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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
Exam Duration:	120 minutes
Certificate Validity Period:	2 years
Real Exam Qty:	60
Passing Score:	70%
Exam Format:	Multiple choice
Exam Price:	USD 200
Available Languages:	English
Related Certifications:	Databricks Certified Machine Learning Associate
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	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
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 model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 4	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 5	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
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 the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 8	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 9	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 10	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

Databricks Certified Machine Learning Professional Sample Questions (Q128-Q133):

NEW QUESTION # 128
Which of the following MLflow Model Registry use cases requires the use of an HTTP Webhook?

A. Starting a testing job when a new model is registered
B. Updating data in a source table for a Databricks SQL dashboard when a model version transitions to the Production stage
C. None of these use cases require the use of an HTTP Webhook
D. Sending an email alert when an automated testing Job fails
E. Sending a message to a Slack channel when a model version transitions stages

Answer: B

NEW QUESTION # 129
A Machine Learning Engineer has deployed a customer churn prediction model to production three months ago. The model serves real-time predictions via a Databricks endpoint with inference logging enabled. They notice declining model accuracy in recent weeks and suspect data drift in customer demographics. They need to implement monitoring to track model performance degradation and input feature drift over time. Which monitoring profile type should they use?

A. Inference profile - Monitor the inference table to track model performance metrics, prediction drift, and input feature drift across time windows.
B. Time series profile - Monitor the inference table using timestamp-based windows to track model performance and input drift over time.
C. Snapshot profile - Monitor the complete inference table to compare current model performance against the baseline established at deployment.
D. Custom profile - Create a hybrid monitoring approach combining snapshot and time series profiles to capture both baseline comparisons and temporal trends.

Answer: A

Explanation:
The inference profile is designed specifically to monitor production model behavior using inference logs. It tracks model performance metrics, prediction distributions, and input feature drift across time windows, enabling detection of performance degradation and demographic data drift after deployment.

NEW QUESTION # 130
A data scientist has created a Python function compute_features that returns a Spark DataFrame with the following schema:

The resulting DataFrame is assigned to the features_df variable. The data scientist wants to create a Feature Store table using features_df.
Which of the following code blocks can they use to create and populate the Feature Store table using the Feature Store Client fs?

A. features_df.write.mode("fs").path("new_table")
B.
C. features_df.write.mode("feature").path("new_table")
D.
E.

Answer: D

NEW QUESTION # 131
Which MLflow function logs files like plots or datasets?

A. mlflow.save_model()
B. mlflow.log_metric()
C. mlflow.log_param()
D. mlflow.log_artifact()

Answer: D

Explanation:
Artifacts include:
charts
model files
datasets.

NEW QUESTION # 132
A data scientist wants to log outlier feature data from a CSV file at path outlier_path with an MLflow run for model model. Which code block will accomplish this task inside of an existing MLflow run block?

A. mlflow.log_artifact(outlier_path, "outlier-features.csv")
B. mlflow.log_data(outlier_path, "outlier-features.csv")
C.
D.

Answer: A

Explanation:
To log external files, such as a CSV containing outlier feature data, MLflow provides the mlflow.log_artifact() function. This function uploads the specified file or directory (outlier_path) as an artifact under the provided artifact path ("outlier-features.csv"). It is the correct way to associate data files with an MLflow run, whereas mlflow.log_model() is reserved for logging model objects, not arbitrary data.

NEW QUESTION # 133
......

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