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

SectionObjectives
Topic 1: 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
          Topic 2: 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
                  Topic 3: 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
                          Topic 4: Model Deployment and Serving- Model deployment strategies
                          • 1. Databricks Model Serving
                            • 2. Batch inference vs real-time inference
                              Topic 5: MLflow and Experiment Tracking- Experiment management
                              • 1. Model comparison and selection
                                • 2. Tracking runs and parameters
                                  - Model registry
                                  • 1. Versioning and lifecycle management

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                                    Databricks Certified Machine Learning Professional Sample Questions (Q49-Q54):

                                    NEW QUESTION # 49
                                    A Machine Learning Engineer has a large dataset with a customer_region column and wants to train separate models for each region, then generate predictions. They need to parallelize this group-specific model training process using Databricks and the Pandas Function API. Which approach will implement this solution?

                                    Answer: C

                                    Explanation:
                                    The Pandas Function API supports parallel, group-specific processing by using groupBy on the grouping column and applyInPandas to execute a custom training function independently for each group. This enables separate models to be trained per region in parallel across the cluster, with each function invocation receiving only the data for its group.


                                    NEW QUESTION # 50
                                    Which MLflow component is used to log parameters, metrics, and artifacts during model training?

                                    Answer: A

                                    Explanation:
                                    MLflow Tracking records experiment runs including:
                                    parameters
                                    metrics
                                    artifacts (models, plots, etc.)


                                    NEW QUESTION # 51
                                    A machine learning engineer has found drift in a production machine learning application. The engineer has determined that retraining and deploying a new model is necessary. Which statement must be true prior to deploying the new model?

                                    Answer: A

                                    Explanation:
                                    When drift is detected, it indicates that the data distribution has changed. Therefore, the new model must perform better than the original model on the most recently available data, as that data reflects current conditions. This ensures the new model is well-adapted to the current environment and will deliver more reliable predictions.


                                    NEW QUESTION # 52
                                    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: C


                                    NEW QUESTION # 53
                                    A Machine Learning Engineer is building an application that requires low latency data lookups in response to a user's question following a RAG based search. They want to ensure their users can receive as recent data as possible for urgent requests, so data should not be more than a few minutes late. The underlying data is a large table that may contain hundreds of gigabytes of data. Which data serving approach will suit their use case?

                                    Answer: D

                                    Explanation:
                                    Online tables with continuous sync mode are designed for low-latency serving while keeping data fresh within minutes. Continuous sync incrementally propagates updates from the large underlying table to the online store, ensuring near-real-time availability for RAG-based lookups without requiring full refreshes, which is ideal for urgent, freshness-sensitive queries on large datasets.


                                    NEW QUESTION # 54
                                    ......

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