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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. Reusable ML workflows
          • 2. Pipeline construction and orchestration
            - Data preparation and feature engineering
            • 1. Data ingestion and cleaning in Databricks
              • 2. Feature engineering techniques
                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
                        Model Deployment and Serving- Model deployment strategies
                        • 1. Databricks Model Serving
                          • 2. Batch inference vs real-time inference
                            Machine Learning Models and Algorithms- Supervised learning methods
                            • 1. Model evaluation metrics
                              • 2. Classification and regression models
                                - Unsupervised learning methods
                                • 1. Dimensionality reduction
                                  • 2. Clustering techniques

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                                    Databricks Certified Machine Learning Professional Sample Questions (Q136-Q141):

                                    NEW QUESTION # 136
                                    After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set. Which SQL command can be used to accomplish this task?

                                    Answer: B


                                    NEW QUESTION # 137
                                    A Data Scientist is training a model to predict whether a customer will purchase a new product.
                                    They are experimenting with many algorithm types (e.g., Linear Regression, Logistic Regression, Decision Trees) and hundreds of hyperparameter combinations. They are using MLflow to track their runs, but managing and distinguishing between runs has become difficult. They need an MLFIow technique that will organize and trace these experiments and that will follow commonly- accepted design patterns. Which technique will fulfill these requirements?

                                    Answer: C

                                    Explanation:
                                    Using parent runs for each algorithm and nested runs for the associated hyperparameter experiments follows a widely accepted MLflow experiment design pattern. This structure makes it easy to organize, compare, and trace large numbers of related runs, improving experiment management and interpretability when exploring many models and hyperparameter configurations.


                                    NEW QUESTION # 138
                                    A Machine Learning Engineer needs to develop fraud detection models with Databricks. They need to ensure seamless collaboration between data engineers and data scientists while maintaining strict governance, version control, and traceability as models progress from development to production. So, they need to choose the Databricks feature that will enable centralized model lineage tracking, cross-workspace access control, and automated synchronization of model versions with their training data. Which Databricks feature will do this?

                                    Answer: D

                                    Explanation:
                                    Unity Catalog provides centralized governance for models, including lineage tracking between models and their training data, cross-workspace access control, and consistent version management as models move from development to production. This enables secure collaboration, traceability, and enterprise-grade model governance across teams.


                                    NEW QUESTION # 139
                                    A data scientist has developed a model to predict ice cream sales using the expected temperature and expected number of hours of sun in the day. However, the expected temperature is dropping beneath the range of the input variable on which the model was trained.
                                    Which type of drift is present in the above scenario?

                                    Answer: E


                                    NEW QUESTION # 140
                                    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: B

                                    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 # 141
                                    ......

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