Databricks-GenAI-Assoc Design Applications Practice Question
A generative AI engineer is designing a multi-stage RAG application on Databricks. The application first retrieves documents using Vector Search, then reranks them with a cross-encoder model, and finally calls a foundation model endpoint to generate an answer. The engineer wants to ensure that the entire pipeline is reproducible and that each stage can be independently versioned and deployed. Which design approach best meets these requirements?
⚠ Common exam trap
The trap here is assuming that separate endpoints or a single script provide sufficient versioning and reproducibility, when the key is to encapsulate each stage as an MLflow model and compose them into a unified pipeline.
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
✓
Package each stage as an MLflow model with its own signature and dependencies, and compose them into a single MLflow pipeline that can be logged and served as one model.
Packaging each stage as an MLflow model with its own signature and dependencies allows independent versioning. Composing them into a single MLflow pipeline enables logging and serving the entire multi-stage RAG application as one model, ensuring reproducibility. This approach is supported by Databricks Model Serving and MLflow, and it simplifies deployment and tracing compared to separate endpoints or scripts.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Package each stage as an MLflow model with its own signature and dependencies, and compose them into a single MLflow pipeline that can be logged and served as one model.
Why this is correct
MLflow models encapsulate code, environment, and signatures, enabling independent versioning. Composing them into a pipeline allows the entire multi-stage application to be logged as a single model, which can be served with Databricks Model Serving. This provides reproducibility and independent versioning of each stage while offering a unified deployment artifact. It is the recommended pattern for complex generative AI pipelines.
- ✗
Implement the pipeline as a single Python script that calls all stages sequentially, and version the script in Git.
Why it's wrong here
A single script couples all stages, making it difficult to version and deploy each stage independently. Changes to one stage require redeploying the whole pipeline, and reproducibility depends on manual environment management. While Git provides version control for the script, it does not address independent versioning of models or retrieval indexes. This approach does not meet the requirement for independent stage versioning.
- ✗
Use MLflow Projects to define each stage as a separate project, and orchestrate them with a Databricks Job that passes artifacts between stages.
Why it's wrong here
MLflow Projects can package code, but they are not designed for low-latency online inference pipelines. Orchestrating with a Databricks Job introduces batch execution semantics, which is unsuitable for real-time RAG. It also does not provide a unified serving interface for the stages. This approach is better for batch workflows than for a reproducible, independently deployable online pipeline.
- ✗
Deploy each stage as a separate Databricks Model Serving endpoint and have the client application call them in sequence.
Why it's wrong here
Separate endpoints for each stage increase network latency and complexity, and the client must manage orchestration and error handling. While each endpoint can be versioned, the overall pipeline is not packaged as a single reproducible artifact. This design also complicates end-to-end tracing and monitoring. It does not provide the unified, reproducible pipeline required.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-GenAI-Assoc practice question is part of Courseiva's free Databricks 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 Databricks-GenAI-Assoc exam.