Fit vs Appropriateness
Two different questions on different evidence. Fit asks whether Claude would do the work well: the right kind of task, the right volume, output someone can check. Appropriateness asks whether the task should be handed over at all, and depends on stakes, authority and accountability.
Exam context: The wrong answer that appears often bases its reasoning on quality: Claude would do the task quickly and well, so it must be fine. A task can be a perfect practical fit and still be work that only a qualified human should do.
See also: 6.1 Identifying Appropriate and Inappropriate Use Cases
The Four Inappropriate Categories
Work requiring professional authority; consequential decisions about identifiable people; fabrication presented as real; and anything the Usage Policy or the organisation's own policy prohibits. Usage Policy prohibitions include tracking or identifying people without their consent (facial recognition, for example), infringing someone else's intellectual property, and fake reviews. The categories are deliberately narrow and specific.
Exam context: Questions reward naming which category makes a use inappropriate, not just that it feels risky. Everything outside the four categories is appropriate, and treating adjacent work as forbidden is the mistake of being too cautious.
See also: 6.1 Identifying Appropriate and Inappropriate Use Cases
Substitution Test
The one-question appropriateness check: would you accept this work from a capable, well-meaning colleague with no professional qualification in the area, no access to the history of the people involved, and no accountability for the outcome? The AI Fluency framework calls the underlying skill Delegation.
Exam context: The three grounds are qualification, history and accountability. Speed, cost and output quality are not grounds; they are fit considerations that wrong options present as if they were grounds of the test.
See also: 6.1 Identifying Appropriate and Inappropriate Use Cases
Usage Policy
Anthropic's official rules for what Claude may be used for, formerly called the Acceptable Use Policy. Its Universal Usage Standards are prohibitions that apply to every user. It requires consumer-facing chatbots to disclose that users are interacting with AI. It also covers High-risk AI Recommendations, such as legal or medical advice to an individual or a hiring decision. For these, it requires meaningful review by a qualified person, who stays responsible for what is delivered or decided. The person who gets the advice or the decision must be told that AI was used.
Exam context: The policy is the basis of two objectives: its prohibitions are the source of the fourth inappropriate category, and its accountability line is where the ownership anchor comes from. Expect older material to use the old name.
See also: 6.1 Identifying Appropriate and Inappropriate Use Cases
The Internal-Use Excuse
The argument that a use is acceptable because the results never leave the organisation. It appears across the domain, attached to inappropriate delegations and unprepared uploads alike, and it is never the right answer.
Exam context: Keeping an output in-house restores neither accountability for the decision it contains nor the affected person's ability to challenge it, and where results go does not change what was disclosed at upload.
See also: 6.1 Identifying Appropriate and Inappropriate Use Cases
Sensitive Material
Anything that should not leave the organisation before it is prepared. Two halves: the obvious inventory (names, addresses, dates of birth, account numbers, health information, anything about children) and material sensitive because of who owns it or what was promised about it.
Exam context: The under-recognised half is where working professionals fail: a document with no names in it can still be the most restricted document in the office.
See also: 6.2 Applying Data Sensitivity, Regulatory and Privacy Considerations
Commercial Confidence
Sensitivity that comes from ownership and promises rather than personal identifiers: information held under a duty of confidence, commercially confidential figures, and third-party material governed by a contract that says how it may be handled.
Exam context: The classic wrong option approves a document for upload because it contains no personal data. Confidentiality obligations bind regardless of whether a single person is named.
See also: 6.2 Applying Data Sensitivity, Regulatory and Privacy Considerations
The Four Safeguards
Four steps, tried in a fixed order, that let sensitive work go ahead: remove what the question does not need, anonymise or aggregate what it needs only in outline, use an authorised route, and ask whoever owns the data or the policy. Abandonment is not one of the four safeguards.
Exam context: Use the first safeguard in the order that works. You can do steps 1 and 2 yourself, today. Steps 3 and 4 wait for a different workspace or for another person. So when removal is enough, steps 3 and 4 cost time and add no protection.
See also: 6.2 Applying Data Sensitivity, Regulatory and Privacy Considerations
The Placebo Family
Safeguards that operate after the disclosure and therefore control nothing: instructing Claude not to retain the material, declaring the upload confidential, adding a confidentiality line to the prompt, and deleting the conversation afterwards.
Exam context: The guide's own sample rationale states the principle: instructing the model not to retain data does not satisfy the policy control. No placebo is ever the right answer. The right answer changes what is uploaded.
See also: 6.2 Applying Data Sensitivity, Regulatory and Privacy Considerations
Anonymise and Aggregate
The second of the four safeguards: replacing individuals with cohorts and exact figures with ranges, so an analysis that needs the patterns in the data can run without the identities of the people. The guide's sample question depends on exactly this step.
