AI-300 ML Model Lifecycle And Operations Practice Question
You have an Azure Machine Learning pipeline that uses 'PipelineData' to pass information between steps. You want to share data between a training step and a scoring step. What is the recommended way to persist this data?
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
✓
Use an 'OutputDataBinding' to a registered Datastore.
'PipelineData' allows intermediate data passing, but 'OutputDataBindings' are preferred for persisting artifacts that need to be accessed later, such as model files.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Embed the data in the run metadata.
Why it's wrong here
Metadata is for small values, not file storage.
- ✗
Write the file to the local temp directory.
Why it's wrong here
Local temp is wiped after the job finishes.
- ✓
Use an 'OutputDataBinding' to a registered Datastore.
Why this is correct
This ensures the data is persisted and accessible.
- ✗
Use an environment variable to pass the file path.
Why it's wrong here
Environment variables have size limits.
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.