CCAO-F Practice Test 9 — 10 Questions

Question 1

A product manager is preparing to launch a Claude-powered assistant that will summarize user-uploaded medical lab reports. Before release, legal asks the team to confirm that the assistant will not provide direct diagnoses or treatment recommendations. The team decides to add a system prompt that instructs Claude to avoid giving medical advice and to recommend consulting a licensed clinician. Which action best aligns with Anthropic's safety guidance for this deployment?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Implement the system prompt, then run adversarial and edge-case evaluations to verify refusal behavior and monitor outputs after launch.. The correct approach layers a clear system prompt with empirical validation and post-deployment monitoring. Instructions alone cannot guarantee safe behavior in a high-stakes domain, so teams must test for refusal consistency and watch for regressions. This combination of preventive prompting and detective controls reflects responsible deployment practices for medical-adjacent use cases.

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Rely solely on the system prompt and ship without any further testing, because Claude's Constitutional AI training already prevents medical advice.

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Disable Claude's ability to discuss health topics entirely by blocking any prompt containing medical terminology before it reaches the model.

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Ask Claude to role-play as a licensed physician so that its medical summaries are more authoritative and useful to end users.

Question 2

A developer is using Claude to generate synthetic data for training a fraud detection model. They want to ensure the synthetic data does not contain real personally identifiable information (PII) from the original dataset. What is the best approach?

Accepted Answer

Prompt Claude to generate data that is statistically similar but does not include any real PII, and then run a PII detection tool on the output before use.. The best approach is to instruct Claude to generate data without real PII and then validate the output with a PII detection tool. This dual strategy minimizes the chance of privacy leaks. Assuming safety, adjusting temperature, or fine-tuning on the original data all fail to provide reliable protection against PII reproduction.

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Use a higher temperature setting to make the output more random and less likely to contain real PII.

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Fine-tune Claude on the original dataset so it learns the patterns without memorizing PII.

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Assume Claude will never reproduce PII because it is trained to avoid such outputs.

Question 3

A product team is evaluating Claude for a customer-facing assistant that must refuse to give medical diagnoses. Which TWO techniques should they use to make the refusal behavior consistent across many different user phrasings? (Choose two.)

Accepted Answer

Include a few-shot example showing the assistant declining a diagnosis request.. Consistent refusal behavior comes from persistent instructions plus concrete demonstrations. A system-prompt policy establishes the rule for every turn, while a few-shot example shows exactly how a refusal should look, helping the model generalize across varied user phrasings. Temperature, max_tokens, and removing the system prompt do not reinforce policy adherence and can weaken it.

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Set temperature to 1.0 to encourage diverse safe responses.

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Increase max_tokens so the model has room to explain refusals.

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Disable the system prompt and rely only on user instructions.

Question 4

A logistics company uses Claude to extract shipment details from scanned customs forms. The forms are supplied as raw OCR text that contains occasional garbled characters and spurious line breaks. The developer wants to reduce the number of fields Claude invents when a value is missing on the form. Which prompt structure change is most likely to achieve this?

Accepted Answer

Wrap the OCR text in <document> tags and add the instruction: 'If a field cannot be found in the document, return null for that field.'. The reliable fix pairs two techniques: structural delimitation of the source text so Claude can tell document content from instructions, and an explicit rule stating what to do when a field is absent. Together they remove the ambiguity that pushes the model toward inventing values, and the null convention makes the result easy to validate programmatically.

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Shorten the OCR text by removing all line breaks before inserting it into the prompt.

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Set temperature to 0 and request the output as a JSON object.

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Add the instruction: 'Be as accurate as possible and avoid mistakes.'

Question 5

An engineer is designing a multi-tenant SaaS feature that lets each customer supply their own Anthropic API key, which the backend stores encrypted and uses when calling the Messages API on that tenant's behalf. A security review asks how requests should be attributed so usage can be billed back and abuse isolated per tenant. Which practice best meets this requirement?

Accepted Answer

Have the backend call the Messages API with each tenant's own stored key so rate limits, usage, and audit trails are scoped to that tenant.. Credential boundaries are the cleanest way to achieve per-tenant attribution and isolation. When each tenant's traffic flows through that tenant's own API key, Anthropic's rate limiting, usage records, and audit logs partition automatically, and a compromised or abusive tenant cannot exhaust capacity belonging to others. The backend retains control over request construction and can layer additional quotas.

