CCAO-F Practice Test 10 — 10 Questions

Question 1

A developer is building a customer support assistant using Claude. The assistant must always respond in a friendly tone, never discuss competitors, and always ask for an order number when the user reports a shipping issue. The developer wants these rules to apply across all conversations with minimal per-request token cost. What is the most appropriate mechanism?

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

Accepted Answer

Place the rules in the system prompt so they are applied consistently to every request.. The system prompt is the correct place for persistent behavioral instructions that should apply to every request. It is processed by the model as a high-level directive and is not repeated in each user message, making it token-efficient. This ensures consistent tone, competitor restrictions, and required questions across all conversations without per-request repetition.

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Add the rules to the end of each user message as a reminder.

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Include the rules as a few-shot example in every user message.

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Fine-tune a custom model with the rules embedded in training data.

Question 2

A developer is integrating Claude into a legal document review system. The system must process lengthy contracts and answer questions about specific clauses. Which TWO of the following techniques help mitigate the risk of Claude hallucinating details not present in the document? (Choose two.)

Accepted Answer

Instruct Claude to answer only based on the provided document and to say 'I don't know' if the information is not present.. To reduce hallucinations when reviewing legal documents, the most effective methods are to instruct Claude to rely solely on the provided text and to require citations for its answers. These techniques ground the model's responses in the source material, making it less likely to invent details and easier to verify accuracy.

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Use a higher temperature setting to encourage more creative responses.

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Set max_tokens to a very low value to limit the response length.

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Fine-tune the model on a dataset of legal contracts.

Question 3

A developer is integrating the Claude API into a production pipeline and needs to handle a response where the model stopped because it hit the output token ceiling before finishing its answer. Which TWO actions are appropriate? (Choose two.)

Accepted Answer

Inspect the 'stop_reason' field and treat a value indicating the token limit as a truncation signal.. When generation stops because it reached the output token ceiling, the response still returns successfully but the content is incomplete. The application should detect this via stop_reason and then either allow more output tokens or continue from the partial text. Retrying unchanged, adjusting temperature, or trusting the HTTP status all fail to detect or resolve the truncation.

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Assume the response is complete and parse it as-is, since a 200 status was returned.

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Retry the identical request unchanged, expecting the model to finish within the same limit.

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Lower 'temperature' to 0 so the model produces a shorter answer next time.

Question 4

A developer is building a Claude-powered assistant for a legal firm. The assistant is asked to draft a clause citing a specific statute. Claude generates a citation that appears authoritative but does not actually exist. The developer wants to reduce the risk of this happening in production. Which approach is most aligned with responsible use?

Accepted Answer

Use retrieval-augmented generation (RAG) to ground Claude's responses in a verified legal database, and add automated checks that validate citations against that database.. Hallucinated citations in legal drafting pose serious risks. Retrieval-augmented generation grounds Claude's responses in a verified database, and automated checks validate that cited statutes exist. This combination addresses the root cause by ensuring outputs are based on real sources and are programmatically verified. Disclaimers, higher temperature, or limited fine-tuning do not reliably prevent fabricated references in production.

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Instruct Claude to always add a disclaimer that its output may contain errors, and rely on the attorney to verify every citation manually.

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Increase the model's temperature setting so that Claude produces more creative and varied citations, reducing the chance of repeating a fake one.

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Fine-tune Claude on a small set of real legal documents and assume it will generalize to all statutes and jurisdictions without further validation.

Question 5

A developer is implementing retry logic for calls to the Anthropic Messages API in a production service. The service must handle transient failures gracefully without overwhelming the API. Which TWO practices should the developer implement? (Choose two.)

Accepted Answer

Retry requests that fail with HTTP 429 and HTTP 500 status codes, using exponential backoff with jitter between attempts.. Resilient retry logic targets transient failures such as 429 and 500 responses, spacing attempts with exponential backoff plus jitter to avoid synchronized retry storms. A retry cap, whether by count or total time budget, prevents runaway loops and bounds latency. Client errors like 400 and 401 are not transient and should not be retried, and the API does not redeliver failed requests, so some client-side retry strategy is necessary.

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Disable all retries and rely on the API's internal queueing to eventually deliver failed requests.

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Retry all failed requests immediately in a tight loop until they succeed, to minimize latency for the end user.

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Retry requests that fail with HTTP 400 and HTTP 401 status codes, since these often resolve on a second attempt.

