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

Topic	Details
Topic 1	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 2	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 3	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 4	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 5	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 6	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 7	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 8	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 9	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 10	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

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Databricks Certified Machine Learning Professional Sample Questions (Q112-Q117):

NEW QUESTION # 112
A data scientist is utilizing MLflow to track their machine learning experiments. After completing a series of runs for the experiment with experiment ID exp_id, the data scientist wants to programmatically work with the experiment run data in a Spark DataFrame. They have an active MLflow Client client and an active Spark session spark. Which of the following lines of code can be used to obtain run-level results for exp_id in a Spark DataFrame?

A. client.list_run_infos(exp_id)
B. mlflow.search_runs(exp_id)
C. spark.read.format("delta").load(exp_id)
D. spark.read.format("mlflow-experiment").load(exp_id)
E. There is no way to programmatically return row-level results from an MLflow Experiment.

Answer: C

NEW QUESTION # 113
After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set. Which SQL command can be used to accomplish this task?

A. DESCRIBE HISTORY
B. VERSION
C. TIMESTAMP
D. HISTORY
E. DESCRIBE

Answer: A

NEW QUESTION # 114
A Machine Learning Engineer is considering moving their functions and unit tests from notebooks into separate Python files (e.g., modules and test scripts) to take advantage of the numerous benefits of this approach like automated execution, code reusability, and version control. Which challenge should the engineer consider with this approach?

A. It makes functions harder to import and reuse across different notebooks.
B. This change would prevent the use of any non-Python environments like Scala.
C. Managing a more complex project structure can be harder to maintain and navigate.
D. Separating code and tests often leads to decreased reliability and poor code quality.

Answer: C

Explanation:
Moving code and unit tests into separate Python modules introduces a more structured project layout, which can increase complexity. Engineers must manage directories, dependencies, and imports carefully, making the project slightly harder to navigate and maintain compared to simple notebook-based workflows, especially for teams new to this structure.

NEW QUESTION # 115
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 # 116
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 of the following changes does the team need to make to the above code block to accomplish the task?

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

Answer: A

NEW QUESTION # 117
......

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