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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
Certificate Validity Period:	2 years
Real Exam Qty:	59
Passing Score:	Not publicly disclosed (approximately 70%)
Available Languages:	English
Related Certifications:	Databricks Certified Machine Learning Associate
Exam Format:	Multi-select, Multiple choice
Exam Duration:	120 minutes
Exam Price:	$200 USD
Recommended Training:	Machine Learning at Scale Advanced Machine Learning Operations
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 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 model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 3	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 4	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 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	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 7	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 8	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

Databricks Certified Machine Learning Professional Sample Questions (Q186-Q191):

NEW QUESTION # 186
Which of the following lists all of the model stages are available in the MLflow Model Registry?

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

Answer: A

NEW QUESTION # 187
A data scientist is utilizing MLflow to track their machine learning experiments. After completing a series of runs for the experiment with experiment ID exp_id, the data scientist wants to programmatically work with the experiment run data in a Spark DataFrame. They have an active MLflow Client client and an active Spark session spark. Which of the following lines of code can be used to obtain run-level results for exp_id in a Spark DataFrame?

A. mlflow.search_runs(exp_id)
B. There is no way to programmatically return row-level results from an MLflow Experiment.
C. spark.read.format("delta").load(exp_id)
D. client.list_run_infos(exp_id)
E. spark.read.format("mlflow-experiment").load(exp_id)

Answer: C

NEW QUESTION # 188
Which of the following describes label drift?

A. Label drift is when there is a change in the distribution of an input variable
B. Label drift is when there is a change in the distribution of a target variable
C. None of these describe label drift
D. Label drift is when there is a change in the distribution of the predicted target given by the model
E. Label drift is when there is a change in the relationship between input variables and target variables

Answer: A

NEW QUESTION # 189
A machine learning engineer has developed a random forest model using scikit-learn and registered the model using MLflow. They now want to deploy that model in parallel. Which of the following operations can they use to create a function they can use to deploy the registered scikit- learn model in parallel?

A. mlflow.sklearn.spark.udf
B. It is not possible to deploy a scikit-learn model in parallel.
C. mlflow.spark.pandas_udf
D. mlflow.pyfunk.spark.udf

Answer: A

Explanation:
The correct operation is mlflow.sklearn.spark_udf, which creates a Spark UDF from a registered scikit-learn model. This allows the model to be applied in parallel across a Spark DataFrame, enabling scalable batch inference using Spark.

NEW QUESTION # 190
A machine learning engineer needs to select a deployment strategy for a new machine learning application. The machine learning application requires central prediction computation and exceedingly fast results, but only a handful of predictions need to be computed at a time. Which deployment strategy can be used to meet these requirements?

A. Streaming
B. Batch
C. Real-time
D. Edge/on-device

Answer: C

Explanation:
Real-time deployment is the appropriate strategy when predictions need to be computed centrally with very low latency, even if only a small number of predictions are required at a time. This ensures fast responses for applications requiring immediate inference.

NEW QUESTION # 191
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