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

SectionObjectives
Machine Learning Models and Algorithms- Supervised learning methods
  • 1. Classification and regression models
    • 2. Model evaluation metrics
      - Unsupervised learning methods
      • 1. Clustering techniques
        • 2. Dimensionality reduction
          MLflow and Experiment Tracking- Experiment management
          • 1. Model comparison and selection
            • 2. Tracking runs and parameters
              - Model registry
              • 1. Versioning and lifecycle management
                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. Batch inference vs real-time inference
                          • 2. Databricks Model Serving
                            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

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                                    Databricks Certified Machine Learning Professional Sample Questions (Q61-Q66):

                                    NEW QUESTION # 61
                                    A machine learning engineer needs to select a deployment strategy for a new machine learning application. The machine learning application requires central prediction computation and exceedingly fast results, but only a handful of predictions need to be computed at a time. Which deployment strategy can be used to meet these requirements?

                                    Answer: B

                                    Explanation:
                                    Real-time deployment is the appropriate strategy when predictions need to be computed centrally with very low latency, even if only a small number of predictions are required at a time. This ensures fast responses for applications requiring immediate inference.


                                    NEW QUESTION # 62
                                    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: C

                                    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 # 63
                                    Which approach is best for scoring large historical datasets?

                                    Answer: D

                                    Explanation:
                                    Spark allows distributed batch scoring across massive datasets.


                                    NEW QUESTION # 64
                                    Which of the following is a simple statistic to monitor for categorical feature drift?

                                    Answer: A


                                    NEW QUESTION # 65
                                    A Machine Learning Engineer needs a continuous deployment pipeline for their models hosted on Databricks Model Serving. The deployment automation should execute after a model is trained and registered using MLflow. The goal of the automation is to deploy the latest version of the model from the MLflow Model Registry only if the model can meet the company's strict latency requirements (P95 < 300ms) while serving production traffic. How can the engineer validate that new models meet their latency requirements when served in production?

                                    Answer: D

                                    Explanation:
                                    Validating latency requirements must be done under real production serving conditions. Routing a small percentage of live production traffic to the new model using Databricks Model Serving allows accurate measurement of end-to-end serving latency. Inference tables capture request latency metrics, enabling calculation of the P95 latency and ensuring it meets the strict production threshold before full rollout.


                                    NEW QUESTION # 66
                                    ......

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