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Databricks Databricks-Machine-Learning-Professional Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Machine Learning Professional
Exam Number:	Databricks-Machine-Learning-Professional
Real Exam Qty:	60
Exam Duration:	120 minutes
Related Certifications:	Databricks Certified Machine Learning Associate
Available Languages:	English
Certificate Validity Period:	2 years
Exam Format:	Multiple choice
Exam Price:	USD 200
Passing Score:	70%
Sample Questions:	Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:	Online (proctored) or Test Center
Pre Condition:	No formal prerequisites, but 1+ years of hands-on experience performing the machine learning tasks outlined in the exam guide is highly recommended. Recommended courses: Machine Learning at Scale and Advanced Machine Learning Operations (instructor-led or self-paced via Databricks Academy).
Official Syllabus URL:	https://www.databricks.com/learn/certification/machine-learning-professional

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

Topic	Details
Topic 1	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 2	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 3	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 4	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 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	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 7	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines

Databricks Certified Machine Learning Professional Sample Questions (Q187-Q192):

NEW QUESTION # 187
A machine learning engineer wants to view all of the active MLflow Model Registry Webhooks for a specific model.
They are using the following code block:

Which of the following changes does the machine learning engineer need to make to this code block so it will successfully accomplish the task?

A. Replace list with view in the endpoint URL
B. There are no necessary changes
C. Replace POST with GET in the call to http request
D. Replace list with webhooks in the endpoint URL
E. Replace POST with PUT in the call to http request

Answer: D

NEW QUESTION # 188
A Data Scientist is training a model to predict whether a customer will purchase a new product.
They are experimenting with many algorithm types (e.g., Linear Regression, Logistic Regression, Decision Trees) and hundreds of hyperparameter combinations. They are using MLflow to track their runs, but managing and distinguishing between runs has become difficult. They need an MLFIow technique that will organize and trace these experiments and that will follow commonly- accepted design patterns. Which technique will fulfill these requirements?

A. Set each algorithm type as a parent run and use nested runs for different hyperparameter settings within that parent run.
B. Use AutoML to automatically test a variety of different algorithms and models.
C. Log runs for each algorithm type to different MLflow Tracking servers to ensure the runs do not get mixed up.
D. Use MLflows Automatic Logging feature to reduce the number of explicit log statements within each run to increase code readability.

Answer: A

Explanation:
Using parent runs for each algorithm and nested runs for the associated hyperparameter experiments follows a widely accepted MLflow experiment design pattern. This structure makes it easy to organize, compare, and trace large numbers of related runs, improving experiment management and interpretability when exploring many models and hyperparameter configurations.

NEW QUESTION # 189
A Machine Learning Engineer needs to build a credit risk model using Databricks. Due to strict data governance, production data cannot be accessed from development or staging environments. To manage MLOps, the engineer uses a "deploy code" strategy with separate development, staging, and production environments mapped to different catalogs in Unity Catalog. The CI/CD pipeline automates environment transitions. What is the primary architectural component promoted from staging to production to generate the final, production-ready model in this scenario?

A. The model artifact trained and validated in the staging environment on a subset of the data.
B. The CI/CD-approved model training source code, which will be executed in the production environment on the full production dataset.
C. A containerized image of the complete inference pipeline, including feature transformations.
D. The model version entry in the Unity Catalog that has been transitioned from the "Staging" to the
"Production" alias.

Answer: B

Explanation:
In a deploy-code strategy, models are not promoted as trained artifacts across environments.
Instead, the approved training code is promoted through CI/CD and re-executed in the production environment against production data to produce the final model. This approach satisfies strict data governance requirements by ensuring production data is only accessed in production while maintaining reproducibility and traceability.

NEW QUESTION # 190
What tool helps avoid feature mismatch between training and inference?

A. Model Registry
B. MLflow Projects
C. Spark SQL
D. Feature Store

Answer: D

Explanation:
Feature Store ensures:
same feature computation logic
consistency between training and serving.

NEW QUESTION # 191
What does an Estimator do in Spark ML?

A. Trains a model using data
B. Transforms a dataset
C. Evaluates model performance
D. Stores features

Answer: A

Explanation:
An Estimator learns from data and produces a Transformer.

NEW QUESTION # 192
......

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