Technical interview guide

LLMOps: Deployment, Versioning & Cost Management

Safely shipping changes to prompts, models, and RAG configuration in production, and keeping the resulting system's cost under control.

Read: 45 min
Practice MCQs: 25
Interview QA: 25
Edition: v4
Editorial status: Reviewed
Relevant for: AI EngineerAgentic AI EngineerForward Deployed Engineer (FDE)

Scope: SLSA 1.2, OCI Image Spec, OpenFeature specification, Google SRE release/canary guidance, FinOps Framework, Model Cards, Datasheets, PagedAttention, GPTQ, NIST GenAI, and OWASP Supply Chain references reviewed 2026-09-04.

Interview QA

Treat each question like a live interview question: answer out loud first (structure, assumptions, tradeoffs), then open the model answer to spot gaps and rehearse a tighter follow-up.

Curated: 2026-10-05 · Written: 2026-10-05 · Reviewed: 2026-10-03

QA-1

Define an LLM release manifest.

QA-2

Secure the LLM artifact supply chain.

QA-3

Design environment promotion for LLM releases.

QA-4

Build a compatibility matrix for model artifacts.

QA-5

Design CI/CD gates for an LLM stack.

QA-6

Design safe shadow evaluation for an LLM release.

QA-7

Implement controlled LLM canary routing.

QA-8

How do you version, track, and package fine-tuned LoRA adapters alongside base model weights in an enterprise model registry?

QA-9

Design state-safe rollback for an LLM system.

QA-10

Migrate LLM memory or RAG state during deployment.

QA-11

Design multi-provider failover.

QA-12

Build LLM unit economics and allocation.

QA-13

Create a capacity and cost forecast.

QA-14

Design runtime LLM budget enforcement.

QA-15

Prioritize an LLM cost optimization backlog.

QA-16

Design safe caching for LLM cost reduction.

QA-17

Design an LLM batch-processing path.

QA-18

Evaluate continuous batching for self-hosted inference.

QA-19

Release a quantized model safely.

QA-20

Plan capacity for an LLM service.

QA-21

Design graceful degradation under LLM budget pressure.

QA-22

Instrument LLMOps release observability.

QA-23

Run an LLM supply-chain incident response.

QA-24

Plan model or prompt retirement.

QA-25