Answer

Proxy all tenant traffic through a single key and reconcile billing by counting tokens in the responses your backend receives.

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Issue each tenant the same platform key but append a unique tenant ID to the user field of every message.

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Send every tenant's request using a single shared platform API key and rely on the prompt contents to identify the tenant.

Question 6

A team is deploying Claude to summarize internal incident reports that may contain sensitive employee information and security vulnerabilities. Which TWO practices best align with responsible use of Claude in this context? (Choose two.)

Accepted Answer

Redact or anonymize sensitive employee and security details before sending content to the Claude API.. Responsible use when handling sensitive incident reports involves minimizing data exposure and adding safeguards. Redacting sensitive details before sending to the API reduces risk, and output filtering catches any sensitive information that may still appear in summaries. Instructing the model to ignore data, storing logs insecurely, or sharing raw data externally do not adequately protect sensitive information and may introduce compliance issues.

Answer

Enable logging of all prompts and responses for auditing, and store them indefinitely in an unencrypted database.

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Share the raw incident reports with a third-party analytics service to cross-check Claude's summaries for accuracy.

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Use a system prompt to instruct Claude to ignore any sensitive information it encounters and not include it in summaries.

Question 7

An analyst is using Claude to answer questions over a 50,000-token legal contract. They notice that answers about clauses near the middle of the document are less accurate than those about the beginning or end. Which strategy best improves accuracy across the entire document?

Accepted Answer

Break the contract into labeled sections and retrieve only the relevant sections for each question.. Long documents can suffer from uneven attention, with mid-document content sometimes under-weighted. Breaking the contract into labeled sections and retrieving only relevant passages for each question ensures the needed clause is prominent in the prompt. This retrieval-based structuring improves accuracy regardless of where the clause originally appeared.

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Increase the model's temperature slightly to broaden its attention.

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Repeat the question at the beginning and end of the prompt around the contract.

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Summarize the contract first and then ask questions only against the summary.

Question 8

A team is designing a Claude prompt that must return a fixed JSON schema with fields "vendor", "amount", and "currency". They want the output to be reliably parseable by downstream code. Which TWO techniques best improve reliability of the structured output? (Choose two.)

Accepted Answer

Use a prefilled assistant turn that begins the JSON, so Claude continues from a known starting point.. Reliable structured output comes from constraining the response shape on two fronts: telling Claude exactly what schema to produce, and preventing it from starting with prose. An explicit schema or example JSON defines the contract, while prefilling the assistant turn with the opening of the JSON forces the model to continue in that format. Together they minimize drift and make downstream parsing dependable, unlike loosening sampling or inviting commentary.

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Set temperature to its maximum so Claude considers many possible JSON layouts.

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Precede the JSON with a short natural-language explanation so a human can verify the result.

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Ask Claude to decide at runtime which fields are most relevant and include only those.

Question 9

A support team wants Claude to answer customer questions using only the company's internal help articles. They plan to paste the relevant article text into the prompt before the user's question. Which prompting technique does this describe?

Accepted Answer

Retrieval-augmented generation (RAG). Inserting relevant source documents into the prompt so the model answers from them is retrieval-augmented generation. The retrieval step supplies factual context, and the generation step produces an answer grounded in that context. This differs from zero-shot, few-shot, and chain-of-thought, which address examples, demonstrations, and reasoning traces respectively.

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Zero-shot prompting

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Chain-of-thought prompting

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Few-shot prompting

Question 10

A developer is designing a tool use workflow with the Claude Messages API. After the model returns a tool_use content block, the application executes the tool and must send the result back. Which TWO of the following are required for the follow-up request to be processed correctly? (Choose two.)

Accepted Answer

Preserve the prior assistant turn, including the tool_use block, in the messages array of the follow-up request.. Completing a tool use loop requires sending the tool's output back as a tool_result content block inside a user message, with a tool_use_id that matches the model's tool_use block id, while keeping the original assistant turn in the messages array. This pairing lets the model associate the result with its earlier call and continue reasoning.

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Re-declare the full tools array in the follow-up request even though tools were already provided in the first request.

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Set the stop_sequences parameter to include the tool name so the model knows when to stop calling tools.

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Convert the tool_result content into plain assistant text so the model can read it as part of its own previous reply.