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

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

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Databricks Certified Machine Learning Professional Sample Questions (Q122-Q127):

NEW QUESTION # 122
Label drift occurs where there is a change in which element?

Answer: C

Explanation:
Label drift refers to a change in the distribution of the target variable over time. This means the frequencies or proportions of classes or target values shift, which can impact model performance even if the input feature distributions remain unchanged.


NEW QUESTION # 123
A Data Scientist is preparing a Spark ML pipeline on a customer dataset with numeric features age, annual_income, and transaction_count, each varying widely. Because the chosen algorithm requires inputs normalized to the [0,1] range, they need to apply the appropriate Spark ML transformer to these features. Which Spark ML transformer should the Data Scientist use to scale all features to the [0,1] range?

Answer: D

Explanation:
MinMaxScaler rescales each numeric feature to a fixed range, typically [0, 1], by subtracting the minimum value and dividing by the feature's range. This makes it the appropriate Spark ML transformer when an algorithm explicitly requires inputs normalized to the [0,1] interval.


NEW QUESTION # 124
What is commonly monitored in deployed ML models?

Answer: C

Explanation:
Production ML systems monitor:
data drift
model drift
prediction accuracy.


NEW QUESTION # 125
A data scientist has developed a model model and computed the RMSE of the model on the test set. They have assigned this value to the variable rmse. They now want to manually store the RMSE value with the MLflow run.
They write the following incomplete code block:

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

Answer: A


NEW QUESTION # 126
A machine learning engineer has created a webhook with the following code block:

Which of the following code blocks will trigger this webhook to run the associate job?

Answer: E


NEW QUESTION # 127
......

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