Week 15: Quiz (10 questions, 8/10 to pass)

Each question names the section or notebook cell it comes from. Answer, then check the key.

1. (MCQ) In the state-graph model, what does a node return? (see Concepts §"The state-graph mental model" and notebook cell [6])

   • (a) The full state, re-serialized from scratch.
   • (b) A partial state, only the fields the node changed, which the framework merges back.
   • (c) A string answer to print.
   • (d) Nothing; nodes mutate global variables.

2. (MCQ) compute_refund(shipment_id) in notebook cell [4] returns 10% of declared value when the shipment is more than 48 hours late, and 50% when more than 7 days late. A shipment delayed 60 hours with declared value $6,000 yields which outcome? (see Concepts §"Worked example 1")

   • (a) $0 (not late enough)
   • (b) $600, and needs_approval = True because it exceeds $500
   • (c) $3,000, and needs_approval = False
   • (d) $600, and needs_approval = False

3. (MCQ) What does interrupt_before=["refund_approval"] actually do? (see Concepts §"Human-in-the-loop" and notebook cell [8])

   • (a) It deletes the refund_approval node.
   • (b) It pauses the graph before refund_approval and yields control to the caller, who must resume to continue.
   • (c) It routes every ticket to refund_approval.
   • (d) It logs a warning but keeps running.

4. (MCQ) In notebook cell [10], two invocations run on the same thread_id (t-1). What is the observable consequence, and what does it demonstrate? (see Concepts §"Checkpoints, resume, and time-travel")

   • (a) The second run crashes because the thread already exists.
   • (b) The history list accumulates across runs, durable conversation memory as state.
   • (c) The second run resets history to empty.
   • (d) The two runs are isolated and share nothing.

5. (MCQ) Which of these is the correct reason to adopt LangGraph rather than keep a Week-14 hand-rolled loop? (see Concepts §"Framework judgment")

   • (a) It makes every agent faster.
   • (b) You need durable, auditable, cyclic control flow, checkpoints, resume, HITL.
   • (c) It removes the need for tools.
   • (d) It is always less code.

6. (Short answer) Name three distinct fields in AgentState, and for each state which node writes it. (see Concepts §"The state-graph mental model" table and notebook cell [2])

7. (Short answer) A checkpoint stores more than the final answer. List the three things a checkpoint records, and explain which one makes "resume after a crash" different from "start over." (see Concepts §"Checkpoints, resume, and time-travel")

8. (MCQ) In route_after_intent, an intent of "escalate" returns which key, and that key maps to which node in add_conditional_edges? (see notebook cell [6] and [8])

   • (a) "tool" → tool_node
   • (b) "refund_approval" → refund_approval_node
   • (c) "escalate" → escalate_node
   • (d) "generate" → generate_node

9. (Short answer) The per-node table (notebook cell [14]) reports calls, latency, and tokens per node. Why is it a mistake to measure only the final answer's latency, and which node do you expect to dominate output tokens in a tracking-heavy sample? (see Concepts §"Worked example 2")

10. (MCQ) When LangGraph is not installed, what does the notebook do so the per-node table and routing accuracy still print? (see notebook cell [0] and [8])

    • (a) It raises and stops.
    • (b) A manual runner walks the same node functions in the same graph order.
    • (c) It skips all the cells.
    • (d) It installs LangGraph automatically.

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Answer key

1. (b): A node returns only the fields it changed; the framework merges them back into the shared state. Returning the full state or mutating globals defeats the explicit-state contract.

2. (b): 60 hours > 48h but < 7 days → the 10% branch: $6,000 × 0.10 = $600, which exceeds the $500 threshold, so needs_approval = True.

3. (b): interrupt_before pauses before the named node and yields control; the run only proceeds after a resume (e.g. Command(resume=\{"approved": True\})).

4. (b): Two runs on the same thread share checkpointed state, so history accumulates. That is durable conversation memory, not a hidden variable.

5. (b): LangGraph earns its complexity for durable, auditable, cyclic control flow. The other options are false (it's more machinery, not automatically faster or shorter).

6. Any three, e.g. intent (written by intent_node), shipment_id (by intent_node), refund_amount and needs_approval (by intent_node), tool_result (by tool_node), final_answer (by generate/escalate/refund_approval), history (by every node), escalated (by escalate_node). Full credit for three correct field→writer pairs.

7. A checkpoint records (1) the full current state (all fields), (2) the thread id, and (3) the position in the graph. The state + position is what makes resume different: you continue from the exact node and values where you stopped, rather than re-running from the first node with an empty state.

8. (c): "escalate" → escalate_node; that is the human-handoff branch of the conditional edge.

9. Measuring only the final answer hides where the time and tokens go, a slow tool node and a slow generate node are different problems with different fixes. In a tracking-heavy sample, tool dominates output tokens because it emits the JSON tracking/policy results.

10. (b): The notebook defines a manual_run fallback that walks the same nodes in the same order, so the per-node table and routing accuracy print regardless of whether LangGraph is installed.