AI-300 ML Model Lifecycle And Operations Practice Question
You are implementing a retraining trigger for a demand forecasting model. You want to trigger a pipeline execution only when the drift metric for the 'Price' feature exceeds a predefined threshold. Which service should you integrate with Azure Machine Learning?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure Event Grid and Logic Apps.
Azure Machine Learning Data Drift Monitors can be configured to emit events to Azure Event Grid, which can then trigger an Azure Logic App or Azure Function to start the pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Azure Batch schedules.
Why it's wrong here
Batch schedules are time-based, not event-based.
- ✗
Azure Data Factory triggers.
Why it's wrong here
Data Factory is for data movement, not drift-based triggers.
- ✗
Azure Monitor alerts on logs.
Why it's wrong here
Monitor alerts are for health, not triggering ML workflows.
- ✓
Azure Event Grid and Logic Apps.
Why this is correct
This is the native integration pattern for drift-based automation.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
This AI-300 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-300 exam.