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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 Price:	USD 200
Related Certifications:	Databricks Certified Machine Learning Associate
Real Exam Qty:	60
Available Languages:	English
Exam Format:	Multiple choice
Certificate Validity Period:	2 years
Exam Duration:	120 minutes
Passing Score:	70%
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	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
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 live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
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 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 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 the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 10	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 (Q135-Q140):

NEW QUESTION # 135
A machine learning engineer has registered a sklearn model in the MLflow Model Registry using the sklearn model flavor with UI model_uri.
Which of the following operations can be used to load the model as an sklearn object for batch deployment?

A. mlflow.spark.load_model(model_uri)
B. mlflow.sklearn.load_model(model_uri)
C. mlflow.pyfunc.load_model(model_uri)
D. mlflow.sklearn.read_model(model_uri)
E. mlflow.pyfunc.read_model(model_uri)

Answer: C

NEW QUESTION # 136
A machine learning engineering team wants to build a continuous pipeline for data preparation of a machine learning application. The team would like the data to be fully processed and made ready for inference in a series of equal-sized batches. Which tool can be used to provide this type of continuous processing?

A. MLflow
B. Delta Lake
C. Structured Streaming
D. Spark UDFs

Answer: D

NEW QUESTION # 137
Which statement is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov- Smirnov (KS) test for numeric feature drift detection?

A. All of these reasons
B. JS is not normalized or smoothed
C. JS is more robust when working with large datasets
D. JS does not require any manual threshold or cutoff determinations
E. None of these reasons

Answer: C

NEW QUESTION # 138
A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. They have programmatically identified the best run from an MLflow Experiment and stored its URI in the model_uri variable and its Run ID in the run_id variable. They have also determined that the model was logged with the name "model". Now, the machine learning engineer wants to register that model in the MLflow Model Registry with the name "best_model".
Which line of code can they use to register the model to the MLflow Model Registry?

A. mlflow.register_model(f"runs:/{run_id}/best_model", "model")
B. mlflow.register_model(model_uri, "best_model")
C. mlflow.register_model(model_uri, "model")
D. mlflow.register_model(run_id, "best_model")
E. mlflow.register_model(f"runs:/{run_id}/model")

Answer: B

NEW QUESTION # 139
A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. They have programmatically identified the best run from an MLflow Experiment and stored its URI in the model_uri variable and its Run ID in the run_id variable. They have also determined that the model was logged with the name "model". Now, the machine learning engineer wants to register that model in the MLflow Model Registry with the name "best_model".
Which of the following lines of code can they use to register the model to the MLflow Model Registry?

A. mlflow.register_model(f"runs:/{run_id}/best_model", "model")
B. mlflow.register_model(model_uri, "model")
C. mlflow.register_model(run_id, "best_model")
D. mlflow.register_model(model_uri, "best_model")
E. mlflow.register_model(f"runs:/{run_id}/model")

Answer: B

NEW QUESTION # 140
......

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