CCAO-F Practice Test 13 — 15 Questions

Question 1

A developer is using the Anthropic Messages API to build a conversational agent. They want to maintain context across multiple turns and ensure that the model's responses are consistent with the system prompt. Which API feature should they use to provide the system prompt?

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

Accepted Answer

Use the 'system' parameter in the API request to provide the system prompt.. The Anthropic Messages API provides a dedicated 'system' parameter for supplying a system prompt. This parameter is separate from the conversational messages and ensures that the model's behavior is consistently guided by the system instructions across all turns. Using this parameter is the correct and efficient way to set a system prompt.

Answer

Prepend the system prompt to every user message in the conversation.

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Set the 'role' of the first message to 'system' in the messages array.

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Include the system prompt as the first user message in the messages array.

Question 2

A developer is choosing between Claude 3 Haiku and Claude 3.5 Sonnet for a real-time chat application that requires very low latency and handles simple, short queries. Cost is a primary concern. Which model is most appropriate and why?

Accepted Answer

Claude 3 Haiku, because it is optimized for speed and cost-effectiveness while still handling straightforward tasks well.. Claude 3 Haiku is optimized for speed and cost, making it the best fit for a real-time chat application with simple, short queries. It provides low latency and low cost per token while maintaining adequate quality for straightforward tasks. Larger models like Claude 3.5 Sonnet or Claude 3 Opus offer more capability but at higher cost and latency, which does not align with the stated priorities.

Answer

Claude 3.5 Sonnet, because it has the largest context window and can handle more concurrent users.

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Claude 3.5 Sonnet, because it is the only model that supports streaming responses for real-time chat.

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Claude 3 Opus, because it provides the highest accuracy for all query types, ensuring customer satisfaction.

Question 3

A developer wants Claude to return structured data that another service can parse automatically. They need the output to follow a fixed schema every time, with no conversational text around it. Which approach is most appropriate?

Accepted Answer

Describe the desired schema in the prompt and ask Claude to respond only with valid JSON that matches it.. Structured output is achieved by specifying the schema precisely and constraining Claude to emit only matching JSON. This removes the need for brittle post-processing and satisfies the requirement that no conversational text surround the data. Alternatives such as reasoning-first responses, Markdown tables, or open-ended formatting all introduce parsing uncertainty.

Answer

Ask Claude to explain its reasoning first and then append the structured data at the end of the response.

Answer

Set max_tokens to a large value and let Claude choose whichever format best represents the data.

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Request the output as a Markdown table and convert it to JSON on the client side.

Question 4

A media company is deploying Claude to help journalists draft articles. The editorial team wants to ensure the tool is used responsibly and that published content remains trustworthy. Which TWO practices best support responsible use of Claude in this newsroom workflow? (Choose two.)

Accepted Answer

Require journalists to verify all factual claims, quotations, and statistics generated by Claude before publication.. Responsible newsroom use of Claude combines human verification of facts with transparent disclosure of AI assistance. Verification prevents hallucinations from reaching the public, while disclosure preserves reader trust. Together they keep journalists accountable for published content and treat Claude as a drafting aid rather than an autonomous publisher.

Answer

Instruct Claude to invent plausible expert quotes when real sources are unavailable to meet tight deadlines.

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Allow Claude to publish articles directly to the public site to speed up the news cycle and reduce editorial costs.

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Remove all human editors from the process so that Claude's outputs are not altered by subjective human bias.

Question 5

An analyst is using Claude to summarize user feedback for a product team. While reviewing outputs, the analyst notices that Claude sometimes invents specific statistics, such as '78% of users reported difficulty with onboarding,' that do not appear anywhere in the source feedback. The analyst wants to reduce this behavior in the workflow. Which approach is most appropriate?

Accepted Answer

Instruct Claude to summarize using only the provided feedback text, to avoid introducing statistics not present in the source, and to flag when it cannot support a claim.. Fabricated statistics appear when the model fills gaps beyond the supplied source material. Instructing Claude to summarize only from the provided feedback and to flag unsupported claims constrains generation to what the input can support. Confidence scores, disclaimers, and higher temperature do not prevent the fabrication and may obscure it, so source-constrained prompting is the appropriate remedy for this workflow.

