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Anthropic CCA-F Exam Syllabus Topics:

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

1. 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.
Your code review assistant needs to analyze pull requests and provide feedback on three aspects: code style compliance, potential security issues, and documentation completeness.
Each aspect requires reading files, running analysis tools, and generating a report section. The review process follows the same three-step workflow for every PR. Which task decomposition pattern is most appropriate for this workflow?

A) Prompt chaining - break the review into sequential steps where each aspect (style, security, documentation) is analyzed separately, with outputs combined in a final synthesis step.
B) Single comprehensive prompt - include all instructions in one prompt and let the model handle all three aspects simultaneously.
C) Routing - classify each PR by type (feature, bugfix, refactor) first, then route to different review prompts optimized for that category.
D) Orchestrator-workers - have a central LLM analyze each PR to dynamically determine which checks are needed, then delegate to specialized worker LLMs for each identified subtask.

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

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

3. Production reviews reveal inconsistent handling of uncertainty in final reports. Sometimes conflicting subagent findings are synthesized into a single confident statement (losing nuance), while other times reports over-hedge with excessive qualifications (becoming unhelpful). When the web search agent returns "industry analysts estimate $50B market size (methodology varies)" and the document analysis agent returns "peer-reviewed study estimates $35B (±$7B, 95% CI)," the coordinator either picks one arbitrarily or produces vague statements like "the market may be
$35B-$50B depending on factors." What systematic approach best addresses this?

A) Implement a confidence calibration layer that normalizes subagent uncertainty expressions to standardized probability scores (0.0-1.0), then weight-average findings by their calibrated confidence.
B) Add a verification subagent that cross-references findings across sources, only passing claims to synthesis that are corroborated by at least two independent sources.
C) Instruct the synthesis agent to structure reports with explicit sections distinguishing well- established findings from contested ones, preserving original source characterizations and methodological context.
D) Configure subagents to only report findings meeting a high-confidence threshold, filtering uncertain information before it reaches the coordinator.

4. A customer sends: "This is frustrating. I've explained my issue twice and nothing is being resolved. I want to talk to a real person NOW." The agent has not yet called any tools to investigate their account. What should the agent do?

A) Immediately call escalate_to_human with the conversation history.
B) First call get_customer and lookup_order to gather account context, then escalate to a human agent.
C) Briefly explain what the agent can help with and offer to resolve the issue quickly, escalating only if the customer repeats their request.
D) Acknowledge the frustration and ask one targeted question to understand the specific issue before escalating.

5. You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
You're implementing a caching layer for API responses to speed up the /products endpoint. You have a rough idea-Redis with a 5-minute TTL-but you're new to production caching and aren't sure what other considerations a robust implementation requires. What's the most effective way to start your iterative workflow?

A) Write a specification with your known requirements and "TBD" markers for uncertain areas, having Claude propose solutions for each TBD as it implements.
B) Use plan mode to analyze the current/products endpoint implementation, then provide your caching requirements once Claude explains how the existing code is structured.
C) Start with a minimal request: "Add Redis caching to /products with 5-minute TTL." Add features and fix issues through follow-up prompts as problems surface during testing.
D) Ask Claude to interview you about the caching requirements before implementing, surfacing considerations like invalidation strategies, cache layers, consistency guarantees, and failure modes.

Solutions: