Practice questions, answered and explained

A team moves to goal-oriented delegation and finds that subagents reach conclusions the coordinator cannot audit. What restores visibility without giving up adaptability?

• A Have the coordinator re-run each subagent's investigation for itself

Re-running each investigation in the coordinator discards the delegation entirely.

• B Return to procedural instructions, so that the path is known in advance

Reverting to procedure gives up the adaptability the team moved to goal-oriented delegation to gain.

• C Require each conclusion to come back with its evidence and its method

Correct. Goal-oriented delegation controls the destination rather than the route, so control is reasserted through the output contract.

• D Reduce each subagent's tool access, so that fewer paths are possible

Narrowing tools limits where the subagent can look without making its reasoning visible.

Goal-oriented delegation controls the destination, not the route — so control is reasserted through the output contract. Requiring evidence and method with every conclusion keeps the coordinator able to audit the reasoning while the subagent stays free to adapt its path.

You are prompting a verification subagent to test one claimed bug. What does its prompt need in order to complete without a round trip?

• A The claim, its exact location, the code itself, and the bar for refuting it

Correct. Verification needs the specific assertion, where to look, the material to look at, and a defined bar for refutation.

• B The claim, together with an instruction telling the verifier to be skeptical

Skepticism without evidence produces an opinion rather than a verdict.

• C The coordinator's own system prompt, so the verifier shares its standards

The coordinator's system prompt conveys standing behavior, not the claim under test.

• D The full list of all findings, so this one can be ranked against the others

The other findings are irrelevant to whether this one is real.

Verification needs the specific assertion, where to look, the material to look at, and a defined bar for refutation. Skepticism without evidence produces an opinion; the other findings are irrelevant to whether this one is real.

Using Managed Agents, where do the model, system prompt, and tool definitions belong?

• A On each session, so that every run can be configured independently of others

Putting configuration on sessions defeats the versioning the surface exists to provide, and it makes every run a fresh definition.

• B On the persisted agent object, which sessions then reference by its stored ID

Correct. Configuration lives on the persisted, versioned agent, and each session pins to it by ID — create the agent once, start a session per run.

• C In the first user message of each session, where the model reads them directly

Instructions in a user message are conversation content, not configuration, and they would have to be repeated on every run.

• D In the environment definition, alongside the container and networking settings

The environment describes the container and its networking; it does not carry the model or the prompt.

The mandatory flow is agent once, session per run: configuration lives on the versioned agent object and sessions pin to it. Putting configuration on sessions defeats the persistence and versioning the surface exists to provide.

An agent needs to remember facts about a user across sessions that end and restart days apart. Which mechanism fits?

• A Prompt caching, since the cached prefix persists between separate sessions

The cache is a short-lived cost optimization on a repeated prefix, not a store that survives days between sessions.

• B A larger context window, which holds more of the history at one time

A window holds one request's material; it does nothing once the conversation has ended.

• C A memory mechanism that persists state outside the conversation itself

Correct. Cross-session recall needs durable storage outside the conversation, loaded back in when the next session starts.

• D Extended thinking, which lets the model reason over earlier sessions

Thinking is reasoning within a request and gives the model no access to anything from an earlier session.

Cross-session recall requires durable storage outside the conversation, since a new session starts with no history. Caching is a cost optimization on repeated prefixes with a short lifetime, and a bigger window does nothing once the conversation has ended.

An employee supplies a policy document and asks a question about it. The answer is fluent and cites a section number. What is the most reliable way to check it?

• A Open the cited section in the supplied document and read it

Correct. The document is in hand, so the citation can simply be checked — direct, conclusive, and quick.

• B Ask the same question in a new conversation and compare the two answers

Agreement between two runs is not evidence: both draw on the same tendencies and can be consistently wrong.

• C Ask the model how confident it is in the answer

A confidence statement is produced the same way the answer was, so it inherits the same uncertainty rather than measuring it.

• D Judge by the fluency and internal consistency of the answer

Fluency and internal consistency are properties of the writing and hold just as well for an incorrect answer.

The source is available, so verification is a matter of reading it — the cheapest and most conclusive check there is. Fluency is unrelated to accuracy. Two conversations can agree and both be wrong, since they share the same tendencies. A stated confidence level is generated text, not a measurement of correctness.

Which feedback is most likely to produce a useful revision?

• A "Try again, and this time produce something rather different from that one entirely please"

This produces a different attempt rather than a corrected one.

• B "Make it rather better than this, to a standard the reader will genuinely appreciate reading"

"Better" supplies no direction to move in.

• C "This isn't quite right, so please have another go at it and see whether you can do any better"

It reports dissatisfaction without saying what is wrong or where.

• D "The second section assumes the reader knows the process; explain it in one sentence first"

Correct. It locates the problem, names the cause and states the remedy.

Useful feedback names what is wrong, where, and what would fix it. The others describe dissatisfaction without locating it, so the revision is a guess. Specificity is what makes the next attempt targeted rather than random.

Which context content should be evicted first when a long-running agent approaches its limit?

• A The system prompt, which has already shaped everything that was done so far

The system prompt governs behavior throughout and is the worst thing to drop.

• B The current task description, which the agent has been working from

The current task is what the agent is working on.

• C The most recent exchange, since it is the least embedded in the work

Recent exchanges are the immediate working state.

• D Raw tool output whose conclusions have already been drawn and recorded

Correct. The value has already been extracted, so the raw form is redundant.

Raw output that has already been reduced to a conclusion is the cheapest thing to lose: the value has been extracted. The system prompt, the current task and recent exchanges are all load-bearing for what happens next.

A team's eval scores have been flat for months while user complaints rise. What is the most likely explanation?

• A Users are simply mistaken about what they are experiencing

Rising complaints are evidence, not error, and dismissing them keeps the eval misleading.

• B The scoring is broken and is not reflecting quality

Broken scoring is possible but would more likely produce erratic scores than stable ones.

• C The model has not changed, so nothing can have got worse

An unchanged model is consistent with changed traffic, which is the point.

• D The eval set no longer represents what users actually do

Correct. Sustained divergence means the set has drifted away from what users actually do.

An eval measures the cases it contains. Sustained divergence between a flat score and rising complaints means production traffic has moved away from the set, which happens as usage evolves. Refreshing it from recent traffic is the fix. Dismissing users ignores the strongest available signal.