Title: Quiz 6 — Guardrails Frameworks: NeMo Guardrails and LLM Guard
Course: AIINFRA 302: AI Security, Guardrails & Governance > Section: Week 06 — Guardrails Frameworks: NeMo Guardrails and LLM Guard

Q: Which of the following best describes what a guardrails framework like NeMo Guardrails or LLM Guard actually is?
- A retraining process that bakes safety directly into model weights
- A separate, programmable runtime layer that inspects, blocks, rewrites, or escalates requests and responses
- A replacement for model-level alignment
- A GPU scheduling optimization
A: A separate, programmable runtime layer that inspects, blocks, rewrites, or escalates requests and responses

Q: Once you install NeMo Guardrails and configure a topical rail, user inputs and model outputs are deterministically guaranteed to be safe.
- True
- False
A: False

Q: Name the five rail types supported by NeMo Guardrails.
Feedback: Sample/expected answer: Input, dialog, retrieval, output, and execution

Q: Which language does NeMo Guardrails use to declaratively define multi-turn dialog flows and topical rails?
- YAML
- Colang
- Jinja2
- GraphQL
A: Colang

Q: A team needs stateless, composable input/output scanning (prompt injection detection, PII redaction, secrets detection) without modeling multi-turn conversation flow. Which framework is the better architectural fit?
- NeMo Guardrails, because Colang is required for any scanning
- LLM Guard, because it provides \~15 input scanners and 20+ output scanners without dialog-management overhead
- Neither — this requires retraining the base model
- Both are functionally identical for this use case
A: LLM Guard, because it provides \~15 input scanners and 20+ output scanners without dialog-management overhead

Q: According to NVIDIA's own guidance, the built-in default rails in NeMo Guardrails are guaranteed to work out of the box for any production use case.
- True
- False
A: False

Q: What is the key engineering decision a team must make about how a guardrails system behaves when a rail check fails, times out, or errors — and why is this described as a deliberate design choice rather than a default?
Feedback: Sample/expected answer: Whether the system fails open (lets the request/response through) or fails closed (blocks it); this is called out as a deliberate design decision matched to the team's risk tolerance, not something the framework provides automatically.

Q: Which statement correctly characterizes the relationship between guardrails and model-level safety measures?
- Guardrails replace the need for least-privilege architecture and model alignment
- Guardrails complement model-level alignment and least-privilege architecture but never replace them
- Guardrails are a component inside the model's neural network weights
- Guardrails eliminate all latency and cost concerns compared to unguarded inference
A: Guardrails complement model-level alignment and least-privilege architecture but never replace them