CCAO-F Practice Test 7 — 10 Questions

Question 1

A developer writes a Python script that calls the Claude Messages API. The script currently reads the API key from a hardcoded string in the source file and the file is committed to a public repository. Which change best addresses the security concern while keeping the script functional?

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

Accepted Answer

Move the key into an environment variable and read it at runtime with os.environ, keeping the key out of source control.. Credentials must never live in source control. Reading the API key from an environment variable at runtime removes it from the codebase and repository history, so the exposure is eliminated while the script continues to authenticate normally. Any key that has already been committed should also be rotated.

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Base64-encode the hardcoded key so it is not readable as plain text in the repository.

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Obfuscate the key by splitting it across several string concatenations in the code before passing it to the client.

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Add the source file to a .gitignore entry so future commits exclude it, leaving the existing key in the file.

Question 2

A developer is writing a prompt for Claude to classify support tickets into categories. They want to reduce ambiguous or inconsistent labels. Which TWO techniques should they apply? (Choose two.)

Accepted Answer

Define each category with clear criteria and include boundary examples.. Consistent classification depends on unambiguous category definitions and deliberate reasoning. Clear criteria with boundary examples give Claude explicit rules, while asking for reasoning before the label encourages careful evaluation of the ticket. Together they reduce arbitrary or inconsistent assignments without relying on sampling changes.

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Set the temperature to 1 to encourage diverse labeling.

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Provide only the category names without descriptions to keep the prompt short.

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Ask Claude to output multiple labels for each ticket to cover all possibilities.

Question 3

A financial analyst is building a Claude-powered assistant that must extract the total amount due from scanned invoices. The invoices vary widely in layout and wording, and the extracted value is later used to trigger payments. The analyst wants to maximize accuracy and avoid plausible but incorrect numbers. Which prompting approach is most appropriate?

Accepted Answer

Use a prompt that instructs Claude to extract the total only from the provided invoice text, and to respond with 'Not found' if the total is not explicitly stated.. Extraction tasks that feed downstream actions like payments require the model to stay strictly within the provided source text and to signal when the requested value is absent. Allowing a 'Not found' response prevents fabrication and supports human review. Approaches that encourage guessing, raise randomness, or force pattern application regardless of source content all increase the risk of plausible but incorrect values, which is exactly what the analyst must avoid.

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Set the temperature to a high value so Claude explores multiple interpretations of the invoice and selects the most consistent total across them.

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Provide a few-shot example showing a correctly extracted total, then ask Claude to apply the same pattern to any invoice regardless of whether the total is present.

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Instruct Claude to return its best guess for the total, and if uncertain, to generate the most likely amount based on typical invoice patterns.

Question 4

A developer is using the Messages API and wants Claude to adopt a strict persona as a compliance reviewer for every request in a long conversation, without repeating the persona instructions in each user turn. The persona must apply to all messages and should take precedence over casual instructions the user might add later. Where should the developer place these persona instructions?

Accepted Answer

In the system prompt parameter, which is applied across all turns of the conversation.. The system prompt is the correct location for instructions that must frame every turn and carry higher priority than user turns. It persists across the conversation without being repeated, and Claude is trained to respect system-level guidance over casual user instructions, which satisfies the compliance persona requirement.

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As a trailing assistant message that restates the persona before each user reply.

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In the first user message of the conversation, before the actual task.

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In the metadata field attached to the request for logging and traceability.

Question 5

A developer is implementing a retrieval-augmented generation pipeline with the Claude Messages API. Retrieved documents are inserted into the user turn, and the developer wants the model to cite which document supports each claim. Which technique most directly improves the model's ability to attribute statements to specific source documents?

Accepted Answer

Number or tag each retrieved document in the prompt and instruct the model to reference those identifiers in its answer.. Attribution improves when the prompt makes source boundaries explicit. Labeling each retrieved document with a stable identifier and instructing the model to cite that identifier gives it a concrete token to attach to each claim, and gives the application something to validate. Sampling settings and output length do not create source-to-claim mappings, and removing document separators actively destroys the structure needed for citation.

Answer

Increase max_tokens so the model has room to quote each source document verbatim in the answer.

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Concatenate all retrieved documents into one continuous block with no separators to keep the prompt compact.

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Raise the temperature so the model explores more of the retrieved context before answering.

