Domain 5: Model Deployment & Production

Databricks Certified Machine Learning Associate · 60 questions

• What is required to create a Databricks Model Serving endpoint from an MLflow registered model?
• For batch inference on a large Delta table using an MLflow-logged scikit-learn model, which approach is most efficient on Databricks?
• A team wants to A/B test a new model version against the current production model on Databricks Model Serving. Which feature enables this?
• When should you use mlflow.pyfunc.PythonModel to wrap a custom model class?
• What is the difference between data drift and concept drift in production ML models?
• When using the Databricks Feature Store for real-time model serving, what component is used to retrieve pre-computed features at low latency?
• Which MlflowClient method transitions a registered model version from Staging to Production?
• How does Delta Lake's ACID transaction support benefit ML batch inference pipelines?
• When orchestrating a multi-step ML pipeline (data prep → feature engineering → training → evaluation → deployment) using Databricks Jobs, which feature enables conditional execution based on the previous step's output?
• When a model logged via the Feature Store's fs.logmodel() is deployed to Databricks Model Serving, what happens to feature retrieval automatically?
• A Databricks Model Serving endpoint has P99 latency of 500ms but the SLA requires P99 < 100ms. Which optimization should be tried first?
• What is the purpose of configuring webhooks for the MLflow Model Registry?
• Which retraining trigger strategy is most appropriate when a model's input feature distribution is known to shift significantly every 90 days?
• How can Delta Live Tables (DLT) be used in an ML pipeline context?
• In a champion/challenger model comparison using Databricks, what is the most rigorous way to determine if the challenger outperforms the champion?
• How does Databricks Model Serving determine which Python packages to install for a deployed MLflow model?
• What is the recommended approach for adding unit tests to a Databricks ML pipeline notebook before promoting to production?
• Which Databricks feature enables monitoring model prediction drift and data quality in production by storing prediction logs in a Delta table and comparing to a baseline?
• When configuring Databricks Model Serving, when is GPU serving MOST justified?
• mlflow.pyfunc is valuable because it:
• At real-time serving time, feature values should typically come from:
• Which statement correctly distinguishes data drift vs concept drift?
• Traffic splitting on Model Serving endpoints is used to:
• A common pattern for batch inference at scale on Databricks is:
• Feature Store integration with Model Serving primarily reduces:
• Lakehouse Monitoring on a predictions Delta table can detect:
• Databricks Jobs are used in ML to:
• Logging inference requests/responses (with privacy controls) enables:
• A production-ready Databricks ML system should include all EXCEPT:
• A custom pyfunc model is a good fit when:
• Scale-to-zero on serving endpoints trades:
• Online champion/challenger evaluation should compare:
• A sound retraining trigger is:
• Oversized models on small serving instances often cause:
• Choosing serverless model serving is often about:
• Choose batch inference over real-time when:
• Production model endpoints should enforce:
• Serving SLOs should track:
• Serving environment mismatches (library versions) often cause:
• An ML system design review on Databricks should verify all EXCEPT:
• Specifying conda/pip env when logging a model helps serving by:
• Shadow deployment means:
• Autoscaling model serving should react to:
• Online feature serving SLAs should include:
• Online A/B testing of models requires:
• Attaching model cards/docs to registry versions supports:
• If a canary model version degrades p95 latency and error rate, you should:
• Hosting multiple model versions behind routing logic enables:
• A robust prediction log table should include:
• An end-to-end Databricks ML associate workflow includes all EXCEPT:
• Provisioned throughput style serving is chosen when:
• Passing model version via controlled routing/headers can support:
• Mitigations for serving cold starts include:
• Asynchronous inference with queues is appropriate when:
• Blue/green style model deployment means:
• Joining delayed ground truth back to prediction logs enables:
• Multi-output regression/classification in one model is useful when:
• Client-side throttling to a model endpoint helps:
• Exporting a model's conda.yaml/requirements enables:
• End-to-end real-time scoring latency includes: