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

SectionObjectives
Topic 1: Model Deployment and Serving- Model deployment strategies
  • 1. Batch inference vs real-time inference
    • 2. Databricks Model Serving
      Topic 2: 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
              Topic 3: 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
                      Topic 4: MLOps, Monitoring, and Governance- Model monitoring
                      • 1. Data drift detection
                        • 2. Performance tracking in production
                          - Governance and compliance
                          • 1. Feature Store usage and management
                            • 2. Model lifecycle governance
                              Topic 5: MLflow and Experiment Tracking- Experiment management
                              • 1. Tracking runs and parameters
                                • 2. Model comparison and selection
                                  - Model registry
                                  • 1. Versioning and lifecycle management

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                                    Databricks Certified Machine Learning Professional Sample Questions (Q28-Q33):

                                    NEW QUESTION # 28
                                    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: D


                                    NEW QUESTION # 29
                                    A data scientist has created a Python function compute_features that returns a Spark DataFrame with the following schema:

                                    The resulting DataFrame is assigned to the features_df variable. The data scientist wants to create a Feature Store table using features_df.
                                    Which of the following code blocks can they use to create and populate the Feature Store table using the Feature Store Client fs?

                                    Answer: A


                                    NEW QUESTION # 30
                                    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 # 31
                                    A Data Scientist needs to analyze drift detection results from Databricks Lakehouse Monitoring.
                                    The system has generated both profile metrics and drift metrics tables. The scientist needs to identify baseline drift in numerical features by comparing current data against a baseline from 6 months ago. Which combination of table columns and values indicates baseline drift in a numerical feature?

                                    Answer: A

                                    Explanation:
                                    Baseline drift is identified in the drift metrics table by setting drift_type to BASELINE, and for numerical features the drift statistics are the KS test (ks_test with a p-value indicating a statistically significant distribution change) and a numeric distance metric such as wasserstein_distance.


                                    NEW QUESTION # 32
                                    A machine learning engineer is manually refreshing a model in an existing machine learning pipeline. The pipeline uses the MLflow Model Registry model "project". The machine learning engineer would like to add a new version of the model to "project".
                                    Which of the following MLflow operations can the machine learning engineer use to accomplish this task?

                                    Answer: E


                                    NEW QUESTION # 33
                                    ......

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