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| Section | Weight | Objectives |
|---|
| Model Development | 47% | - Feature Store
- 1. Automated feature pipelines
- 2. Feature Store concepts and usage
- MLflow Advanced Features
- 1. Model registry and versioning
- 2. Experiment tracking and model management
- SparkML and Scaling
- 1. Distributed training and hyperparameter tuning
- 2. Building scalable ML pipelines with SparkML
- 3. Ray/Optuna distributed tuning
|
| Model Lifecycle Management (MLOps) | 43% | - Automated Workflows
- 1. Automated retraining workflows
- 2. CI/CD pipelines for ML
- Monitoring
- 1. Model performance monitoring
- 2. Lakehouse Monitoring for drift detection
- Testing Strategies
- 1. Data quality testing
- 2. Model testing and validation
- Environment Management
- 1. Environment configuration and reproducibility
- 2. Databricks Asset Bundles (DABs)
|
| Model Deployment | 10% | - Model Serving
- 1. Databricks Model Serving
- 2. Custom serving solutions
- Deployment Strategies
- 1. Batch Deployment
- 2. Streaming Deployment
- 3. Real-time Deployment
|
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Databricks Certified Machine Learning Professional Sample Questions (Q108-Q113):
NEW QUESTION # 108
A machine learning engineering team wants to build a continuous pipeline for data preparation of a machine learning application. The team would like the data to be fully processed and made ready for inference in a series of equal-sized batches. Which tool can be used to provide this type of continuous processing?
- A. MLflow
- B. Spark UDFs
- C. Delta Lake
- D. Structured Streaming
Answer: B
NEW QUESTION # 109
A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or computing predictions using that model. They decide to wrap this preprocessing in a custom model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as a pyfunc model.
Which of the following is a benefit of this approach when loading the logged pyfunc model for downstream deployment?
- A. The same preprocessing logic will automatically be applied when calling predict
- B. The pvfunc model can be used to deploy models in a parallelizable fashion
- C. This approach has no impact when loading the logged Pvfunc model for downstream deployment
- D. There is no longer a need for pipeline-like machine learning objects
- E. The same preprocessing logic will automatically be applied when calling fit
Answer: D
NEW QUESTION # 110
Which MLflow command logs a trained model?
- A. mlflow.run()
- B. mlflow.register_model()
- C. mlflow.start_run()
- D. mlflow.log_model()
Answer: D
Explanation:
mlflow.log_model() saves the model artifact within the run.
NEW QUESTION # 111
Which of the following is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov-Smirnov (KS) test for numeric feature drift detection?
- A. None of these reasons
- B. All of these reasons
- C. JS does not require any manual threshold or cutoff determinations
- D. JS is more robust when working with large datasets
- E. JS is not normalized or smoothed
Answer: D
NEW QUESTION # 112
A data scientist is using MLflow to track their machine learning experiment. As a part of each MLflow run, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values.
They are using the following code block:

The code block is not nesting the runs in MLflow as they expected.
Which of the following changes does the data scientist need to make to the above code block so that it successfully nests the child runs under the parent run in MLflow?
- A. Indent the child run blocks within the parent run block
- B. Remove the nested=True argument from the child runs
- C. Add the nested=True argument to the parent run
- D. Provide the same name to the run name parameter for all three run blocks
- E. Add the nested=True argument to the parent run and remove the nested=True arguments from the child runs
Answer: E
NEW QUESTION # 113
......
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