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

Section	Objectives
Topic 1: Model Deployment and Serving	- Model deployment strategies 1. Databricks Model Serving 2. Batch inference vs real-time inference
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: Machine Learning Workflow on Databricks	- End-to-end ML pipelines 1. Reusable ML workflows 2. Pipeline construction and orchestration - Data preparation and feature engineering 1. Feature engineering techniques 2. Data ingestion and cleaning in Databricks
Topic 4: MLOps, Monitoring, and Governance	- Model monitoring 1. Performance tracking in production 2. Data drift detection - Governance and compliance 1. Model lifecycle governance 2. Feature Store usage and management
Topic 5: Machine Learning Models and Algorithms	- Unsupervised learning methods 1. Clustering techniques 2. Dimensionality reduction - Supervised learning methods 1. Model evaluation metrics 2. Classification and regression models

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Databricks Certified Machine Learning Professional Databricks-Machine-Learning-Professional Prüfungsfragen mit Lösungen (Q182-Q187):

182. Frage
A Machine Learning Engineer is building a fraud detection model that needs to use both pre- computed features from a feature table and real-time calculated features based on user location data sent with each inference request. The engineer has created a Python UDF called calculate_distance in Unity Catalog at main.fraud_detection.calculate_distance that computes the distance between a transaction location and the user's current location. The feature table main.fraud_detection.user_features contains historical user spending patterns with primary key user_id.
The engineer has written the following code to implement this scenario:

Which benefit of this implementation approach makes it suited to the real-time fraud detection use case?

A. The FeatureFunction caches computed distance values in the online store to improve inference latency for location pairs.
B. The model automatically performs feature lookup and computation during inference without additional serving code.
C. The Unity Catalog registry automatically creates REST API endpoints for UDF functions used in feature computation.
D. The FeatureLookup function avoids the need for joining the full table main.fraud_detection.user_features with training_set increasing efficiency.

Antwort: B

Begründung:
By defining both FeatureLookup and FeatureFunction objects in the training set and logging the model with the FeatureEngineeringClient, the feature logic is packaged with the model. During inference, Databricks automatically performs feature lookups from the feature table and computes the on-demand distance feature using request-time inputs, without requiring any additional custom serving or feature-joining code. This makes the approach well suited for real-time fraud detection.

183. Frage
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?

Antwort: C

184. Frage
A Machine Learning Engineer has a real-time fraud detection model deployed that approves or blocks millions of transactions daily. They need to deploy a new version of the model with improved detection accuracy to this high-traffic, business-critical application. Because any model downtime could result in lost revenue or customer dissatisfaction, the engineer must ensure zero downtime and minimal disruption for end users. Leadership also requires that any rollback to the previous version be immediate if issues are detected with the new model in production. Which deployment strategy meets these requirements?

A. Use a blue-green deployment for the new model, maintaining two separate production environments (one "blue," one "green") and switching user traffic to the new version only after confirming it is healthy, enabling instant rollback if needed.
B. Deploy the new model in parallel with the old version, monitor both for a set period, and then notify all stakeholders to manually switch over to the new version at a coordinated time.
C. Replace the current production model with the new version during a scheduled maintenance window, notify affected users of potential brief disruptions, and prepare to reroute requests to a backup if failures occur after deployment.
D. Use a canary deployment by initially routing a small percentage of user traffic to the new model version, monitoring results, and gradually increasing exposure until all traffic uses the new version if no problems are detected.

Antwort: A

Begründung:
A blue-green deployment maintains two fully operational production environments and shifts traffic between them instantly. This approach provides zero downtime during deployment and allows immediate rollback to the previous model version if issues arise, which is critical for high- traffic, business-critical real-time systems.

185. Frage
Which of the following MLflow Model Registry use cases requires the use of an HTTP Webhook?

A. None of these use cases require the use of an HTTP Webhook
B. Sending a message to a Slack channel when a model version transitions stages
C. Updating data in a source table for a Databricks SQL dashboard when a model version transitions to the Production stage
D. Starting a testing job when a new model is registered
E. Sending an email alert when an automated testing Job fails

Antwort: C

186. Frage
A machine learning engineer has developed a model and registered it using the FeatureStoreClient fs. The model has model URI model_uri. The engineer now needs to perform batch inference on customer-level Spark DataFrame spark_df, but it is missing a few of the static features that were used when training the model. The customer_id column is the primary key of spark_df and the training set used when training and logging the model.
Which of the following code blocks can be used to compute predictions for spark_df when the missing feature values can be found in the Feature Store by searching for features by customer_id?

A. df = fs.get_missing_features(spark_df, model_uri)
fs.score_batch(model_uri, df)
B. fs.score_batch(model_uri, spark_df)
C. fs.score_model(model_uri, spark_df)
D. df = fs.get_missing_features(spark_df)
fs.score_batch(model_uri, df)
E. df = fs.get_missing_features(spark_df, model_uri)
fs.score_model(model_uri, df)

Antwort: B

187. Frage
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