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Databricks Databricks-Machine-Learning-Professional Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Machine Learning Professional Exam
Exam Number:	Databricks-Machine-Learning-Professional
Passing Score:	Not publicly disclosed (approximately 70%)
Exam Duration:	120 minutes
Real Exam Qty:	59
Exam Price:	$200 USD
Exam Format:	Multi-select, Multiple choice
Available Languages:	English
Related Certifications:	Databricks Certified Machine Learning Associate
Certificate Validity Period:	2 years
Recommended Training:	Advanced Machine Learning Operations Machine Learning at Scale
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:	Online proctored or in-person test center
Pre Condition:	No mandatory prerequisites; recommended: 6+ months hands-on experience with Databricks ML, SparkML, MLflow, and Python
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 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 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 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	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 5	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 6	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 7	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors

Databricks Certified Machine Learning Professional Sample Questions (Q146-Q151):

NEW QUESTION # 146
A machine learning engineer wants to deploy a model for real-time serving using MLflow Model Serving. For the model, the machine learning engineer currently has one model version in each of the stages in the MLflow Model Registry. The engineer wants to know which model versions can be queried once Model Serving is enabled for the model. Which of the following lists all of the MLflow Model Registry stages whose model versions are automatically deployed with Model Serving?

A. Staging, Production, Archived
B. None, Staging, Production, Archived
C. None, Staging, Production
D. Staging, Production
E. Production

Answer: D

NEW QUESTION # 147
A Machine Learning Engineer has deployed a fraud detection model in Databricks Model Serving to detect fraudulent transactions. The engineer wants to compare the model's predictions with the actual fraud classifications from the Fraud Ops team to monitor model performance. The Fraud Ops team uses a unique transaction_id to investigate fraudulent activity and persist their findings to a fraud_findings table. The engineer enabled inference tables on the endpoint, but they are not sure how to map the models' predictions to the Fraud Ops team's classifications. How can the engineer uniquely join the models' prediction to the fraud_findings table with the fewest code changes?

A. Modify the model to include an additional input: transaction_id. Log, register and deploy the new model. In the model serving request body, add transaction_id as an additional input feature. Join the inference table with the fraud_findings table using transaction_id as the join key.
B. Join the inference table with the fraud_findings table using timestamp_ms as the join key.
C. Store databricks_request_id returned from each model serving request and persist it to the fraud_findings table. Join the inference table with the fraud_findings table using databricks_request_id as the join key.
D. Populate the client_request_id field with the transaction_id in the model serving request body.
Join the inference table with the fraud_findings table using client_request_id (which contains the transaction_id) as the join key.

Answer: D

Explanation:
Databricks Model Serving inference tables automatically log the client_request_id field for each request. By populating this field with the existing transaction_id in the request body, the engineer can directly and uniquely join inference predictions with the fraud_findings table using the same identifier, achieving accurate performance monitoring with minimal code changes and no model retraining or redeployment.

NEW QUESTION # 148
Which of the following describes the concept of MLflow Model flavors?

A. A convention that deployment tools can use to understand the model
B. A convention that MLflow Model Registry can use to organize its Models by project
C. A convention that deployment tools can use to wrap preprocessing logic into a Model
D. A convention that MLflow Model Registry can use to version models
E. A convention that MLflow Experiments can use to organize their Runs by project

Answer: E

NEW QUESTION # 149
A Machine Learning Engineer needs a continuous deployment pipeline for their models hosted on Databricks Model Serving. The deployment automation should execute after a model is trained and registered using MLflow. The goal of the automation is to deploy the latest version of the model from the MLflow Model Registry only if the model can meet the company's strict latency requirements (P95 < 300ms) while serving production traffic. How can the engineer validate that new models meet their latency requirements when served in production?

A. A/B test the latest model with Databricks Model Serving so that the latest model receives 5% of production traffic and the current model receives the rest. Use inference tables to calculate P95 latency and verify it is less than 300ms.
B. Serve the latest model on Databricks Model Serving and use a load testing client to generate requests at the production request rate. Use the load testing client to calculate P95 latency and verify it is less than 300ms.
C. Use the MLflow Get Run API to retrieve the model metrics from MLflow Tracking and verify that the model_latency metric is less than 300ms.
D. Use the MLflow Get Run API to retrieve the model metrics from MLflow Tracking and verify that the inference_latency metric is less than 300ms.

Answer: A

Explanation:
Validating latency requirements must be done under real production serving conditions. Routing a small percentage of live production traffic to the new model using Databricks Model Serving allows accurate measurement of end-to-end serving latency. Inference tables capture request latency metrics, enabling calculation of the P95 latency and ensuring it meets the strict production threshold before full rollout.

NEW QUESTION # 150
A data scientist has developed a scikit-learn model sklearn_model and they want to log the model using MLflow.
They write the following incomplete code block:

Which lines of code can be used to fill in the blank so the code block can successfully complete the task?

A. mlflow.spark.log_model(sklearn_model, "model")
B. mlflow.sklearn.log_model(sklearn_model, "model")
C. mlflow.sklearn.load_model("model")
D. mlflow.spark.track_model(sklearn_model, "model")
E. mlflow.sklearn.track_model(sklearn_model, "model")

Answer: D

NEW QUESTION # 151
......

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