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

SectionObjectives
Topic 1: MLflow and Experiment Tracking- Experiment management
  • 1. Model comparison and selection
    • 2. Tracking runs and parameters
      - Model registry
      • 1. Versioning and lifecycle management
        Topic 2: Machine Learning Models and Algorithms- Supervised learning methods
        • 1. Classification and regression models
          • 2. Model evaluation metrics
            - Unsupervised learning methods
            • 1. Dimensionality reduction
              • 2. Clustering techniques
                Topic 3: 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
                        Topic 4: Model Deployment and Serving- Model deployment strategies
                        • 1. Batch inference vs real-time inference
                          • 2. Databricks Model Serving
                            Topic 5: MLOps, Monitoring, and Governance- Model monitoring
                            • 1. Data drift detection
                              • 2. Performance tracking in production
                                - Governance and compliance
                                • 1. Model lifecycle governance
                                  • 2. Feature Store usage and management

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                                    Databricks Certified Machine Learning Professional Sample Questions (Q167-Q172):

                                    NEW QUESTION # 167
                                    A machine learning engineer has developed a model and registered it using the FeatureStoreClient fs. The model has model URI model_uri. The engineer now needs to perform batch inference on customer-level Spark DataFrame spark_df, but it is missing a few of the static features that were used when training the model. The customer_id column is the primary key of spark_df and the training set used when training and logging the model.
                                    Which of the following code blocks can be used to compute predictions for spark_df when the missing feature values can be found in the Feature Store by searching for features by customer_id?

                                    Answer: C


                                    NEW QUESTION # 168
                                    Which of the following describes label drift?

                                    Answer: E


                                    NEW QUESTION # 169
                                    A Data Scientist has been performing hyperparameter tuning using Ray Tune with grid search.
                                    After team discussions, they decide to switch to Bayesian optimization to more efficiently explore the parameter space.
                                    Their current code is:

                                    How can they implement this change?

                                    Answer: A

                                    Explanation:
                                    Bayesian optimization in Ray Tune requires defining a continuous or discrete search space (such as tune.randint) and explicitly configuring a Bayesian search algorithm. Using tune.randint defines a probabilistic parameter domain, and setting search_alg to BayesOptSearch enables Bayesian optimization to efficiently explore the space based on past trial results, rather than exhaustively enumerating all values as in grid search.


                                    NEW QUESTION # 170
                                    A data scientist has developed a model model and computed the RMSE of the model on the test set. They have assigned this value to the variable rmse. They now want to manually store the RMSE value with the MLflow run.
                                    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 # 171
                                    A machine learning engineer has deployed a model recommender using MLflow Model Serving.
                                    They now want to query the version of that model that is in the Production stage of the MLflow Model Registry. Which of the following model URls can be used to query the described model version?

                                    Answer: D

                                    Explanation:
                                    In MLflow Model Serving, the correct URL pattern to query a specific stage of a registered model is https:///model/<model_name>/<stage>/invocations Thus, to query the model named recommender in the Production stage, the proper endpoint is
                                    https:///model/recommender/Production/invocations.
                                    This allows direct access to the active production version without needing its version number.


                                    NEW QUESTION # 172
                                    ......

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