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

SectionObjectives
Machine Learning Models and Algorithms- Unsupervised learning methods
  • 1. Dimensionality reduction
    • 2. Clustering techniques
      - Supervised learning methods
      • 1. Model evaluation metrics
        • 2. Classification and regression models
          MLflow and Experiment Tracking- Model registry
          • 1. Versioning and lifecycle management
            - Experiment management
            • 1. Tracking runs and parameters
              • 2. Model comparison and selection
                Model Deployment and Serving- Model deployment strategies
                • 1. Databricks Model Serving
                  • 2. Batch inference vs real-time inference
                    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. Data drift detection
                                  • 2. Performance tracking in production

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                                    Databricks Certified Machine Learning Professional Sample Questions (Q155-Q160):

                                    NEW QUESTION # 155
                                    A data scientist has developed a scikit-learn model sklearn_model and they want to log the model using MLflow.
                                    They write the following incomplete code block:

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

                                    Answer: C


                                    NEW QUESTION # 156
                                    Which of the following is a simple statistic to monitor for categorical feature drift?

                                    Answer: C


                                    NEW QUESTION # 157
                                    A machine learning engineer wants to delete an active MLflow Model Registry Webhook with webhook ID webhook_id for a specific model.
                                    They are using the following code block:

                                    Which change does the machine learning engineer need to make to this code block so it will successfully accomplish the task?

                                    Answer: B

                                    Explanation:
                                    To delete a webhook using the MLflow REST API, the correct endpoint is:
                                    /api/2.0/mlflow/registry-webhooks/delete
                                    In the provided code, the endpoint incorrectly uses "list", which retrieves webhooks rather than deleting them. The HTTP method "DELETE" is correct, so the only necessary change is to replace list with delete in the endpoint URL.


                                    NEW QUESTION # 158
                                    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. Which code block can they use to accomplish the task?

                                    Answer: D

                                    Explanation:
                                    The correct method for transitioning a model version to a new stage using the MLflow Client is client.transition_model_version_stage(name, version, stage). This updates the stage of a specific model version in the Model Registry, such as moving it from "Staging" to "Production". The stage parameter specifies the target stage only -- there is no from argument.


                                    NEW QUESTION # 159
                                    In a Spark ML Pipeline, what is the role of a Transformer?

                                    Answer: D

                                    Explanation:
                                    A Transformer takes a dataset and transforms it into another dataset.


                                    NEW QUESTION # 160
                                    ......

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