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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
Available Languages:	English
Exam Format:	Multiple choice
Certificate Validity Period:	2 years
Real Exam Qty:	60
Exam Duration:	120 minutes
Passing Score:	70%
Related Certifications:	Databricks Certified Machine Learning Associate
Exam Price:	USD 200
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	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
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	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature 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 JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 6	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 7	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 8	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 9	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow

Databricks Certified Machine Learning Professional Sample Questions (Q77-Q82):

NEW QUESTION # 77
A data scientist has developed a scikit-learn model sklearn_model and they want to log the model using MLflow.
They write the following incomplete code block:

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

A. mlflow.sklearn.log_model(sklearn_model, "model")
B. mlflow.spark.track_model(sklearn_model, "model")
C. mlflow.spark.log_model(sklearn_model, "model")
D. mlflow.sklearn.track_model(sklearn_model, "model")
E. mlflow.sklearn.load_model("model")

Answer: B

NEW QUESTION # 78
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.pyfunk.spark.udf
B. mlflow.spark.pandas_udf
C. It is not possible to deploy a scikit-learn model in parallel.
D. mlflow.sklearn.spark.udf

Answer: D

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 # 79
A machine learning engineer has implemented a numeric drift monitoring solution by examining trends in the summary statistics of input variables. However, the engineer's stakeholders would like a more robust monitoring solution. Which of the following can provide a more robust drift monitoring solution for numeric feature variables?

A. Oversampling
B. Statistical tests
C. Correlations
D. None of these can provide more robust drift monitoring than summary statistics

Answer: B

Explanation:
Statistical tests (such as the Kolmogorov-Smirnov test or Wasserstein distance) provide a more robust and quantitative method for detecting numeric feature drift compared to simple summary statistics. These tests compare the full distributions of features between datasets, making them more sensitive to subtle changes in data behavior over time.

NEW QUESTION # 80
A Data Scientist is building a propensity model for an e-commerce start-up. The company maintains 7GB of historical data and receives about 5MB of new transaction data daily. The goal is to generate daily purchase predictions for all users by 7:00 AM each morning. As the start-up is in its early stages, the data scientist must prioritize a highly cost-efficient approach. Which approach should the Data Scientist take?

A. Use a single-node memory compute to build a model with libraries like scikit-learn. The model can then be scheduled as a nightly batch job that runs on a single-node compute.
B. Use a multi-node compute and leverage distributed frameworks like SparkML to train the model.
The model can then be scheduled as a nightly batch job that runs on a multi-node compute.
C. Use a single-node compute to build a model with libraries like scikit-learn. The model can then be deployed as an always-on REST API so that users can query the API to get the predictions whenever they want.
D. Use a single-node compute to build a model with libraries like scikit-learn. The model can then be integrated into an always-on streaming pipeline, to ensure immediate processing of incoming data in order to meet the SLA.

Answer: A

Explanation:
With only 7GB of historical data and a small daily increment (about 5MB), a single-node memory- optimized cluster can comfortably train and score using scikit-learn without the overhead and cost of distributed compute. Scheduling a nightly batch job is the most cost-efficient way to meet a fixed daily SLA (7:00 AM) because the compute can be started only for the job run and then terminated, avoiding the expense of always-on serving or streaming infrastructure.

NEW QUESTION # 81
Which MLflow component is used to log parameters, metrics, and artifacts during model training?

A. MLflow Models
B. MLflow Tracking
C. MLflow Projects
D. Model Registry

Answer: B

Explanation:
MLflow Tracking records experiment runs including:
parameters
metrics
artifacts (models, plots, etc.)

NEW QUESTION # 82
......

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