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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Model Deployment12%- Custom model serving
- Deployment strategies
- Model rollout and version management
Topic 2: ML Ops44%- Testing and validation strategies
- Environment management with Databricks Asset Bundles
- Model monitoring and drift detection with Lakehouse Monitoring
- Automated retraining workflows
Topic 3: Model Development44%- Feature Store and automated feature pipelines
- Scalable ML pipelines with SparkML
- Advanced MLflow usage
- Distributed training and hyperparameter tuning

>> Databricks-Machine-Learning-Professional Examinations Actual Questions <<

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Databricks Certified Machine Learning Professional Sample Questions (Q12-Q17):

NEW QUESTION # 12
A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client. Which code block can they use to accomplish the task?

Answer: A

Explanation:
The correct method for transitioning a model version to a new stage using the MLflow Client is client.transition_model_version_stage(name, version, stage). This updates the stage of a specific model version in the Model Registry, such as moving it from "Staging" to "Production". The stage parameter specifies the target stage only -- there is no from argument.


NEW QUESTION # 13
Which of the following is a simple statistic to monitor for categorical feature drift?

Answer: C


NEW QUESTION # 14
Which of the following Databricks-managed MLflow capabilities is a centralized model store?

Answer: B


NEW QUESTION # 15
A Machine Learning Engineer has deployed a fraud detection model that processes 10,000 transactions per hour. The model was trained on data from Q1 2024, but it's now Q4 2024. The ML team notices three concerning trends: (1) the model's precision has dropped from 92% to
78% over the past month, (2) the average transaction amount in recent data has increased from
$150 to $220 and (3) the relationship between transaction frequency and fraud likelihood has weakened significantly due to new payment methods being introduced. The engineer needs to implement a monitoring solution that can detect the root cause of the performance degradation, identify why the precision dropped, and be able to do this at the scale needed. Which monitoring pipeline component will do this?

Answer: D

Explanation:
A drift detection pipeline is designed to diagnose the root causes of model performance degradation at scale. It can detect data drift, such as changes in the distribution of transaction amounts, and concept drift, where the relationship between input features and the fraud label changes due to new payment methods. This directly explains why precision dropped and provides actionable insight beyond simply observing metric degradation.


NEW QUESTION # 16
Why is Apache Spark useful for machine learning training?

Answer: B

Explanation:
Spark processes large distributed datasets efficiently.


NEW QUESTION # 17
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