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

SectionObjectives
Topic 1: MLOps, Monitoring, and Governance- Governance and compliance
  • 1. Model lifecycle governance
    • 2. Feature Store usage and management
      - Model monitoring
      • 1. Performance tracking in production
        • 2. Data drift detection
          Topic 2: Model Deployment and Serving- Model deployment strategies
          • 1. Databricks Model Serving
            • 2. Batch inference vs real-time inference
              Topic 3: MLflow and Experiment Tracking- Experiment management
              • 1. Tracking runs and parameters
                • 2. Model comparison and selection
                  - Model registry
                  • 1. Versioning and lifecycle management
                    Topic 4: 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
                            Topic 5: 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

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                                    Databricks Certified Machine Learning Professional Sample Questions (Q124-Q129):

                                    NEW QUESTION # 124
                                    A Machine Learning Engineer is training a large-scale gradient boosting model using SparkML on a cluster of machines. The training job fails due to memory overflow on a single executor node after processing several iterations. The cluster resources are limited to executor nodes with 16 CPU cores and 64 GB RAM each. The engineer wants to continue training the model without changing hyperparameters or reducing the dataset size. They know Spark's architecture well and want to take advantage of its benefits. Which approach will allow the Machine Learning Engineer to solve this issue?

                                    Answer: A

                                    Explanation:
                                    Spark ML algorithms, including gradient-boosted trees, are designed around data parallelism. By increasing the number of executor nodes, the dataset can be further partitioned so each executor processes only a subset of the data, reducing per-executor memory pressure while keeping the same model configuration and dataset size. This leverages Spark's distributed architecture without requiring model parallelism or hyperparameter changes.


                                    NEW QUESTION # 125
                                    A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable. They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df.
                                    Which of the following code blocks can they use to create a function called predict that they can use to complete the task?

                                    Answer: C


                                    NEW QUESTION # 126
                                    A machine learning engineer is in the process of implementing a concept drift monitoring solution. They are planning to use the following steps:
                                    1. Deploy a model to production and compute predicted values
                                    2. Obtain the observed (actual) label values
                                    3. _____
                                    4. Run a statistical test to determine if there are changes over time
                                    Which of the following should be completed as Step #3?

                                    Answer: C


                                    NEW QUESTION # 127
                                    Which of the following is a probable response to identifying drift in a machine learning application?

                                    Answer: D


                                    NEW QUESTION # 128
                                    A Machine Learning Engineer is considering moving their functions and unit tests from notebooks into separate Python files (e.g., modules and test scripts) to take advantage of the numerous benefits of this approach like automated execution, code reusability, and version control. Which challenge should the engineer consider with this approach?

                                    Answer: B

                                    Explanation:
                                    Moving code and unit tests into separate Python modules introduces a more structured project layout, which can increase complexity. Engineers must manage directories, dependencies, and imports carefully, making the project slightly harder to navigate and maintain compared to simple notebook-based workflows, especially for teams new to this structure.


                                    NEW QUESTION # 129
                                    ......

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