CCAO-F practice questions, free.

Claude gives you a precise-sounding market statistic for a board deck. What do you do before using it?

• Ask the same question in a fresh conversation and keep the figure if both answers agree
• Ask Claude to restate the figure with a confidence rating and keep it if the rating is high
• Label the slide as an AI-generated estimate so the board knows to treat the figure with caution
• Ask for the source, then confirm the number against that original before it goes in the deck

You asked Claude to summarize a 30-page report. How do you check the summary is faithful?

• Ask Claude to grade its own summary for accuracy and revise anything it rates below confident
• Compare it with a summary from a different model and keep only the points the two share
• Read the report's executive summary and confirm the two versions cover the same themes
• Ask for the supporting passage behind each key claim and spot-check a few against the report

Which output is safe to send without a human review step?

• A termination letter for an employee, since the template language is standard
• A short reply telling a customer whether their refund is required under your policy
• A press statement about a service outage, if the facts in it came from the incident log
• A first draft of an internal meeting agenda that you will edit before circulating it

Where does Claude fit best in a weekly reporting process?

• Pulling the figures from source systems and deciding which ones are worth reporting this week
• Signing off on the final report, since it can check the numbers faster than a person can
• Writing and sending the report directly to stakeholders once the numbers have been approved
• Drafting the narrative from the approved numbers, with a person reviewing before it is sent

You connect Claude to your company's document store. What should you check first?

• Whether the connector supports write access so Claude can file its outputs back into the store
• Whether search results come back quickly enough for the team to use it in daily work
• Whether the document store vendor has published guidance on AI assistant integrations
• Which folders the connector can read and whether that matches the data's classification

A colleague wants to paste a customer's full medical file into a consumer chat account to summarize it. What is the right response?

• Paste it, get the summary, and delete the conversation from the account history afterwards
• Remove the customer's name and date of birth, then paste the rest into the consumer account
• Ask the customer for written consent by email first, then paste the file into the consumer account
• Check the data policy first and use an approved workspace with the right settings, if one exists

Claude drafts screening criteria for job applicants. What must happen before they are used?

• A review for bias and legality by the people who are accountable for the hiring decision
• A check that each criterion appears in the job posting so applicants know how they are assessed
• A comparison against the criteria a competitor lists publicly to make sure nothing is missing
• A trial run against last year's applicant pool to confirm the criteria produce a sensible shortlist

The prompt "write something about our product" produced a vague result. What improves it most?

• Pasting the full product spec sheet and asking for a write-up that covers everything in it
• Telling Claude the result was vague and asking it to try again with more concrete detail
• Adding the audience, purpose, format, length, and an example of the tone you want
• Asking Claude to write three versions so you can pick the one closest to what you had in mind

The first draft is close but not right. What is the efficient next step?

• Start a fresh conversation and rewrite the prompt with the lessons from the first attempt
• Edit the draft by hand, since explaining the changes takes longer than making them
• Ask for ten alternative drafts and combine the best paragraphs from each yourself
• Give specific feedback on what to change and iterate on the same draft

You need to rename a field across 200 files in a code repository. Which tool fits?

• A Project with the repository files attached so Claude can see all of them at once
• A chat conversation, pasting each file in turn and copying the edited version back
• A spreadsheet listing every file path so Claude can generate the edits row by row
• Claude Code, or a developer using it, working in the repository directly

For quick drafting of routine emails, what is the sensible model choice?

• A faster, cheaper model, saving the most capable one for deep analysis
• Rotate between models on each email so you can compare their tone over time
• The most capable model for every email, since consistency of quality matters most
• Whichever model was released most recently, since newer models replace older ones

Your team runs the same weekly task with the same standing instructions and reference files. What setup avoids re-explaining every time?

• A saved prompt template that each person pastes as the first message every week
• A Project with the standing instructions and reference files attached
• A single long-running conversation that the whole team continues week after week
• A shared document of past outputs that Claude is asked to imitate each week

Halfway through a long conversation Claude stops following an instruction you gave at the start. Most likely cause and fix?

• The instruction conflicted with a later request; shorten your messages from here on
• The instruction is buried in a long context; restate it, or move it into project instructions
• A model update changed its behavior mid-conversation; report the bug and switch to a different model
• The conversation hit a length limit; keep going and the instruction will apply again shortly

Claude declines a legitimate request about a sensitive topic. What is the productive move?

• Tell Claude that you accept full responsibility for how the answer will be used and retry
• Move the request to a different model that is known to refuse fewer sensitive topics
• Add context about who you are and the legitimate purpose, then rephrase the request
• Split the request into smaller pieces that each look harmless and combine the answers yourself

You ask for a summary of a document you attached, and the answer includes facts not in the document. What do you do?

