Claude Certified Associate - Foundations (CCAO-F) practice questions

A customer service team lead at a local council drafts replies to residents' questions about bin collections, parking permits and council tax. For three weeks she has pasted each new query into one long conversation, and recent drafts have started quoting a parking rule she corrected in the first week. The council's current policy documents and reply style apply to each query. What should she do?

• AKeep the long conversation going and restate the corrected parking rule above each new query she pastes in.
• BSet up a Project holding the policy documents and reply-style instructions, then start a fresh chat per query.check_circle Correct
• CAsk Claude in the current conversation to list the rules it is applying, then check that list for mistakes.
• DTurn on Memory so the corrected parking rule carries over, then carry on using the same long conversation.

Why A is wrong: Restating the correction is tempting because it fixes the symptom she noticed. It leaves her in a conversation long enough to blur earlier detail, so other corrections and the reply style can drift in the same way.

Why B is correct: This is recurring work with stable context. A Project gives each new chat the same instructions and the current policy documents from the start, so no single conversation grows long enough to lose earlier detail.

Why C is wrong: Reviewing Claude's own list feels like a verification step, but it asks the same degraded conversation to report on itself. It does nothing to stop the next drafts drifting and adds a manual check to each query.

Why D is wrong: Memory can carry some context between chats, which makes this sound like a fix. It is not where reference policy belongs, and keeping one very long conversation is the cause of the problem rather than a cure for it.

An HR coordinator at a logistics company has 300 applications for warehouse supervisor roles and a shortlist due in three days. The company's recruitment policy states that every shortlisting decision must be made by a named recruiter. The hiring manager suggests asking Claude to rank the applicants and reject the bottom half automatically. What should the coordinator do?

• AUse Claude to summarise each application against the published criteria, and have the recruiter make every shortlisting decisioncheck_circle Correct
• BLet Claude reject the bottom half, because the published criteria are objective and it applies them more consistently
• CAsk Claude to rank the applicants and write a reason for each rejection, then send the rejections as drafted
• DDrop Claude from the process entirely, since any use of AI in recruitment breaches the company's recruitment policy

Why A is correct: This uses Claude for what it suits, condensing 300 applications against the criteria so the recruiter can work faster, while the shortlisting decision about each person stays with the named recruiter as the policy requires.

Why B is wrong: Consistency sounds like fairness, and the deadline makes automation tempting. But a rejection is a decision about a person, the policy requires a named recruiter to make it, and Claude can misread or unevenly weigh applications in ways nobody would catch if no human looks.

Why C is wrong: Written reasons look like accountability, so this can feel more responsible than a bare ranking. Claude is still making the decision, the reasons are generated after the fact rather than checked, and no recruiter has decided anything as the policy requires.

Why D is wrong: Caution about AI in hiring is sensible, so stepping back can feel safe. But the policy governs who makes the decision, not whether a recruiter may use help to read applications, so abandoning Claude over-corrects and puts the deadline at risk for no gain.

An HR adviser at a logistics company asks Claude to 'Summarise this parental leave policy' and attaches the policy. The summary is accurate but full of legal phrasing and long sentences. It will be posted in the staff room for warehouse employees, many of whom read English as a second language. What should the adviser change in the prompt?

• AAsk for a more detailed summary so that no part of the policy is left out
• BSwitch to the most capable model so the summary is written more clearly
• CState who will read it and ask for plain English with short sentencescheck_circle Correct
• DRun the same prompt again and choose whichever version reads best

Why A is wrong: Completeness feels like the safe choice for a policy document. But the problem is readability for this audience, and adding more detail would make the summary longer and harder for these readers, not easier.

Why B is wrong: A more capable model sounds like a fix for any quality problem. But no model can guess an audience the prompt leaves out, so this costs more without addressing the missing information.

Why C is correct: The prompt never said who the readers were, so Claude matched the register of the source policy. Naming the audience and the reading level gives Claude the information it needs to pitch the language correctly.

Why D is wrong: Regenerating can produce a slightly different draft, which makes it tempting. With the same prompt Claude still has no reason to change its register, so the adviser is relying on luck instead of a clear instruction.