Answer

Instruct Claude to include confidence scores next to every statistic it generates, so readers can judge reliability.

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Increase the model's temperature so it produces more varied summaries and is less likely to repeat the same invented figures.

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Ask Claude to append a disclaimer that some figures may be illustrative and should be verified before use.

Question 6

An engineer is writing a first integration against the Claude Messages API and needs to select the correct endpoint and required authentication header. The application will send a messages array with user and assistant turns and read the response content blocks. Which configuration is correct?

Accepted Answer

POST to /v1/messages with the API key in the x-api-key header and an anthropic-version header.. The Messages API is invoked by posting to /v1/messages, authenticating with the x-api-key header, and including an anthropic-version header to select the API version. The request body carries the model, max_tokens, and the messages array, and the reply is returned as content blocks. Other paths or query-string credentials do not match the supported interface.

Answer

POST to /v1/chat/completions with the API key in an Authorization: Bearer header.

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POST to /v1/complete with the API key in an Authorization: Bearer header.

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POST to /v1/messages with the API key as a query string parameter named api_key.

Question 7

A legal-tech startup uses Claude to summarize deposition transcripts that are frequently 80,000 to 120,000 tokens long. Early tests show the model sometimes ignores instructions placed near the top of the prompt and produces summaries that omit late sections of the transcript. Which TWO prompt-engineering changes should the developer make to improve instruction adherence across the full context? (Choose two.)

Accepted Answer

Wrap each deposition section in clearly labeled tags such as <section id="12"> and require the summary to cite section IDs.. Long-context adherence improves when the instruction sits after the material it governs and when the document carries structural anchors the model can traverse. Placing instructions last exploits stronger attention near the end of the prompt, while labeled sections with a citation requirement force coverage of every part of the transcript and make omissions detectable.

Answer

Repeat the summarization instructions verbatim in the system prompt and again immediately before the transcript.

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Raise the temperature to encourage the model to explore more of the transcript.

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Increase the max_tokens parameter so the summary can be longer.

Question 8

A product team is integrating Claude into a children's educational app. Before launch, they must ensure the model does not produce age-inappropriate content. Which action best aligns with Anthropic's safety guidance for this deployment?

Accepted Answer

Implement additional input and output filtering, plus human review, specifically tuned for the children's audience.. Deploying Claude in a child-facing context requires going beyond baseline protections. The correct approach is to add audience-specific input/output filtering and human review, as Anthropic emphasizes that safety is a shared responsibility. Relying solely on defaults, disabling safeguards, or testing only with adults all fail to address the unique risks of a children's educational app.

Answer

Rely on the model's default safety filters without additional testing, since Anthropic already ensures all outputs are child-safe.

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Only test with adult users first and assume the same behavior will hold for children.

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Disable all safety features to avoid false refusals, because educational content is inherently safe.

Question 9

A developer is using the Claude API to build a content moderation tool. They want to ensure that Claude's responses comply with Anthropic's usage policies. Which of the following is a required step when using the API for moderation?

Accepted Answer

Ensure that the tool does not generate or promote harmful content, and adhere to Anthropic's Acceptable Use Policy.. The key requirement when using the Claude API for moderation is to comply with Anthropic's Acceptable Use Policy, which prohibits generating or promoting harmful content. This ensures the tool itself does not become a source of harm. Other practices like rate limiting or logging are beneficial but not mandated by the policy. Adherence to the AUP is essential for all applications.

Answer

Log all API requests and responses for auditing purposes.

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Implement rate limiting to prevent excessive API calls.

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Use a specific model version that is optimized for moderation tasks.

Question 10

A team is instrumenting a production Messages API integration and wants to programmatically detect throttling and transient server faults so their client library can back off and retry. Which TWO HTTP status codes should their response handler treat as retryable conditions? (Choose two.)

Accepted Answer

529. Retryable conditions are those where the same request may succeed later without modification. Rate limiting and infrastructure overload both fall in that category because they reflect temporary capacity or throughput constraints on the service side, not defects in the request. Client-side errors such as malformed input, bad credentials, or insufficient permissions will reproduce deterministically and should be fixed rather than retried.