• Ask Claude to use only the attached document and cite where each point comes from, then verify
• Re-upload the document in a new chat, since the extra facts suggest the upload was corrupted
• Remove the extra facts yourself and send the rest, since the document content is presumably fine
• Keep the extra facts if they are accurate, since they add useful context for the reader

Claude produced a legal-sounding clause for a contract. What is the right next step?

• Compare it against a similar clause in a signed contract and use it if the wording matches
• Ask Claude to confirm the clause is enforceable in your jurisdiction before adding it
• Have counsel review it, treating the draft as a starting point rather than as advice
• Add it to the contract with a note that it was AI-drafted so the other party can review it

A calculation in Claude's answer looks off. What do you do?

• Ask Claude to show its working step by step and accept the result if the steps look sound
• Ask Claude to double-check its own calculation and flag anything it is not sure about
• Ask the same question again in a new conversation and use whichever figure appears twice
• Re-run the arithmetic yourself or in a spreadsheet and correct the answer if needed

Which is the best signal that a summary is faithful to the source?

• It reuses the source's own terminology and section headings throughout
• Every key claim can be traced back to a specific passage in the source
• It was produced by the most capable model with the source attached to the chat
• It reads as balanced and does not add opinions that the source did not state

Claude answers a question about your company's Q3 revenue with a specific figure you never provided. What happened?

• It inferred the figure from earlier numbers in the conversation, so the math is checkable
• It pulled the figure from a connected finance system that an admin set up earlier
• It guessed; models fill gaps with plausible text unless grounded in provided data
• It retrieved the figure from a public filing, so it is probably close to correct

What is a practical way to check a translated customer email before sending?

• Ask Claude to rate the translation's accuracy on a scale of one to ten before you send it
• Run the email through a second translation tool and send it if the two outputs match closely enough
• Ask Claude to confirm the tone is polite and appropriate for a customer in that market
• Ask for a back-translation into your language and compare meaning, or have a native speaker skim it

Which output needs the most scrutiny?

• A list of medication interactions for a patient handout
• A draft tweet announcing a new feature to your followers
• A meeting agenda for the weekly product sync
• A brainstorm of twenty blog titles for the marketing team

Claude gives two different answers to the same factual question on two runs. What does that tell you?

• The first answer is more likely right, since the context was cleaner at the start
• Both answers are probably partly right, so combine them into a single response
• The second answer is more likely right, since the model refined its response
• The model is uncertain or the question is ambiguous; verify against a source

Your team wants a rule of thumb for when to trust Claude's output without checking. Best rule?

• Trust output from the most capable model; verify output from faster, cheaper models
• Trust drafts you will edit anyway; verify anything factual, numeric, legal, medical, or public-facing
• Trust answers under 100 words; verify anything longer, since longer answers have more room for errors
• Trust answers that cite a source; verify only the answers that come with no citation at all

Claude cites a study by name and year. What is the check?

• Ask Claude for the DOI and journal name, which it can only supply if the study is real
• Confirm the study exists and says what is claimed, using the journal or a database
• Ask Claude whether it is confident the citation is accurate and keep it if it says yes
• Check that the year is recent enough for the findings to still apply to your situation

A support team wants Claude to draft replies to tickets. Which design keeps quality high?

• Claude answers tickets it rates as simple automatically and routes the rest to agents, with no review of the automated ones
• Claude drafts from the ticket; agents send the draft unchanged and log any customer complaints for review each quarter
• Claude drafts from the ticket and knowledge base; an agent reviews and sends; wrong drafts feed back into instructions
• Claude drafts from the ticket; a manager samples a fixed 5% of sent replies each week and updates instructions from the sample

Which task is a poor fit for Claude in a finance workflow?

• Making the final approval decision on employee expense reports
• Drafting a memo that explains the monthly results to leadership
• Extracting figures from scanned invoices for a person to review
• Summarizing variance commentary from the business unit leads

What should you map before adding Claude to a process?

• The team's existing prompts and templates, so you can standardize them before rolling out anything new
• The steps, inputs, outputs, and where judgment happens, so you know which steps are drafting and which are decisions
• The list of available models and their costs, so you pick the cheapest one that works
• The budget and timeline, so you know how much automation you can afford to build

A marketing team wants weekly competitor summaries. Which setup?

• A fresh chat each week where the analyst pastes the competitor list and the latest news links
• An automated agent that gathers competitor news and posts the summary straight to the team channel each Monday
• A shared conversation that the team keeps open all quarter so Claude retains the full history
• A Project with standing instructions and the competitor list, run weekly with a person reviewing before circulation

Which is a sign that a task should stay manual for now?

