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

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

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                                    Databricks Certified Machine Learning Professional Sample Questions (Q129-Q134):

                                    NEW QUESTION # 129
                                    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 Staging stage of the MLflow Model Registry. Which model URI can be used to query the described model version?

                                    Answer: D

                                    Explanation:
                                    To query a model version in a specific stage (like Staging) using MLflow Model Serving on Databricks, the correct model URI format is:
                                    https://<databricks-instance>/model/<model-name>/<stage>/invocations
                                    So, for the model named recommender in the Staging stage, the correct URI is:
                                    https://<databricks-instance>/model/recommender/Staging/invocations.


                                    NEW QUESTION # 130
                                    A machine learning engineer has created a webhook with the following code block:

                                    Which of the following code blocks will trigger this webhook to run the associate job?

                                    Answer: D


                                    NEW QUESTION # 131
                                    A Machine Learning Engineer is setting up a cluster for a deep learning training run, but has a number of settings options to choose from. Their cluster will be reused by other engineers on their team and their datasets vary in size from hundreds of MBs to hundreds of GBs. They need to choose a configuration that allows for performant, stable deep learning training without excessive costs. Which configuration will do this?

                                    Answer: D

                                    Explanation:
                                    Using the ML Runtime ensures deep learning frameworks and GPU drivers are preconfigured and optimized. A moderately sized CPU driver is sufficient for coordination, while GPU-enabled worker nodes handle the computationally intensive training workload. Enabling autoscaling allows the cluster to efficiently adapt to datasets ranging from hundreds of megabytes to hundreds of gigabytes, providing strong performance without overprovisioning and controlling costs in a shared team environment.


                                    NEW QUESTION # 132
                                    A machine learning engineer wants to programmatically create a new Databricks Job whose schedule depends on the result of some automated tests in a machine learning pipeline.
                                    Which of the following Databricks tools can be used to programmatically create the Job?

                                    Answer: A


                                    NEW QUESTION # 133
                                    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: D


                                    NEW QUESTION # 134
                                    ......

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