of the job.
D. Use MLflow to track the model parameters and metrics in development, then export the same training conditions in staging and production.

Answer: C

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 # 65
Which of the following machine learning model deployment paradigms is the most common for machine learning projects?

A. Real-time
B. None of these deployments
C. Streaming
D. Batch
E. On-device

Answer: C

NEW QUESTION # 66
A Machine Learning Engineer has created a custom PyFunc model wrapper for a fraud detection system that needs to be registered in Unity Catalog under the schema risk_models in the production catalog. The model requires specific dependencies and must be accessible for governance and version control. The engineer is working on a dedicated cluster with Unity Catalog enabled, but the model registration is failing. Why is the model failing to be registered in this case?

A. The PyFunc model must inherit from mlflow.sklearn.Model instead of mlflow.pyfunc.PythonModel.
B. The model signature must be provided, as Unity Catalog requires all models to have signatures.
C. The cluster must be configured with shared access mode instead of dedicated mode.
D. The model artifacts must be stored in DBFS rather than Unity Catalog volumes.

Answer: C

Explanation:
Unity Catalog model registration requires the cluster to run in shared access mode. Dedicated (single-user) clusters do not support registering models into Unity Catalog because governance, lineage, and access control enforcement rely on shared-mode execution. As a result, even if Unity Catalog is enabled, model registration will fail when attempted from a dedicated cluster.

NEW QUESTION # 67
A Machine Learning Engineer is building an application that requires low latency data lookups in response to a user's question following a RAG based search. They want to ensure their users can receive as recent data as possible for urgent requests, so data should not be more than a few minutes late. The underlying data is a large table that may contain hundreds of gigabytes of data. Which data serving approach will suit their use case?

A. A fast database hosted in MLflow model serving
B. Online tables with triggered mode and a time series key
C. Online tables with continuous sync mode
D. Online tables with snapshot sync mode

Answer: C

Explanation:
Online tables with continuous sync mode are designed for low-latency serving while keeping data fresh within minutes. Continuous sync incrementally propagates updates from the large underlying table to the online store, ensuring near-real-time availability for RAG-based lookups without requiring full refreshes, which is ideal for urgent, freshness-sensitive queries on large datasets.

NEW QUESTION # 68
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_metric
D. mlflow.log_param
E. mlflow.log_model

Answer: C

NEW QUESTION # 69
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