• It involves judgment calls that the team currently makes based on experience
• Errors are costly, the inputs are inconsistent, and there is no quick way to check outputs
• It runs weekly and the inputs arrive in slightly different formats each time
• It takes under an hour and the team already has a template that works well

How do you measure whether Claude improved a workflow?

• Survey the team after a month on whether the work feels faster and less tedious
• Compare the tool's monthly cost against the hours the team estimates it saved
• Compare time, error rate, and rework before and after on the same kind of work
• Track how many prompts the team writes per week as a proxy for adoption

When connecting Claude to a shared drive, which question comes first?

• Which team members should get access to the connected assistant first
• Whether Claude can write files back to the drive or only read them
• How quickly it returns results when searching across a large folder tree
• Which folders it may read and whether the data classification allows it

A colleague builds a workflow where Claude's output goes straight into a customer-facing form. What is missing?

• A review or validation step before anything reaches the customer
• A more capable model so the output is accurate enough to send directly
• A log of every output so problems can be traced after customers report them
• A more detailed prompt so the output matches the form's fields exactly

Which content should not be pasted into a tool your organization has not approved for it?

• Product descriptions that are already on the public website
• Your own meeting notes from an internal planning session
• Customer personal data, health records, or confidential financials
• Press releases the company published in the last quarter

Why does responsible use require a named person accountable for AI-assisted decisions?

• Because it gives the organization a single person to hold responsible when the model makes a costly error
• Because the vendor's terms of service transfer liability for outputs to the customer
• Because the model cannot be accountable; a person must be able to explain and stand behind the outcome
• Because regulators require a named AI officer to be listed for every automated decision

Claude drafts performance review language for an employee. Which risk is highest?

• Formatting that does not match the HR system's review template and has to be redone by hand
• Language so generic that it gives the employee nothing concrete to act on in the coming year
• Bias in the language or reasoning affecting a person's career, so it needs careful human review
• Length that runs well past what managers are expected to write for an annual review

What does an acceptable use policy typically prohibit?

• Using the tool for harmful, deceptive, or otherwise prohibited purposes
• Asking questions about topics that are unrelated to your job role
• Sharing outputs with anyone outside the team that produced them
• Using the tool for any task that involves customer names or contact details

A vendor claims its AI tool 'never makes mistakes'. Your response?

• Ask the vendor for the accuracy benchmarks behind the claim and accept it if they look strong
• Include the claim in the contract so the vendor bears the cost of any mistakes
• Treat the claim as false and design review steps into the workflow anyway
• Run a two-week pilot and drop the review steps once the pilot shows no errors

What should you do if Claude produces content that seems to reproduce a copyrighted text at length?

• Shorten the passage so that it falls under what fair use typically allows
• Use it, since content produced by an AI tool is not subject to copyright
• Cite Claude as the author so the original rights holder is not misrepresented
• Do not use it verbatim; rewrite or source it properly, and flag the case

Which practice supports transparency when AI helped produce a document?

• Keeping AI-assisted drafts out of the document history so reviewers see only the final
• Disclosing AI assistance where your organization's policy or the audience expects it
• Publishing under a team name rather than an individual so no one is misattributed
• Adding a footnote naming the model version used on every document you produce

Which prompt is most likely to produce a usable customer apology email?

• 'Draft a customer apology email. Use our standard tone. Include a credit. Do not make it too long.'
• 'Write an apology to a customer for a late order. Keep it professional and make sure they feel heard.'
• 'Write a 120-word apology to a customer whose order arrived a week late, warm but not groveling, offering a 15% credit; here is our tone guide.'
• 'Write a sincere apology for a shipping delay from a customer service manager, with a goodwill gesture.'

You want three distinct options rather than three variations of one idea. What do you say?

• 'Give me three options and make sure they are genuinely different from each other, not variations'
• 'Give me three options that differ in approach, for example one conservative, one bold, one unconventional'
• 'Give me three options. Be as creative as possible and avoid repeating yourself across them.'
• 'Give me three options, then rank them by how creative each one is so I can see the range'

Claude's draft uses jargon your audience will not understand. Best fix?

• Tell it the audience and ask for plain language with an example of the level you want
• Replace the jargon yourself before sending, since you know the audience best
• Ask it to add a glossary at the end so readers can look up the terms they do not know
• Ask for a shorter draft, since cutting length usually forces out the technical terms

What is the value of giving Claude the raw material (notes, data, previous versions) with your request?

• Grounded requests produce specific, accurate drafts instead of generic ones
• It lets the model learn your company's style for future conversations
• It shortens the conversation, since Claude can skip the clarifying questions
• It lets Claude verify its own claims against your documents before answering

When a task is large, what approach works better?

• Start with the hardest section so the remaining ones can follow its pattern
• Break it into steps: outline first, then sections, reviewing between steps
• Ask for the full deliverable twice and merge the stronger parts of each version
• Write one detailed prompt that covers every section so the output is consistent

Claude keeps adding a closing paragraph you do not want. What is the efficient instruction?