Question 6

A developer is building a document-summarization service that sends very large PDFs to Claude. The first request returns a 413 error with a message indicating the request entity is too large. The developer wants to keep using the Messages API directly. Which change best resolves the error while preserving the ability to summarize the full document?

Accepted Answer

Split the document into smaller chunks and send them in multiple Messages API calls, then combine the partial summaries.. An HTTP 413 signals that the request body exceeds the API's permitted size. The Messages API does not accept arbitrarily large inputs, so the developer must reduce each request's payload. Chunking the document into smaller pieces and summarizing each, then combining results, keeps every request within limits and still covers the whole document. Output-length parameters, streaming flags, and sampling settings do not influence request size limits.

Answer

Set the stream parameter to true so the API streams the request body and bypasses the size limit.

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Lower the temperature to 0 so the model processes the input more efficiently and accepts the larger payload.

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Increase the max_tokens value in the request to allow the API to accept more input.

Question 7

An engineering team wants Claude to classify thousands of support tickets into categories. They need the model to always return one of five exact category labels. Which approach most reliably constrains the output to those labels?

Accepted Answer

List the five allowed labels in the system prompt and instruct the model to output only one of them.. To force output into a closed set of labels, the prompt must enumerate the allowed values and instruct the model to return exactly one. Placing this in the system prompt ensures the constraint applies to every classification request. Vague instructions, high temperature, or downstream keyword matching all allow invalid or ambiguous outputs that break automated pipelines.

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Use a high temperature so the model explores all categories evenly.

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Post-process the model's free-text output with a keyword search for category names.

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Ask the model to 'pick the best category' in the user message.

Question 8

A developer is building a document Q&A service on the Claude API. Each request sends the full 180-page PDF as base64 in a single text content block, and most calls now fail with a 400 error stating the request exceeds the maximum allowed size. The developer wants to keep using the same model and continue asking questions about the whole document. Which approach should the developer take?

Accepted Answer

Upload the PDF with the Files API, then reference the returned file identifier in the message content instead of embedding base64.. Large documents should be ingested once through the Files API and then referenced by identifier in subsequent Messages calls, which keeps each request's inline payload small while preserving full-document context. Re-sending base64, relocating it to the system prompt, or compressing it all leave the oversized or unreadable payload inside the request, so the size rejection or loss of usable content persists.

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Split the document into chunks, send each chunk in its own request, and merge the returned answers inside the application.

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Move the PDF into the system prompt so that the document bytes are not counted against the user message size.

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Compress the base64 string with gzip and place the compressed value in the text content block.

Question 9

A data engineer is using Amazon Bedrock to invoke Claude 3 Haiku for classifying customer feedback into categories. They notice that the model sometimes returns categories that are not in the predefined list. Which change to the prompt is most likely to improve adherence to the allowed categories?

Accepted Answer

Provide a few-shot examples in the prompt showing correct classifications for similar feedback, including the exact category labels.. Few-shot examples in the prompt show Claude the exact category labels and the expected format, which significantly improves adherence to a predefined set. By demonstrating correct classifications, the model learns the pattern and is less likely to invent new categories. Other parameters like temperature, stop sequences, and max_tokens affect randomness, truncation, and length, but do not teach the model which labels are valid.

Answer

Increase the temperature to 1.0 so Claude explores more creative category assignments.

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Add a stop sequence that matches the first invalid category so Claude stops generating when it deviates.

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Reduce the max_tokens parameter so Claude has less room to generate invalid categories.

Question 10

A support-engineering team is designing a Claude prompt to triage incoming bug reports into one of five severity levels. They observe that when the report is ambiguous, Claude sometimes invents a justification for a severity that is not actually supported by the text. They want the prompt to make uncertainty explicit rather than forcing a confident label. Which TWO changes should they make to the prompt? (Choose two.)

Accepted Answer

Add an explicit 'unknown' or 'needs_review' option to the allowed severity set and instruct Claude to choose it when the report lacks sufficient detail.. Grounding each severity in a verbatim evidence quote forces the model to anchor claims in the report text, and offering an explicit needs_review category gives it a legitimate way to express uncertainty instead of inventing support. Together these two changes convert an overconfident classifier into one that surfaces ambiguity for human follow-up.

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Raise the temperature so Claude explores a wider range of possible severity interpretations for each report.

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Ask Claude to think step by step silently and return only the final severity label with no supporting text.

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Provide ten few-shot examples in which every report is confidently assigned one of the five severities.