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

SectionObjectives
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 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. 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. Batch inference vs real-time inference
                          • 2. Databricks Model Serving
                            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 (Q150-Q155):

                                    NEW QUESTION # 150
                                    A machine learning engineer has created a webhook with the following code block:

                                    Which of the following code blocks will trigger this webhook to run the associate job?

                                    Answer: A


                                    NEW QUESTION # 151
                                    Which approach is best for scoring large historical datasets?

                                    Answer: C

                                    Explanation:
                                    Spark allows distributed batch scoring across massive datasets.


                                    NEW QUESTION # 152
                                    A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client. At the same time, they would like to archive any model versions that are already in the Production stage. Which code block can they use to accomplish the task?

                                    Answer: A


                                    NEW QUESTION # 153
                                    A machine learning engineer is attempting to create a webhook that will trigger a Databricks Job job_id when a model version for model model transitions into any MLflow Model Registry stage.
                                    They have the following incomplete code block:

                                    Which of the following lines of code can be used to fill in the blank so that the code block accomplishes the task?

                                    Answer: A


                                    NEW QUESTION # 154
                                    A data scientist would like to switch from manually using MLflow logging to MLflow Autologging for all machine learning libraries used in a notebook.
                                    They begin by adding mlflow.autolog()to the top of the below code block:

                                    The data scientist is now trying to determine which line of code will kick off the MLflow Autologging process.
                                    Which line of code within the above code block will start the MLflow Autologging process?

                                    Answer: B

                                    Explanation:
                                    The MLflow Autologging process is triggered when a model's training function is called. In this case, rf.fit(X_train, y_train) starts the autologging process because mlflow.autolog() automatically tracks parameters, metrics, and the model when a supported estimator (like RandomForestRegressor) is fitted. The context manager mlflow.start_run() only initiates an MLflow run, but the actual autologging begins during the .fit() execution.


                                    NEW QUESTION # 155
                                    ......

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