Databricks-Machine-Learning-Professional Exam & Databricks-Machine-Learning-Professional Fragen&Antworten

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

Section	Objectives
Topic 1: 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 2: MLflow and Experiment Tracking	- Experiment management 1. Tracking runs and parameters 2. Model comparison and selection - Model registry 1. Versioning and lifecycle management
Topic 3: Machine Learning Models and Algorithms	- Supervised learning methods 1. Classification and regression models 2. Model evaluation metrics - Unsupervised learning methods 1. Dimensionality reduction 2. Clustering techniques
Topic 4: 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
Topic 5: Model Deployment and Serving	- Model deployment strategies 1. Databricks Model Serving 2. Batch inference vs real-time inference

>> Databricks-Machine-Learning-Professional Exam <<

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Databricks Certified Machine Learning Professional Databricks-Machine-Learning-Professional Prüfungsfragen mit Lösungen (Q21-Q26):

21. Frage
A Machine Learning Engineer needs to develop fraud detection models with Databricks. They need to ensure seamless collaboration between data engineers and data scientists while maintaining strict governance, version control, and traceability as models progress from development to production. So, they need to choose the Databricks feature that will enable centralized model lineage tracking, cross-workspace access control, and automated synchronization of model versions with their training data. Which Databricks feature will do this?

A. Unity Catalog for governing models via a centralized registry with role-based access
B. MLflow Tracking for logging parameters and metrics during experimentation
C. Mosaic AI Model Serving for deploying models as REST APIs
D. Lakehouse Monitoring for tracking data quality and model drift

Antwort: A

Begründung:
Unity Catalog provides centralized governance for models, including lineage tracking between models and their training data, cross-workspace access control, and consistent version management as models move from development to production. This enables secure collaboration, traceability, and enterprise-grade model governance across teams.

22. Frage
A machine learning engineer has created a webhook with the following code block:

Which code block will trigger this webhook to run the associate job?

Antwort: C

Begründung:
The webhook in the first image is configured to trigger on the
MODEL_VERSION_TRANSITIONED_TO_PRODUCTION event. The chosen code uses
client.transition_model_version_stage(...) to move the model version to "Production", which matches the event type in the webhook and will correctly trigger the associated job.

23. Frage
A machine learning engineer is manually refreshing a model in an existing machine learning pipeline. The pipeline uses the MLflow Model Registry model "project". The machine learning engineer would like to add a new version of the model to "project". Which MLflow operation can the machine learning engineer use to accomplish this task?

A. MlflowClient.get_model_version
B. MlflowClient.update_registered_model
C. mlflow.register_model
D. The machine learning engineer needs to create an entirely new MLflow Model Registry model
E. mlflow.add_model_version

Antwort: B

24. Frage
A data scientist has run the following line of code to create a Feature Store table:

Which statement correctly describes the data in the Feature Store table new_table?

A. The data in new_table is exactly the same as the data in df
B. The data in new_table is the values in column customer_id from df
C. There is no data in new_table
D. The data in new_table is the customer-related features from df

Antwort: C

Begründung:
The fs.create_table() command defines metadata for a Feature Store table, such as its name, primary key, schema, and description - but it does not insert any data into the table. This command simply registers an empty table structure based on the provided schema (df.schema).
To populate the table with data, a separate fs.write_table() or equivalent write operation must be executed afterward.

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

Antwort: B

26. Frage
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