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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
Passing Score:70%
Related Certifications:Databricks Certified Machine Learning Associate
Real Exam Qty:60
Exam Duration:120 minutes
Exam Price:USD 200
Certificate Validity Period:2 years
Exam Format:Multiple choice
Available Languages:English
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 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 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
  • 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 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
  • Describe concept drift and its impact on model efficacy
  • Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 6
  • Describe the advantages of using the pyfunc MLflow flavor
  • Manually log parameters, models, and evaluation metrics using MLflow
Topic 7
  • Identify which code block will trigger a shown webhook
  • Describe the basic purpose and user interactions with Model Registry
Topic 8
  • Identify the requirements for tracking nested runs
  • Describe an MLflow flavor and the benefits of using MLflow flavors
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
  • 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

Databricks Certified Machine Learning Professional Sample Questions (Q44-Q49):

NEW QUESTION # 44
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 # 45
Which of the following describes concept drift?

Answer: D


NEW QUESTION # 46
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 Staging stage of the MLflow Model Registry. Which model URI can be used to query the described model version?

Answer: B

Explanation:
To query a model version in a specific stage (like Staging) using MLflow Model Serving on Databricks, the correct model URI format is:
https://<databricks-instance>/model/<model-name>/<stage>/invocations
So, for the model named recommender in the Staging stage, the correct URI is:
https://<databricks-instance>/model/recommender/Staging/invocations.


NEW QUESTION # 47
A machine learning engineer is attempting to create a webhook that will trigger a Databricks Job job_id when a model version for model model transitions into any MLflow Model Registry stage.
They have the following incomplete code block:

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

Answer: C


NEW QUESTION # 48
Which approach is best for scoring large historical datasets?

Answer: B

Explanation:
Spark allows distributed batch scoring across massive datasets.


NEW QUESTION # 49
......

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