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

SectionObjectives
Model Deployment and Serving- Model deployment strategies
  • 1. Databricks Model Serving
    • 2. Batch inference vs real-time inference
      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
              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. Pipeline construction and orchestration
                      • 2. Reusable ML workflows
                        - Data preparation and feature engineering
                        • 1. Data ingestion and cleaning in Databricks
                          • 2. Feature engineering techniques
                            MLOps, Monitoring, and Governance- Model monitoring
                            • 1. Performance tracking in production
                              • 2. Data drift detection
                                - Governance and compliance
                                • 1. Model lifecycle governance
                                  • 2. Feature Store usage and management

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                                    Databricks Certified Machine Learning Professional Sample Questions (Q161-Q166):

                                    NEW QUESTION # 161
                                    A Data Scientist is training a model to predict whether a customer will purchase a new product.
                                    They are experimenting with many algorithm types (e.g., Linear Regression, Logistic Regression, Decision Trees) and hundreds of hyperparameter combinations. They are using MLflow to track their runs, but managing and distinguishing between runs has become difficult. They need an MLFIow technique that will organize and trace these experiments and that will follow commonly- accepted design patterns. Which technique will fulfill these requirements?

                                    Answer: A

                                    Explanation:
                                    Using parent runs for each algorithm and nested runs for the associated hyperparameter experiments follows a widely accepted MLflow experiment design pattern. This structure makes it easy to organize, compare, and trace large numbers of related runs, improving experiment management and interpretability when exploring many models and hyperparameter configurations.


                                    NEW QUESTION # 162
                                    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 # 163
                                    A Data Scientist is building a machine learning pipeline to classify raw text using a Logistic Regression model in Spark using Spark MLlib's Pipelines. This pipeline has three stages: the Tokenizer (to split the raw text in tokens), a HashingTF (to transform tokens into hashes) and the Logistic Regression itself (to perform the classification of texts). The Spark DataFrame with the training data is called trainingDF and the one with the test data is called testDF.
                                    In order to do this, they use the following incomplete piece of code:

                                    Which option correctly states:
                                    (i) The complete command to run model training;
                                    (ii) The complete command to execute the prediction on test data;
                                    (iii) The object type of the model object returned by the model
                                    training command.

                                    Answer: D

                                    Explanation:
                                    In Spark MLlib, a Pipeline is trained using the fit method, which applies all estimator stages to the training DataFrame and returns a PipelineModel. Predictions are generated by calling transform on the fitted PipelineModel, which applies the full pipeline (tokenization, feature hashing, and logistic regression) to the test DataFrame.


                                    NEW QUESTION # 164
                                    A machine learning engineer is converting a Hyperopt-based hyperparameter tuning process from manual MLflow logging to MLflow Autologging. They are trying to determine how to manage nested Hyperopt runs with MLflow Autologging.
                                    Which of the following approaches will create a single parent run for the process and a child run for each unique combination of hyperparameter values when using Hyperopt and MLflow Autologging?

                                    Answer: E


                                    NEW QUESTION # 165
                                    Which of the following is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov-Smirnov (KS) test for numeric feature drift detection?

                                    Answer: A


                                    NEW QUESTION # 166
                                    ......

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