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

Section	Objectives
Model Deployment and Serving	- Model deployment strategies 1. Batch inference vs real-time inference 2. Databricks Model Serving
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
MLflow and Experiment Tracking	- Experiment management 1. Tracking runs and parameters 2. Model comparison and selection - Model registry 1. Versioning and lifecycle management
MLOps, Monitoring, and Governance	- Governance and compliance 1. Model lifecycle governance 2. Feature Store usage and management - Model monitoring 1. Data drift detection 2. Performance tracking in production
Machine Learning Models and Algorithms	- Unsupervised learning methods 1. Dimensionality reduction 2. Clustering techniques - Supervised learning methods 1. Classification and regression models 2. Model evaluation metrics

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Databricks Certified Machine Learning Professional Sample Questions (Q169-Q174):

NEW QUESTION # 169
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?

A. Compute the evaluation metric using the observed and predicted values
B. Measure the latency of the prediction time
C. Retrain the model
D. Obtain the observed values (actual) feature values
E. None of these should be completed as Step #3

Answer: E

NEW QUESTION # 170
A machine learning engineer has developed a machine learning pipeline that produces a scikit- learn model model and computes the RMSE rmse. MAE mae, and R-squared r2 values for the test set. They now want to log these values with the MLflow run. These values are stored in the dictionary metrics.
They run the following code block:

The code block produces an error.
Which changes to the code block will successfully complete the task?

A. Replace mlflow.log_metric with mlflow.sklearn.log_metric
B. Replace metrics with rmse, mae, r2
C. Replace log_metric with log_metrics
D. Replace metrics with model

Answer: C

Explanation:
The method mlflow.log_metric() logs a single metric, while mlflow.log_metrics() is used to log multiple metrics at once from a dictionary. Since metrics is a dictionary containing rmse, mae, and r2, the correct function is mlflow.log_metrics(metrics).

NEW QUESTION # 171
A Machine Learning Engineer has automated a model retraining job in Databricks. Each scheduled run trains multiple candidate models with new sales data and logs all runs with MLflow.
The goal is to select and register the best-performing model at the end of each cycle to ensure optimal forecast accuracy. Which approach will meet this goal?

A. Register all models from the run since all of them are trained on newer data and hence will be more accurate.
B. Register the first model that has an evaluation metric better than the previously deployed model.
C. Register the model with the lowest training loss regardless of validation metrics.
D. Register the best model based on the primary evaluation metric.

Answer: D

Explanation:
Selecting and registering the model that performs best on the primary evaluation metric ensures that only the highest-quality model is promoted at each retraining cycle. This approach aligns with MLOps best practices by basing promotion decisions on objective performance criteria rather than training order or assumptions about data freshness.

NEW QUESTION # 172
Which MLflow command logs a trained model?

A. mlflow.run()
B. mlflow.start_run()
C. mlflow.log_model()
D. mlflow.register_model()

Answer: C

Explanation:
mlflow.log_model() saves the model artifact within the run.

NEW QUESTION # 173
Which of the following lists all of the model stages are available in the MLflow Model Registry?

A. None. Staging. Production. Archived
B. Development. Staging. Production. Archived
C. Staging. Production. Archived
D. None. Staging. Production
E. Development. Staging. Production

Answer: A

NEW QUESTION # 174
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