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

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

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                                    Databricks Certified Machine Learning Professional Sample Questions (Q74-Q79):

                                    NEW QUESTION # 74
                                    A Data Scientist needs to perform inference on a continuously updated Delta table called sales_data using an MLflow-registered Spark ML pipeline model (catalog.prod.sales_forecaster).
                                    Predictions must be written to a Delta table forecast_results, which must be updated with low latency leveraging a cluster with three executors. They want to maximize the efficient use of their cluster when doing this. Which approach will suit their needs?

                                    Answer: C

                                    Explanation:
                                    This approach uses a Spark Structured Streaming read from the continuously updated Delta table and applies an MLflow-registered Spark UDF for inference. The model execution is distributed across the three executors, enabling parallel, low-latency scoring as new data arrives. Writing the results with writeStream efficiently updates the forecast_results Delta table incrementally, maximizing cluster utilization and aligning with best practices for continuous, scalable batch- stream inference in Databricks.


                                    NEW QUESTION # 75
                                    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 # 76
                                    Which of the following lists all of the model stages are available in the MLflow Model Registry?

                                    Answer: C


                                    NEW QUESTION # 77
                                    A machine learning engineer wants to deploy a model for real-time serving using MLflow Model Serving. For the model, the machine learning engineer currently has one model version in each of the stages in the MLflow Model Registry. The engineer wants to know which model versions can be queried once Model Serving is enabled for the model. Which of the following lists all of the MLflow Model Registry stages whose model versions are automatically deployed with Model Serving?

                                    Answer: B


                                    NEW QUESTION # 78
                                    A Machine Learning Engineer is conducting hyperparameter tuning for multiple XGBoost models using Ray Tune on Databricks. They want to integrate MLflow tracking to monitor their experiments and need to ensure proper authentication. The engineer has Ray 2.41 installed and wants to use both Ray Tune and MLflow together in their distributed tuning workflow. They have to configure Databricks to run the hyperparameter optimization with MLflow integration. Which set of configuration steps will do this?

                                    Answer: A

                                    Explanation:
                                    When using Ray Tune with MLflow on Databricks, Ray workers must be able to authenticate back to the Databricks workspace to log runs to MLflow Tracking. Setting the DATABRICKS_HOST and DATABRICKS_TOKEN environment variables before initializing the Ray cluster ensures all Ray processes can securely communicate with Databricks and correctly log MLflow experiments during distributed hyperparameter tuning.


                                    NEW QUESTION # 79
                                    ......

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