Answer

401

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400

Answer

403

Question 11

A developer is building a multi-turn chat application with the Anthropic Messages API. After several exchanges, they notice Claude loses track of details mentioned early in the conversation. Which action best addresses this while staying within the API's design?

Accepted Answer

Resend the full prior message history with each new request.. The Messages API does not persist conversation state; each call is independent. To let Claude reference earlier details, the application must include those prior turns in the messages array of every request. A larger context window or a system instruction cannot substitute for actually sending the history, and temperature has no bearing on recall.

Answer

Increase the temperature to improve recall of earlier turns.

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Add a system prompt saying 'Remember all previous messages.'

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Switch to a larger Claude model with a bigger context window.

Question 12

A developer is using the Anthropic Messages API to build a conversational agent. They want to maintain context across multiple turns and ensure that the model's responses are consistent with the system prompt. Which API feature should they use to provide the system prompt?

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

Accepted Answer

The sliding window may discard important early instructions or context that Claude needs to maintain consistent behavior throughout the conversation.. A sliding window reduces token usage by dropping older turns, but it also removes early context that may be essential for consistent behavior. Claude does not retain memory outside the provided messages, so once early instructions or facts are dropped, they are unavailable. The developer should consider summarization or selective retention of important early context to balance token efficiency with conversational coherence.

Answer

The sliding window will increase latency because Claude must reprocess the entire conversation history on each turn.

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Claude will automatically summarize the dropped turns and include the summary in its response, which may introduce hallucinations.

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The sliding window will cause Claude to exceed the model's maximum context window because older turns are still counted in the token limit.

Question 13

A team is building a Claude-powered chatbot for mental health support. They want to ensure responsible deployment. Which TWO practices should they implement? (Choose two.)

Accepted Answer

Include clear disclaimers that the chatbot is not a substitute for professional help and provide crisis hotline information.. For a mental health chatbot, responsible deployment requires disclaimers with crisis resources and a human escalation path. These measures protect users by setting expectations and ensuring high-risk cases reach professionals. Diagnosing, prescribing, or never escalating are unsafe and violate the principle that AI should not replace licensed care in high-stakes domains.

Answer

Allow the model to diagnose mental health conditions to provide faster assistance.

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Train the model to keep conversations confidential by never escalating to humans, to build trust.

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Use the model to prescribe medication based on symptoms described by users.

Question 14

A developer is building a document-summarization service with the Claude API. After upgrading from a Claude 3 model to a newer model, the application's HTTP requests start failing with a 400 error complaining about an unrecognized parameter. The developer's request body includes a field that was accepted before. Which change to the request body is MOST likely required?

Accepted Answer

The deprecated 'max_tokens_to_sample' field must be replaced with 'max_tokens'.. Modern Claude models are called through the Messages API, where the output-length cap is expressed as max_tokens. Requests that still carry the older max_tokens_to_sample field are rejected as malformed, so the minimal correct fix is to rename that field. Parameters such as temperature and system remain top-level and valid, so restructuring them would not resolve the 400 error.

Answer

The 'messages' array must be renamed to 'prompt' to match the newer schema.

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The 'system' prompt must be converted into a user message before sending.

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The 'temperature' parameter must be moved inside the 'metadata' object.

Question 15

A developer is sending a large knowledge-base article to the Claude Messages API for analysis. The API returns HTTP 400 with an error message indicating the input is too long. Which statement best describes the correct remediation?

Accepted Answer

Reduce or chunk the input content so the prompt plus the requested output fits within the model's context window.. An HTTP 400 with an explicit input-length message means the prompt plus expected completion exceeds the model's context window. The only correct fix is to shrink the input through chunking, summarization, or removal of irrelevant content. Parameters like max_tokens control output length, and retry logic only helps transient failures, so neither resolves a deterministic size violation.

Answer

Switch the request method from POST to GET to bypass the length restriction.

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Retry the request with exponential backoff because the error is transient.

Answer

Increase the max_tokens parameter so the model has room for both the input and the output.