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Anthropic CCA-F Exam Syllabus Topics:

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

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

2. An engineering team notices that Claude occasionally provides answers beyond the company's internal policy documents. They want responses to rely only on approved documentation whenever possible. Which solution is MOST appropriate?

A) Disable system prompts
B) Use Retrieval-Augmented Generation (RAG)
C) Shorten every prompt
D) Increase temperature to 1.0

3. You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
An engineer submits two requests:
- Request A: "Rename the getUserData function to fetchUserProfile
everywhere it's used."
- Request B: "Improve error handling throughout the data processing
module - add try/catch blocks, meaningful error messages, and ensure
failures don't silently corrupt data."
For which request does specifying an explicit multi-phase workflow (such as analyze -> propose
-> implement with review) most improve outcome quality?

A) Request B, the error handling task
B) Neither request benefits significantly
C) Request A, the function rename task
D) Both requests benefit equally

4. You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
When analyzing complex legal cases that cite multiple precedents, the document analysis subagent processes each sequentially. A landmark case citing 12 precedents takes over 3 minutes to analyze completely. What's the most effective way to reduce this latency while preserving the coordinator's ability to monitor and debug the system?

A) Create a recursive agent hierarchy where analysis agents subdivide work among child agents until reaching single-precedent granularity
B) Implement a message queue where precedent analysis tasks are processed asynchronously by a pool of worker agents
C) Enable the document analysis subagent to spawn its own specialized subagents dynamically when it encounters cases with many citations
D) Have the coordinator spawn parallel document analysis subagents, each handling a subset of precedents, then aggregate results before synthesis

5. An application frequently repeats the same background instructions in every API request. What is a potential downside?

A) Increased hallucinations only.
B) Lower reliability.
C) Reduced model capability.
D) Higher token usage and cost.

Solutions: