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

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

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                                    Databricks Certified Machine Learning Professional Sample Questions (Q63-Q68):

                                    NEW QUESTION # 63
                                    Which statement is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov- Smirnov (KS) test for numeric feature drift detection?

                                    Answer: C


                                    NEW QUESTION # 64
                                    Which of the following describes concept drift?

                                    Answer: D


                                    NEW QUESTION # 65
                                    Which tool can be used to automatically start a testing Job when a new version of an MLflow Model Registry model is registered?

                                    Answer: C

                                    Explanation:
                                    MLflow Model Registry Webhooks can be configured to automatically trigger actions - such as running a testing job - when events occur, like registering a new model version. This enables automation in CI/CD workflows for machine learning models.


                                    NEW QUESTION # 66
                                    A machine learning engineer has a machine learning pipeline where predictions are updated annually. The final prediction dataset contains millions of rows, and that dataset is irregularly accessed. Which solution should the machine learning engineer use to maintain cost efficiency?

                                    Answer: C

                                    Explanation:
                                    For data that is large in size, infrequently accessed, and updated only periodically, cloud-based object storage (such as AWS S3, Azure Blob Storage, or Google Cloud Storage) is the most cost- efficient option. It offers durable, scalable, and inexpensive storage compared to in-memory or low-latency databases, which are optimized for frequent access and real-time performance rather than long-term, infrequent retrieval.


                                    NEW QUESTION # 67
                                    A Data Scientist needs to perform inference on a continuously updated Delta table called sales_data using an MLflow-registered Spark ML pipeline model (catalog.prod.sales_forecaster).
                                    Predictions must be written to a Delta table forecast_results, which must be updated with low latency leveraging a cluster with three executors. They want to maximize the efficient use of their cluster when doing this. Which approach will suit their needs?

                                    Answer: B

                                    Explanation:
                                    This approach uses a Spark Structured Streaming read from the continuously updated Delta table and applies an MLflow-registered Spark UDF for inference. The model execution is distributed across the three executors, enabling parallel, low-latency scoring as new data arrives. Writing the results with writeStream efficiently updates the forecast_results Delta table incrementally, maximizing cluster utilization and aligning with best practices for continuous, scalable batch- stream inference in Databricks.


                                    NEW QUESTION # 68
                                    ......

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