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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions:

1. Which of the following scenarios represent architecturally appropriate use cases for implementing hooks in a production Claude application? Choose 2 correct answers.

A) Normalizing tool outputs, such as date formats or status codes, before they are appended to the context window.
B) Guaranteeing that an extracted JSON object has no semantic reasoning errors.
C) Ensuring Claude maintains a conversational and friendly tone with customers.
D) Intercepting tool calls to deterministically block policy-violating actions before they execute.

2. During a multi-agent synthesis phase, the coordinator receives conflicting financial statistics from a web search subagent and a document analysis subagent. What is the required architectural pattern for handling this conflict?

A) Maintain structured claim-source mappings to preserve information provenance (source, confidence, timestamp) allowing for an informed resolution.
B) Discard both values and prompt the user to manually input the correct statistic.
C) Average the two conflicting values together to provide a balanced estimate.
D) Arbitrarily select the value from the document subagent, as internal documents are generally more reliable.

3. When designing the output structure for a synthesis agent that processes diverse data streams (e.g., financial statistics, news prose, and technical specifications), what is the recommended presentation pattern?

A) Render different content types appropriately (e.g., financial data as tables, news as prose, technical findings as structured lists) rather than forcing a uniform format.
B) Restrict the output strictly to natural language paragraphs to maintain conversational fluency.
C) Use the Code Execution tool to automatically generate an Excel workbook containing all data types.
D) Convert all extracted data into a uniform, flat JSON array to simplify downstream parsing.

4. During a long-running customer support session, the agent repeatedly summarizes the conversation history to save context space. By turn 10, the agent forgets the customer's specific account number and the exact promotional price they were quoted. How should you redesign the context management to prevent this?

A) Increase the frequency of progressive summarization to occur every 2 turns instead of 5.
B) Restart the session from scratch after 5 turns and ask the customer to repeat their details.
C) Extract transactional facts into an immutable 'case facts' block positioned at the start of the context that is never summarized.
D) Store the conversation in an external database and run a lexical search query on every turn.

5. During a long-running customer support session, the agent repeatedly summarizes the conversation history to save context space. By turn 10, the agent forgets the customer's specific account number and the exact promotional price they were quoted. How should you redesign the context management to prevent this?

A) Increase the frequency of progressive summarization to occur every 2 turns instead of 5.
B) Restart the session from scratch after 5 turns and ask the customer to repeat their details.
C) Extract transactional facts into an immutable 'case facts' block positioned at the start of the context that is never summarized.
D) Store the conversation in an external database and run a lexical search query on every turn.

Solutions: