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

SectionWeightObjectives
Model Development47%- Feature Store
  • 1. Feature Store concepts and usage
  • 2. Automated feature pipelines
- SparkML and Scaling
  • 1. Ray/Optuna distributed tuning
  • 2. Distributed training and hyperparameter tuning
  • 3. Building scalable ML pipelines with SparkML
- MLflow Advanced Features
  • 1. Experiment tracking and model management
  • 2. Model registry and versioning
Model Lifecycle Management (MLOps)43%- Monitoring
  • 1. Model performance monitoring
  • 2. Lakehouse Monitoring for drift detection
- Automated Workflows
  • 1. Automated retraining workflows
  • 2. CI/CD pipelines for ML
- Testing Strategies
  • 1. Data quality testing
  • 2. Model testing and validation
- Environment Management
  • 1. Environment configuration and reproducibility
  • 2. Databricks Asset Bundles (DABs)
Model Deployment10%- Model Serving
  • 1. Custom serving solutions
  • 2. Databricks Model Serving
- Deployment Strategies
  • 1. Streaming Deployment
  • 2. Real-time Deployment
  • 3. Batch Deployment

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Databricks Certified Machine Learning Professional Sample Questions (Q12-Q17):

NEW QUESTION # 12
A data scientist is utilizing MLflow to track their machine learning experiments. After completing a series of runs for the experiment with experiment ID exp_id, the data scientist wants to programmatically work with the experiment run data in a Spark DataFrame. They have an active MLflow Client client and an active Spark session spark. Which of the following lines of code can be used to obtain run-level results for exp_id in a Spark DataFrame?

Answer: C


NEW QUESTION # 13
A machine learning engineering manager has asked all of the engineers on their team to add text descriptions to each of the model projects in the MLflow Model Registry. They are starting with the model project "model" and they'd like to add the text in the model_description variable.
The team is using the following line of code:

Which change does the team need to make to the above code block to accomplish the task?

Answer: D


NEW QUESTION # 14
A team is building a machine learning pipeline, which includes feature engineering, model training, model evaluation, and model deployment. Over the last year, the project has grown a lot, with multiple contributors working on it. Currently, they are manually testing their pipelines and functions. However, they are looking to build out a MLOps process and want to organize their unit tests. Which approach to organizing functions and unit tests should they take?

Answer: C

Explanation:
Separating production code into dedicated modules and organizing unit tests in a separate test suite follows standard software engineering and MLOps best practices. This structure improves maintainability, enables automated testing in CI/CD pipelines, supports collaboration among multiple contributors, and scales effectively as the project grows.


NEW QUESTION # 15
Which of the following lists all of the model stages are available in the MLflow Model Registry?

Answer: C


NEW QUESTION # 16
A machine learning engineer needs a python_model to access a collection of files using its load_context operation. The collection of files being accessed by python_model.load_context needs to be saved when the model is being logged. A dictionary of the names and paths of these files is properly stored in my_dict.
The machine learning engineer has written the following incomplete block of code:

Which lines of code can be used to fill in the blank to successfully complete the code block to accomplish the task?

Answer: D

Explanation:
When logging a custom Python model with mlflow.pyfunc.log_model(), the artifacts parameter is used to specify a dictionary (my_dict) of file names and their paths that the model may need to access during loading or inference. These files are stored as model artifacts and become available to the model through the load_context method.


NEW QUESTION # 17
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