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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
Certificate Validity Period:2 years
Exam Price:USD 200
Exam Duration:120 minutes
Related Certifications:Databricks Certified Machine Learning Associate
Real Exam Qty:60
Available Languages:English
Passing Score:70%
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
  • 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 2
  • Describe concept drift and its impact on model efficacy
  • Describe summary statistic monitoring as a simple solution for numeric feature drift
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 the requirements for tracking nested runs
  • Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 5
  • Identify live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 6
  • Identify less performant data storage as a solution for other use cases
  • Describe why complex business logic must be handled in streaming deployments
Topic 7
  • Identify which code block will trigger a shown webhook
  • Describe the basic purpose and user interactions with Model Registry
Topic 8
  • Describe the advantages of using the pyfunc MLflow flavor
  • Manually log parameters, models, and evaluation metrics using MLflow
Topic 9
  • Identify JIT feature values as a need for real-time deployment
  • Describe how to list all webhooks and how to delete a webhook
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

Databricks Certified Machine Learning Professional Sample Questions (Q151-Q156):

NEW QUESTION # 151
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 change does the team need to make to the above code block to accomplish the task?

Answer: D


NEW QUESTION # 152
A machine learning engineer has deployed a model recommender using MLflow Model Serving.
They now want to query the version of that model that is in the Production stage of the MLflow Model Registry. Which of the following model URIs can be used to query the described model version?

Answer: C


NEW QUESTION # 153
A machine learning engineer is developing a recommendation system for online content. They are using the Databricks Feature Store to store features for training and inference. Which unit test should they create?

Answer: B

Explanation:
When using the Databricks Feature Store, the most important unit tests validate that feature transformation logic produces correct and consistent outputs. Testing feature transformation functions ensures that features written to the Feature Store are accurate and reliable for both training and inference, independent of infrastructure or serving mechanisms.


NEW QUESTION # 154
A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable.
They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df. Which of the following code blocks can they use to create a function called predict that they can use to complete the task?

Answer: C


NEW QUESTION # 155
A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable. They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df.
Which of the following code blocks can they use to create a function called predict that they can use to complete the task?

Answer: B


NEW QUESTION # 156
......

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