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Anthropic Claude Certified Architect Foundations (CCA-F) CCA-F Prüfungsfragen mit Lösungen

1. 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.
A user is expanding the research system beyond its single web search agent by adding specialized data sources. They add a financial API agent that returns structured JSON with revenue, margins, and growth rates; a news monitoring agent that returns prose summaries of recent developments; and a patent analysis agent that returns structured lists of technology areas. The synthesis agent combines these into executive briefings. Currently, it converts everything to bullet points, causing financial comparisons to lose tabular clarity and news summaries to lose narrative flow. What change would most improve briefing quality?

A) Standardize all subagent outputs to JSON with fields for claim, evidence, source, and confidence
B) Standardize all subagent outputs to prose summaries with inline citations
C) Update the synthesis agent to render each content type appropriately - financial data as tables, news as prose
D) Add a format conversion layer between subagents and synthesis that transforms all outputs to a common intermediate representation

2. A customer returns 4 hours after the initial session about the same billing dispute. The previous
32-turn session contains lookup_order results showing "Status: PENDING, Expected resolution:
24-48 hours." In testing, you observe that when resuming sessions with stale tool results, the agent often references the outdated data in responses (e.g., "I see your refund is still being processed") even after subsequent fresh tool calls return different information. What approach most reliably handles returning customers?

A) Resume with full history and configure the agent to automatically re-call all previously-used tools at session start to ensure data freshness.
B) Resume with full history and add a system prompt instruction telling the agent to always prefer the most recent tool results when multiple calls to the same tool exist in context.
C) Start a new session, inject a structured summary of the previous interaction (issue type, actions taken, resolution status), then make fresh tool calls before engaging.
D) Resume with full history but filter out previous tool_result messages before resuming, keeping only the human/assistant turns so the agent must re-fetch needed data.

3. 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.
Your monorepo contains shared coding standards in /docs/standards/ : security-rules.md (for services handling user data), testing-petterns.md (for all packages), and api-conventions.md (for API-facing services). Your 15 packages are organized by feature domain ( /packages/auth/,
/packages/billing/, /packages/notifications/ , etc.) without naming conventions indicating which handle user data or expose APIs. Package maintainers are expected to configure their own local development settings, as they understand their package's domain requirements. Currently, all package CLAUDE.md files duplicate all three standards, applying irrelevant guidance. What's the most effective approach?

A) Use @imports in each package's CLAUDE.md to reference only the specific standard files relevant to that package, based on the maintainer's domain knowledge.
B) Create .claude/rules/ files for each standard with YAML frontmatter paths listing every package directory where that standard should apply.
C) Put all standards in the root CLAUDE.md with override instructions like "ignore security-rules.md when working in packages that don't handle user data."
D) Create a shared-stendards.md that uses @imports to combine all three standards, then have each package's CLAUDE.md import that combined file.

4. 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've documented API error handling conventions in a CLAUDE.md file at your project root, specifying that endpoint handlers should use a custom ApiError class. After several sessions, you notice Claude Code sometimes follows these conventions and sometimes uses generic try/catch blocks with string messages. The inconsistency appears random across different coding sessions. What's the most efficient first diagnostic step?

A) Search for conflicting instructions in ~/.claude/CLauDe.md or ~/.claude/rules/ that might override your project conventions.
B) Add a more detailed code examples to your CLAUDE.md showing the exact ApiError usage pattern for different endpoint types.
C) Create path-specific rules in .claude/rules/handlers.md with YAML frontmatter scoping the error handling instructions to your API handler files.
D) Run /memory to check which memory files are loaded and verify your CLAUDE.md is included.

5. After 30+ turns, your conversational assistant shows noticeably slower responses and occasionally produces less coherent outputs. Investigation reveals: (1) average conversations reach 50,000 tokens by turn 35, (2) production logs show 94% of user messages only reference the previous 3-5 exchanges, (3) the 6% of queries referencing earlier context typically ask about information the user could easily re-state. Your goal is to improve response speed and quality while maintaining good user experience. What's the most effective approach?

A) Enable prompt caching and continue sending the complete conversation history, using cached prefixes to reduce per-request costs while preserving all context.
B) Build a retrieval system that stores all conversation turns and uses semantic search to pull in relevant historical context only when the current query appears to reference past information.
C) Implement a summarization layer that progressively compresses older conversation turns into a running summary while keeping the most recent 5-6 turns verbatim, maintaining full historical context in condensed form.
D) Implement a sliding window keeping only the system prompt and last 8-10 turns. When users reference earlier context, acknowledge the limitation and ask them to re-state the relevant information.

Fragen und Antworten: