Databricks-Machine-Learning-Professional Free Sample Questions, Databricks-Machine-Learning-Professional Exam Dumps

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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. Pipeline construction and orchestration
        • 2. Reusable ML workflows
          Topic 2: MLflow and Experiment Tracking- Model registry
          • 1. Versioning and lifecycle management
            - Experiment management
            • 1. Model comparison and selection
              • 2. Tracking runs and parameters
                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: Model Deployment and Serving- Model deployment strategies
                        • 1. Databricks Model Serving
                          • 2. Batch inference vs real-time inference
                            Topic 5: Machine Learning Models and Algorithms- Supervised learning methods
                            • 1. Model evaluation metrics
                              • 2. Classification and regression models
                                - Unsupervised learning methods
                                • 1. Clustering techniques
                                  • 2. Dimensionality reduction

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                                    Databricks Certified Machine Learning Professional Sample Questions (Q148-Q153):

                                    NEW QUESTION # 148
                                    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: B


                                    NEW QUESTION # 149
                                    Which of the following Databricks-managed MLflow capabilities is a centralized model store?

                                    Answer: A


                                    NEW QUESTION # 150
                                    A data scientist is utilizing MLflow to track their machine learning experiments. After completing a run with run ID run_id for the experiment with experiment ID exp_id, the data scientist wants to programmatically return the logged metrics for run_id. They have an active MLflow Client client and an active Spark session spark. Which lines of code can be used to return the logged metrics for run_id?

                                    Answer: A

                                    Explanation:
                                    The correct way to retrieve logged metrics for a specific run using the MLflow Client is client.get_run(run_id).data.metrics. This returns a dictionary of all metrics logged for that run. The method in the image (mlflow.search_runs(...)) is for querying across multiple runs, not for accessing a specific run's metrics.


                                    NEW QUESTION # 151
                                    A data scientist has developed a scikit-learn random forest model model, but they have not yet logged model with MLflow. They want to obtain the input schema and the output schema of the model so they can document what type of data is expected as input.
                                    Which of the following MLflow operations can be used to perform this task?

                                    Answer: E


                                    NEW QUESTION # 152
                                    A machine learning engineer needs to select a deployment strategy for a new machine learning application. The feature values are not available until the time of delivery, and results are needed exceedingly fast for one record at a time. Which of the following deployment strategies can be used to meet these requirements?

                                    Answer: D


                                    NEW QUESTION # 153
                                    ......

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