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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 Models and Algorithms- Supervised learning methods
        • 1. Model evaluation metrics
          • 2. Classification and regression models
            - Unsupervised learning methods
            • 1. Dimensionality reduction
              • 2. Clustering 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
                        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. Pipeline construction and orchestration
                              • 2. Reusable ML workflows
                                Model Deployment and Serving- Model deployment strategies
                                • 1. Databricks Model Serving
                                  • 2. Batch inference vs real-time inference

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                                    Databricks Certified Machine Learning Professional Sample Questions (Q142-Q147):

                                    NEW QUESTION # 142
                                    A machine learning engineer wants to view all of the active MLflow Model Registry Webhooks for a specific model.
                                    They are using the following code block:

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

                                    Answer: A


                                    NEW QUESTION # 143
                                    A data scientist has developed a model to predict whether or not it will rain using the expected temperature and expected cloud coverage. However, the proportion of days where it actually rains has increased dramatically from the proportion in the data on which the model was trained.
                                    Which type of drift is present in the above scenario?

                                    Answer: D

                                    Explanation:
                                    Label drift occurs when the distribution of the target variable (label) changes over time while the relationship between features and the label remains the same. In this scenario, the proportion of days when it rains (the label) has changed significantly compared to the training data, indicating label drift.


                                    NEW QUESTION # 144
                                    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 MLflow operation can the machine learning engineer use to accomplish this task?

                                    Answer: E


                                    NEW QUESTION # 145
                                    A data scientist wants to track the runs of their random forest model. The data scientist is changing the number of trees and the maximum depth of the trees in the forest across each run.
                                    They write the following code block:

                                    Which Python object type does params need to be an instance of?

                                    Answer: D

                                    Explanation:
                                    The params variable must be a dictionary (dict) because mlflow.log_params() expects a dictionary where each key-value pair represents a parameter name and its corresponding value.
                                    Additionally, the model instantiation RandomForestRegressor(**params) also requires params to be a dictionary to unpack the parameters correctly.


                                    NEW QUESTION # 146
                                    A machine learning engineer is in the process of implementing a feature drift monitoring solution.
                                    They are planning to use the following steps:
                                    1. Measure the distributions of each feature variable in the training
                                    set
                                    2. Deploy a model to production
                                    3. Measure the distributions of each feature variable in inference
                                    4. _______
                                    Which action should be completed as Step #4?

                                    Answer: B

                                    Explanation:
                                    The final step in a feature drift monitoring solution is to run a statistical test (e.g., Kolmogorov- Smirnov test) to determine whether the feature distributions in production have significantly diverged from those in the training set. This helps detect drift and maintain model reliability.


                                    NEW QUESTION # 147
                                    ......

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