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

SectionObjectives
MLOps, Monitoring, and Governance- Governance and compliance
  • 1. Model lifecycle governance
    • 2. Feature Store usage and management
      - Model monitoring
      • 1. Data drift detection
        • 2. Performance tracking in production
          MLflow and Experiment Tracking- Model registry
          • 1. Versioning and lifecycle management
            - Experiment management
            • 1. Model comparison and selection
              • 2. Tracking runs and parameters
                Machine Learning Models and Algorithms- Unsupervised learning methods
                • 1. Clustering techniques
                  • 2. Dimensionality reduction
                    - Supervised learning methods
                    • 1. Classification and regression models
                      • 2. Model evaluation metrics
                        Machine Learning Workflow on Databricks- End-to-end ML pipelines
                        • 1. Reusable ML workflows
                          • 2. Pipeline construction and orchestration
                            - Data preparation and feature engineering
                            • 1. Data ingestion and cleaning in Databricks
                              • 2. Feature engineering techniques
                                Model Deployment and Serving- Model deployment strategies
                                • 1. Batch inference vs real-time inference
                                  • 2. Databricks Model Serving

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                                    Databricks Certified Machine Learning Professional Sample Questions (Q132-Q137):

                                    NEW QUESTION # 132
                                    Which of the following describes label drift?

                                    Answer: D


                                    NEW QUESTION # 133
                                    Which of the following deployment paradigms can centrally compute predictions for a single record with exceedingly fast results?

                                    Answer: D


                                    NEW QUESTION # 134
                                    Which MLflow component is used to log parameters, metrics, and artifacts during model training?

                                    Answer: A

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


                                    NEW QUESTION # 135
                                    Why is Delta Lake time travel useful in ML pipelines?

                                    Answer: D

                                    Explanation:
                                    Time travel allows training on exact historical datasets.


                                    NEW QUESTION # 136
                                    A data scientist wants to track the runs of their random forest model. The data scientist is changing the number of trees and the maximum depth of the trees in the forest across each run.
                                    They write the following code block:

                                    Which Python object type does params need to be an instance of?

                                    Answer: A

                                    Explanation:
                                    The params variable must be a dictionary (dict) because mlflow.log_params() expects a dictionary where each key-value pair represents a parameter name and its corresponding value.
                                    Additionally, the model instantiation RandomForestRegressor(**params) also requires params to be a dictionary to unpack the parameters correctly.


                                    NEW QUESTION # 137
                                    ......

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