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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	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 4	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 5	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
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

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Quiz 2026 Databricks-Machine-Learning-Professional: Latest Databricks Certified Machine Learning Professional New Study Guide

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Databricks Certified Machine Learning Professional Sample Questions (Q50-Q55):

NEW QUESTION # 50
A Machine Learning Engineer needs to deploy a production ML workflow that includes an MLflow experiment for tracking model training runs, a registered model in Unity Catalog for version management, and a model serving endpoint for real-time inference. The team requires a unified configuration approach that ensures consistent deployment across development and production environments while adhering to infrastructure-as-code best practices. Which approach should the Machine Learning Engineer use to define all three components together?

A. Use Terraform to provision the infrastructure and then manually configure each ML component through the Databricks UI.
B. Define experiments, registered_models, and model_serving_endpoints resources in a Databricks Asset Bundle (DAB) configuration file.
C. Use MLflow's deployment tools to create individual deployment configurations for each component, then orchestrate them using Databricks Jobs.
D. Create separate REST API calls for each component and run them sequentially using a shell script.

Answer: B

Explanation:
Databricks Asset Bundles allow experiments, Unity Catalog-registered models, and model serving endpoints to be defined declaratively in a single configuration. This provides a unified, version-controlled, infrastructure-as-code approach that ensures consistent deployment across environments and aligns with MLOps best practices.

NEW QUESTION # 51
Which of the following can be used to compare the relative prevalence of specific values in a single categorical variable between two datasets or time periods?

A. Number of unique values
B. Mode
C. Jensen-Shannon distance
D. Contingency tables

Answer: D

Explanation:
Contingency tables (also known as cross-tabulations) display the frequency distribution of categorical variables, allowing comparison of how specific category values occur across two datasets or time periods. This makes them effective for identifying categorical feature drift or shifts in value prevalence.

NEW QUESTION # 52
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 a fake dataset.
B. Implement an integration test that runs the feature engineering pipeline in the production environment.
C. Implement an integration test that runs the entire pipeline from feature engineering to model inference in the production environment.
D. Add a unit test that verifies the new mutual friends feature function correctly calculates expected values on live data.

Answer: A

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 # 53
Which of the following is an obstacle related to streaming machine learning applications?

A. End-to-end fault tolerance
B. Out-of-order data
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 # 54
A machine learning engineering manager has asked all of the engineers on their team to add text descriptions to each of the model projects in the MLflow Model Registry. They are starting with the model project "model" and they'd like to add the text in the model_description variable.
The team is using the following line of code:

Which change does the team need to make to the above code block to accomplish the task?

A. Add a Python model as an argument to update_registered_model
B. Replace update_registered_model with update_model_version
C. Replace client.update_registered_model with mlflow
D. There no changes necessary
E. Replace description with artifact

Answer: D

NEW QUESTION # 55
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