Exam context: The enabling safeguard between two wrong extremes: uploading as-is and abandoning the analysis. Check the prepared version by asking whether anyone could be identified by combining what remains.
See also: 6.2 Applying Data Sensitivity, Regulatory and Privacy Considerations
Sanctioned Workspace
The Claude workspace an organisation provides and governs. Three things apply to it and to no personal account: the organisation's agreement over how its data is handled, administrator-held settings, and organisation-level control over how long its data is retained. When the organisation's own policy is stricter than the workspace agreement, staff follow the policy.
Exam context: The rule has real reasons behind it. It is not etiquette. Wrong options present the rule as cost allocation, manager oversight or courtesy. The right reasoning names the agreement and controls that do not apply outside the workspace.
See also: 6.3 Following Organisational AI Policies and Governance Standards
Personal Account
An account governed by the individual's own consumer relationship with Anthropic: their settings decide whether Anthropic uses the chats for model improvement, and consumer retention mechanics apply. None of it is administered, seen or governed by the employer.
Exam context: The tempting variant is the paid personal plan with carefully chosen settings. Still a personal account: what matters is whose agreement governs the data, not how conscientiously the individual configured their own.
See also: 6.3 Following Organisational AI Policies and Governance Standards
Shadow AI
Work material put into accounts the organisation does not govern, typically a colleague's personal Claude account used because it is quicker or because sanctioned access has not arrived yet.
Exam context: The professional response has two halves: do not copy the workaround, and raise it with whoever owns the policy or the data. Wrong options offer the first half alone, or deletion presented as a remedy.
See also: 6.3 Following Organisational AI Policies and Governance Standards
Policy Silence
The gap where an AI policy simply does not mention the material or the use in question. Silence means the question has not been answered, not that the answer is yes. Having no policy at all is a different case. Write a short rule: what may go in, what may not, and whom to ask. Take it to the people who run the organisation, and start when they agree it.
Exam context: Treating silence as permission converts a gap in a document into an unauthorised disclosure. The right action is asking the policy owner, which is an action, not a refusal, and it improves the policy for everyone.
See also: 6.3 Following Organisational AI Policies and Governance Standards
Controller and Processor
The regulatory vocabulary behind the sanctioned workspace: for commercial products, the customer organisation is Controller of the data its users submit, deciding what happens to it, while Anthropic acts as Processor, handling it only on the organisation's instructions.
Exam context: Remember the plain-English meaning first: the organisation decides and Anthropic follows its instructions. The formal labels are background. This split is what a personal account lacks.
See also: 6.3 Following Organisational AI Policies and Governance Standards
Accountability Anchor
The principle that the person whose name is on the work owns it completely: the sender of the email, the publisher of the report, the signer of the letter. Saying Claude drafted it is a statement about production method and transfers nothing.
Exam context: The wrong option spreads responsibility onto the tool or whoever configured the shared Project. Options that blame the tool are never the right answer. The right answer accepts responsibility for the error and corrects it for those affected.
See also: 6.4 Understanding the Ethical Implications of AI Usage
AI Disclosure
Telling an audience that AI was involved in producing work. The Usage Policy has two fixed rules. Consumer-facing chatbots and external-facing AI agents must disclose that users are interacting with AI. A person who gets advice or a decision based on a High-risk AI Recommendation must be told that AI was used. For everything else, disclosure depends on proportion and on what the reader relies on. A useful disclosure says what AI was used for, what the person reviewed, and who is accountable for the result.
Exam context: Both extremes are wrong options: declaring AI assistance on every routine output, and never declaring it at all. Disclosure is owed when the reader is relying on who did the thinking or the judging, and it is not a way to avoid blame for errors.
See also: 6.4 Understanding the Ethical Implications of AI Usage
Fairness Review
A separate read of any work that describes people, done by someone who knows them, looking only at how they are characterised. It sits apart from fact-checking because an output can pass every accuracy check and still describe a group unfairly.
Exam context: Options that stop at verified figures have answered the accuracy question while the ethical one is still open. Asking Claude to review its own draft for bias is a placebo that looks like verification, not the review.
See also: 6.4 Understanding the Ethical Implications of AI Usage
Colleague Stakeholders
The people whose work changes when Claude takes over part of a process. They deserve to know what changed and why; quietly automating a shared task, or generating a colleague's contribution without telling them, is an honesty question, not a quality one.
Exam context: The stakeholder candidates most often miss. Stakeholder lists on the exam include colleagues, readers and the people described; they never include Anthropic, the model, or competitors.
See also: 6.4 Understanding the Ethical Implications of AI Usage