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

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

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                                    Databricks Certified Machine Learning Professional Sample Questions (Q101-Q106):

                                    NEW QUESTION # 101
                                    Which statement is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov- Smirnov (KS) test for numeric feature drift detection?

                                    Answer: E


                                    NEW QUESTION # 102
                                    Which of the following can be used to compare the relative prevalence of specific values in a single categorical variable between two datasets or time periods?

                                    Answer: B

                                    Explanation:
                                    Contingency tables (also known as cross-tabulations) display the frequency distribution of categorical variables, allowing comparison of how specific category values occur across two datasets or time periods. This makes them effective for identifying categorical feature drift or shifts in value prevalence.


                                    NEW QUESTION # 103
                                    Feature drift occurs when there is a change in which element?

                                    Answer: A

                                    Explanation:
                                    Feature drift refers to a change in the distribution of one or more input features over time, even if the target variable and model relationship remain stable. This can degrade model performance because the model was trained on a different feature distribution than it encounters during inference.


                                    NEW QUESTION # 104
                                    Which deployment paradigm can centrally compute predictions for a single record with exceedingly fast results?

                                    Answer: E


                                    NEW QUESTION # 105
                                    A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. The pipeline needs to automatically refresh its model each time it runs. The project is attached to the existing model model_name in the MLflow Model Registry.
                                    They are using the following code block as part of their solution:

                                    Which statement describes the impact of the registered_model_name=model_name parameter and argument given that model_name already exists in the MLflow Model Registry?

                                    Answer: A

                                    Explanation:
                                    When using registered_model_name=model_name in mlflow.spark.log_model(), MLflow automatically registers the logged model under the specified model name. If that model name already exists in the MLflow Model Registry, MLflow creates a new version of that existing registered model rather than a new model entry. This enables automatic versioning and continuous model refresh with each pipeline run.


                                    NEW QUESTION # 106
                                    ......

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