{
  "lesson": "09-hybrid-memory-mem0",
  "title": "Hybrid Memory: Vector + Graph + KV",
  "questions": [
    {
      "stage": "pre",
      "question": "Which query class does a KV store handle best?",
      "options": [
        "Reachability across customers sharing a billing entity",
        "Direct fact lookup keyed by (user, type, entity)",
        "Temporal queries valid-at-time",
        "Semantic similarity over long conversations"
      ],
      "correct": 1,
      "explanation": "KV is O(1) on exact keys; vector is for similarity, graph is for relationships."
    },
    {
      "stage": "pre",
      "question": "What are the three stores Mem0 writes in parallel on each add()?",
      "options": [
        "Postgres, Redis, ClickHouse",
        "Cache, queue, log",
        "Embedding, attention, FFN",
        "Vector, KV, graph"
      ],
      "correct": 3,
      "explanation": "Mem0 fans every write out to vector, KV, and graph stores."
    },
    {
      "stage": "check",
      "question": "What three dimensions feed Mem0's fusion score?",
      "options": [
        "Confidence, perplexity, BLEU",
        "Precision, recall, F1",
        "Relevance, importance, recency",
        "Latency, throughput, cost"
      ],
      "correct": 2,
      "explanation": "Score is a weighted sum of relevance, importance, and recency; weights tune per product."
    },
    {
      "stage": "check",
      "question": "What does Mem0g do when an incoming fact contradicts an existing edge?",
      "options": [
        "Raises an exception",
        "Rewrites the user_id",
        "Marks the existing edge invalid but does not delete it, so temporal queries can still traverse",
        "Deletes the edge"
      ],
      "correct": 2,
      "explanation": "Soft invalidation preserves history for temporal (valid-at-time) queries."
    },
    {
      "stage": "check",
      "question": "Why does the lesson recommend tuning fusion weights per product?",
      "options": [
        "It is required by Apache 2.0",
        "Vector libraries reject equal weights",
        "Recency dominates for chat agents while importance dominates for compliance agents and relevance dominates for retrieval agents",
        "Providers require it"
      ],
      "correct": 2,
      "explanation": "Different products want different bias on relevance/importance/recency; one set of weights does not fit all."
    },
    {
      "stage": "post",
      "question": "What is the scope taxonomy Mem0 uses?",
      "options": [
        "User, session, agent",
        "Local, regional, global",
        "Public, private, secret",
        "Read, write, admin"
      ],
      "correct": 0,
      "explanation": "Scopes are user (cross-session), session (one thread), agent (per-instance state)."
    },
    {
      "stage": "post",
      "question": "What is embedding drift in this pattern, and how does the lesson recommend mitigating it?",
      "options": [
        "Vectors get encrypted; rotate keys",
        "The embedding API changes URL; pin a domain",
        "Embeddings overflow integers; switch to float64",
        "Vector retrieval quality degrades as the corpus grows; periodically re-embed the top-N most used records"
      ],
      "correct": 3,
      "explanation": "Periodic re-embedding of hot records keeps retrieval quality steady as the corpus grows."
    }
  ]
}