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

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

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Databricks Certified Machine Learning Professional Sample Questions (Q113-Q118):

NEW QUESTION # 113
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 # 114
A machine learning engineer has detected that concept drift is occurring in a production machine learning application. Which result is the impact of concept drift?

Answer: B

Explanation:
Concept drift occurs when the relationship between input features and the target variable changes over time. This leads to a decrease in the model's efficacy, as the model is no longer accurately capturing the underlying data patterns it was trained on.


NEW QUESTION # 115
A machine learning engineer has found drift in a production machine learning application. The engineer has determined that retraining and deploying a new model is necessary. Which statement must be true prior to deploying the new model?

Answer: D

Explanation:
When drift is detected, it indicates that the data distribution has changed. Therefore, the new model must perform better than the original model on the most recently available data, as that data reflects current conditions. This ensures the new model is well-adapted to the current environment and will deliver more reliable predictions.


NEW QUESTION # 116
A Machine Learning Engineer is using joblibspark and MLflowCallback to perform a distributed hyperparameter tuning experiment via Optuna. Assuming they have a single objective they are optimizing for, what will be the default sampler implemented by Optuna?

Answer: A

Explanation:
For single-objective optimization, Optuna uses the Tree-structured Parzen Estimator (TPE) sampler by default. TPESampler is a Bayesian optimization method that efficiently explores the hyperparameter space by modeling promising parameter regions, making it well suited for scalable hyperparameter tuning with joblibspark and MLflowCallback.


NEW QUESTION # 117
Which of the following lists all of the model stages are available in the MLflow Model Registry?

Answer: A


NEW QUESTION # 118
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