Question 6

A developer is writing the first integration test against the Claude Messages API using the official Anthropic SDK. The test must send a single user turn and read the model's text reply. Which request structure correctly represents the required Messages API input?

Accepted Answer

A messages array containing one object with role "user" and content set to the prompt string, plus the model and max_tokens parameters.. The Messages API expects a required model, a required max_tokens, and a messages array whose entries use role "user" or "assistant" with string or block content. A single user message with a string prompt is the smallest valid request. System instructions are passed through a separate top-level system field, and the model must always be named explicitly, so the other structures would be rejected before generation.

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A messages array with alternating user and assistant turns, and the model name omitted because it is inferred from the endpoint.

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A messages array with a single object whose role is "system" and content set to the prompt, plus max_tokens.

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A prompt parameter containing the text, plus the model name, with no messages array.

Question 7

A marketing agency uses Claude to generate blog drafts for clients. A junior writer pastes a competitor's copyrighted article into the prompt and asks Claude to 'rewrite it closely enough that it reads differently but keeps all the same arguments and examples.' What is the most appropriate responsible-use action for the agency?

Accepted Answer

Refuse the near-verbatim rewrite request and instead use Claude to produce an original draft informed by the writer's own research and analysis.. Responsible use means not directing Claude to launder another creator's copyrighted work into a superficially different article. The defensible path is to decline the near-verbatim rewrite and instead have the writer conduct independent research, then use Claude to draft original content grounded in that research. This preserves the agency's legal position and respects the competitor's rights.

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Ask Claude to paraphrase sentence by sentence and then publish the result without attribution to the competitor.

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Run the rewrite through a second AI tool to make the text harder to trace back to the original article.

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Proceed, because the output is generated text and therefore cannot infringe the competitor's copyright.

Question 8

A developer is adding a retrieval step so Claude can answer questions over a 200-page internal policy manual. The full manual exceeds the context window, so the application must select relevant sections to include in each Messages API request. Which strategy best keeps answers accurate while staying within the context limit?

Accepted Answer

Embed the manual into chunks, retrieve the top-matching chunks for each question, and include only those excerpts in the request.. When source material exceeds the context window, retrieval-augmented generation is the appropriate pattern: split the document into retrievable chunks, select the passages most similar to the incoming question, and place only those excerpts in the request. This bounds token usage while keeping the answer grounded in the passages most likely to contain the relevant policy.

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Truncate the manual to the first N tokens that fit and always send that same prefix with every question.

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Increase the max_tokens parameter on each request so the model can internally hold more of the manual.

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Summarize the entire manual once with Claude, cache the summary, and send the summary with every question instead of the source text.

Question 9

A developer is building a customer support assistant using Claude. The assistant must always respond in a friendly tone, never discuss competitors, and always ask for an order number when the user reports a shipping issue. The developer wants these rules to apply across all conversations with minimal per-request token cost. What is the most appropriate mechanism?

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

Accepted Answer

Summarize the conversation periodically and include the summary plus recent messages in subsequent requests.. Periodically summarizing the conversation and including the summary with recent messages keeps essential context within token limits. This method preserves coherence over many turns without unbounded growth. It is a standard pattern for long-running conversations in the Anthropic Messages API, ensuring Claude has the necessary background to respond appropriately.

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Send the entire conversation history with each request, including all previous messages, to preserve full context.

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Store the conversation in a vector database and retrieve relevant past messages based on the current query.

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Rely on Claude's built-in memory feature to automatically remember previous interactions across sessions.

Question 10

A support team is building a Claude-powered assistant that answers questions using a 200-page employee handbook. They place the entire handbook inside <handbook> tags in the system prompt and the user's question at the end of the user turn. Testing shows Claude sometimes ignores details buried in the middle of the handbook. Which change best addresses this while keeping the same model and context window?

Accepted Answer

Move the most relevant handbook sections to the beginning of the context, just before the user's question, and keep the full handbook available.. Long-context performance in Claude is not uniform: content near the beginning and end tends to be used more reliably than content buried in the middle. When a relevant section is stranded mid-document, retrieval degrades. Repositioning the most relevant excerpts adjacent to the question, while keeping the full handbook available, aligns the strongest signal with the query and fixes the observed symptom without changing model or window size.

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Lower the temperature to 0 so Claude becomes deterministic and stops skipping handbook details.

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Split the handbook into 200 separate user turns so each page gets equal attention from Claude.

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Increase the max_tokens parameter so Claude has more room to reason about the handbook before answering.