{
  "lesson": "23-sre-for-ai",
  "title": "SRE for AI — Multi-Agent Incident Response, Runbooks, Predictive Detection",
  "questions": [
    {
      "stage": "pre",
      "question": "What multi-agent shape does the lesson recommend for AI SRE?",
      "options": [
        "Supervisor agent that breaks the incident into sub-queries for specialized log, metric, and runbook agents, then synthesizes and presents to a human",
        "One monolithic agent owning everything",
        "Random selection of one of three agents",
        "Two agents in series with no supervisor"
      ],
      "correct": 0,
      "explanation": ""
    },
    {
      "stage": "check",
      "question": "Which auto-remediation set does the lesson call safe?",
      "options": [
        "Modifying IAM policies",
        "Altering databases",
        "Re-architecting service topology",
        "Restart pod, revert a specific deploy, scale a pool within pre-approved bounds, enable a pre-approved feature flag"
      ],
      "correct": 3,
      "explanation": ""
    },
    {
      "stage": "check",
      "question": "How does NeuBird Hawkeye use adversarial evaluation to filter hallucinated root causes?",
      "options": [
        "Picks the higher-confidence model's answer always",
        "Runs the same model twice on the same input",
        "Two models independently analyze the same incident; agreement = high confidence, disagreement = escalate to human with both hypotheses",
        "Uses GAN-style training"
      ],
      "correct": 2,
      "explanation": ""
    },
    {
      "stage": "check",
      "question": "What does operational memory solve in AI SRE?",
      "options": [
        "Loss of tribal knowledge when teams turn over — runbooks and post-mortems live in a vector DB that agents retrieve on every incident",
        "Network egress filtering",
        "Cold start of inference pods",
        "Token cost attribution"
      ],
      "correct": 0,
      "explanation": ""
    },
    {
      "stage": "post",
      "question": "What MIT 2025 result does the lesson cite for pre-incident prediction?",
      "options": [
        "Predictions remain unsolved",
        "100% prediction with 1-second lead",
        "89% of outages predicted 10-15 minutes early using logs + GPU temps + API error patterns",
        "10% with no lead time"
      ],
      "correct": 2,
      "explanation": ""
    },
    {
      "stage": "post",
      "question": "What operational constraint does the lesson stress about predictive detection?",
      "options": [
        "Predictions replace runbooks",
        "Predictions without actuation are just dashboards — the operational question is what action (pre-drain, page, auto-scale) the prediction triggers",
        "Predictions should never be wired to action",
        "Predictions are always accurate"
      ],
      "correct": 1,
      "explanation": ""
    }
  ]
}