Technical interview guide

LLM Observability & Monitoring

Monitoring, logging, and tracing LLM and agent systems in production — the specific signals and tooling that apply on top of general observability practice.

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

Scope: OpenTelemetry specification and semantic conventions reviewed at 1.44.0 on 2026-09-04, W3C Trace Context 2021 Recommendation, OpenMetrics 1.0, Google SRE monitoring, RAGAS, Lost in the Middle, Model Cards, NIST GenAI, and OWASP Prompt Injection/Sensitive Disclosure references.

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 observability objectives for an LLM product.

QA-2

Instrument an end-to-end LLM trace.

QA-3

Choose metrics, logs, traces, and evaluations for an LLM failure.

QA-4

Define model-call telemetry fields.

QA-5

Design low-cardinality LLM metrics.

QA-6

Instrument streaming LLM latency and completion.

QA-7

Build trustworthy LLM cost telemetry.

QA-8

Instrument RAG observability safely.

QA-9

Instrument a tool-using agent workflow.

QA-10

Design privacy controls for LLM telemetry.

QA-11

How do you detect hallucinations and factual drift in real-time LLM production traffic without ground truth labels?

QA-12

Design sampling for LLM traces and evaluations.

QA-13

How do you instrument distributed tracing across multi-agent workflows, recursive tool calls, and external retrieval steps?

QA-14

Add continuous LLM quality evaluation to observability.

QA-15

Use feedback signals responsibly in monitoring.

QA-16

Design drift monitoring for an LLM application.

QA-17

Design actionable LLM alerts.

QA-18

Design layered LLM observability dashboards.

QA-19

Build an incident replay workflow.

QA-20

Monitor external model-provider change risk.

QA-21

Instrument and monitor LLM caching.

QA-22

Create an LLM telemetry retention and deletion policy.

QA-23

Reconcile an observability discrepancy during incident response.

QA-24

Roll out an observability change safely.

QA-25