• 'Rewrite this without the fluff at the end'
• 'Keep it brief and skip anything that is not essential'
• 'End after the final bullet; do not add a closing paragraph'
• 'Match the length of the previous version exactly'

For a long, careful analysis of a 60-page report, which is the better choice?

• The most capable model, accepting slower responses for the depth of analysis
• Whichever model the team used last time, so results are comparable across reports
• The fastest model, since it can be run several times and the answers compared
• A smaller model with the report split into chunks so each fits in a short prompt

Which feature lets a whole team share standing instructions and files?

• A shared Project with the instructions and files attached
• A shared browser bookmark to a chat with the instructions pinned at the top
• A saved conversation that each team member opens and continues
• A team email thread that holds the latest version of the instructions

A colleague asks whether to use the consumer chat app or an organization workspace for client work. Deciding factor?

• Which one has access to the newest model, since client deliverables should use the best output
• Cost per seat, since the consumer app is cheaper and client budgets are tight
• Data handling and admin controls: the organization workspace governs what happens to client data
• Response speed and rate limits, since client work is time-sensitive and volume can be high

Which task fits Claude Code rather than the chat app?

• Drafting a newsletter that goes out to the engineering team each month
• Making the same change across dozens of files in a code repository
• Writing a job post for a senior developer role from the hiring manager's notes
• Summarizing a recorded architecture review call for the people who missed it

When would a smaller, faster model be the right pick?

• For high-volume, simple tasks like tagging or short drafts where speed and cost matter more than depth
• When the conversation is going to run long, since smaller models hold context more reliably over time
• For legal or medical analysis, where a concise answer is safer than a long one
• When the team is new to AI tools, since simpler models are easier to prompt well

What belongs in a Project's instructions?

• Every document the team has produced on the topic, so Claude has the fullest possible picture
• A log of past conversations that went well, so Claude can reproduce the pattern
• Standing context: who the audience is, tone, formats, and rules that apply to every conversation
• The specific request for this week, updated each time the task changes

Your Project's knowledge files are out of date and answers reflect old policy. Fix?

• Add a line to the instructions telling Claude to prefer the most recent file when they conflict
• Replace the outdated files and tell the team that the source of truth has changed
• Add the new policy document alongside the old ones so Claude can see both versions
• Start a new Project from scratch and move the team over once it is set up

What is a good way to keep a team's prompts consistent?

• A weekly review where the team compares the prompts each person wrote that week
• A rule that everyone uses the same model so the outputs come out in a similar style
• Shared templates or skills with the agreed structure, stored where the team can find them
• One designated person who writes every prompt and sends it to the rest of the team

Which is a reason to split work into several Projects?

• The knowledge files are large and splitting them spreads the storage across Projects
• Projects perform better when each one holds fewer than ten conversations
• Each team member should own a Project so their conversations stay private
• Different audiences, tones, or confidentiality levels that should not mix

How much should a knowledge file contain?

• A single summary page, since longer files make answers slower and less focused
• What conversations in that Project regularly need, kept current
• Only finalized documents; drafts and working notes should stay out of the Project
• Everything the team has written on the topic, so nothing relevant is left out

What should you record when a prompt template works well?

• The name of the person who wrote it, so colleagues know who to ask about it
• The number of times it has been used, so the team can see which templates matter
• The template, the inputs it expects, and an example output, so others can reuse it
• The model version and the date, so the team knows when it may need to be re-tested

Claude's answer ignores an attached document. First thing to check?

• That the document uploaded correctly and that your prompt refers to it explicitly
• Whether the conversation is too long and the document has dropped out of context
• Whether the model you selected supports file attachments of that size and type
• Whether the document contains text or only scanned images, which need conversion first

A conversation has drifted and answers are worse. What helps?

• Ask Claude to summarize the conversation so far and continue from that summary in the same chat
• Start a new conversation with a concise summary of the task and decisions so far
• Switch to a more capable model mid-conversation so it can recover the thread
• Shorten your messages so the model has less new text to process each turn

Claude says it cannot access a link you pasted. Why?

• The link is broken; check it in your browser and resend it once it loads
• The link is too long for the model to parse; shorten it with a URL shortener and try again
• It may not be able to browse, or the page may be restricted; paste the content instead
• The page needs a login; sign in yourself first and paste the link again, since the session carries over

Output is in the wrong format despite instructions. Fastest fix?

• Switch to a more capable model, since format compliance improves with model size
• Show a short example of the exact format you want
• Ask Claude to explain why it chose that format before trying again
• Repeat the same instruction more firmly and put it at the very end of the prompt