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

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Anthropic CCA-F Exam Syllabus Topics:

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

1. A financial services company plans to integrate Claude into an internal document analysis platform. The architects want to minimize exposure of confidential client information while maintaining high-quality responses. Which approach BEST aligns with Anthropic's recommended architecture?

A) Replace Claude with a smaller local model regardless of task complexity.
B) Remove unnecessary sensitive information before sending prompts whenever possible.
C) Store every prompt permanently for auditing.
D) Send every document without preprocessing to maximize context.

2. Which prompt revision is MOST likely to improve output quality?

A) Include objective, context, constraints, and expected format.
B) "Summarize this."
C) "Help me."
D) Use only uppercase letters.

3. A customer raises three separate issues during one session: a refund inquiry (turns 1-15), a subscription question (turns 16-30), and a payment method update (turns 31-45). At turn 48, the customer asks "What happened with my refund?" The conversation is approaching context limits.
What strategy best maintains the agent's ability to address all issues throughout the session?

A) Summarize earlier turns into a narrative description, preserving full message history only for the active issue.
B) Extract and persist structured issue data (order IDs, amounts, statuses) into a separate context layer.
C) Implement sliding window context that retains the most recent 30 turns.
D) Rely on MCP tools to re-fetch relevant information on demand when the customer references earlier issues.

4. Your agent has called lookup_order multiple times while investigating a customer's return requests. Each response includes 40+ fields (items, shipping details, payment info, status history). Tool outputs now represent the majority of the conversation's context. The customer mentions two more orders they want to discuss. What's the most effective approach before making additional lookups?

A) Move all tool responses to a vector database with semantic indexing, retrieving relevant portions as the conversation continues
B) Proceed with additional lookups without modifying the existing tool output context
C) Have the model generate a natural language summary of each order's key details, replacing structured responses with prose descriptions
D) Extract only the return-relevant fields (items, purchase date, return window, status) from each existing order response, removing verbose details

5. The coordinator provides detailed step-by-step instructions to the web search subagent, specifying exact search queries, source priorities, and date filters. Production monitoring reveals three issues: (1) the subagent reports "insufficient results" rather than trying alternative approaches when pre-specified searches fail, (2) research quality drops for emerging topics that don't match expected patterns, and (3) the subagent rarely surfaces valuable tangential sources.
What's the most effective way to improve subagent adaptability?

A) Add explicit fallback directives to the detailed instructions: "If specified searches yield fewer than N results, attempt alternative query formulations before reporting failure."
B) Remove procedural details entirely, delegating with simple goals like "research X thoroughly" and relying on the subagent's general capabilities.
C) Specify research goals and quality criteria (coverage breadth, source diversity, recency) rather than procedural steps, letting the subagent determine its search strategy.
D) Implement a topic classification step where the coordinator categorizes requests as "well-defined" or "exploratory" and uses different instruction styles for each category.

Solutions: