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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 Price:USD 200
Available Languages:English
Passing Score:70%
Related Certifications:Databricks Certified Machine Learning Associate
Certificate Validity Period:2 years
Exam Duration:120 minutes
Exam Format:Multiple choice
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:

TopicDetails
Topic 1
  • Identify live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 2
  • Describe concept drift and its impact on model efficacy
  • Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 3
  • 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 4
  • Identify which code block will trigger a shown webhook
  • Describe the basic purpose and user interactions with Model Registry
Topic 5
  • 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 6
  • 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 7
  • Describe the advantages of using the pyfunc MLflow flavor
  • Manually log parameters, models, and evaluation metrics using MLflow
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 the requirements for tracking nested runs
  • Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 10
  • Describe model serving deploys and endpoint for every stage
  • Identify scenarios in which feature drift and
  • or label drift are likely to occur
Topic 11
  • Identify less performant data storage as a solution for other use cases
  • Describe why complex business logic must be handled in streaming deployments

Databricks Certified Machine Learning Professional Sample Questions (Q72-Q77):

NEW QUESTION # 72
A Machine Learning Engineer is responsible for maintaining a fraud detection model deployed on Databricks. They want to implement a retraining pipeline that automatically starts when the model's F1 score drops below a threshold or when input feature distributions change significantly.
Which two actions should the engineer take to implement this automated retraining? (Choose two.)

Answer: A,B

Explanation:
Databricks Lakehouse Monitoring stores model performance and data drift metrics in Delta tables, which can be monitored using Databricks SQL alerts. By creating alerts on F1 score degradation or significant feature drift and configuring those alerts to send webhook notifications, the engineer can automatically trigger a retraining job whenever predefined conditions are met, enabling event-driven, automated retraining aligned with MLOps best practices.


NEW QUESTION # 73
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?

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 # 74
A data scientist has developed a model to predict whether or not it will rain using the expected temperature and expected cloud coverage. However, the proportion of days where it actually rains has increased dramatically from the proportion in the data on which the model was trained.
Which type of drift is present in the above scenario?

Answer: A

Explanation:
Label drift occurs when the distribution of the target variable (label) changes over time while the relationship between features and the label remains the same. In this scenario, the proportion of days when it rains (the label) has changed significantly compared to the training data, indicating label drift.


NEW QUESTION # 75
A machine learning engineer is monitoring categorical input variables for a production machine learning application. The engineer believes that missing values are becoming more prevalent in more recent data for a particular value in one of the categorical input variables. Which of the following tools can the machine learning engineer use to assess their theory?

Answer: D


NEW QUESTION # 76
Which of the following describes the purpose of the context parameter in the predict method of Python models for MLflow?

Answer: E


NEW QUESTION # 77
......

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