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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Context Management & Responsible AI	15%	- Safety and compliance 1. Constitutional AI principles 2. Refusal handling and risk mitigation - Context window optimization 1. Token management and truncation strategies 2. Context retention and summarization
Tool Design & MCP Integration	18%	- Tool definition and best practices 1. Tool descriptions and selection logic 2. Error handling and validation - Model Context Protocol (MCP) 1. Tools, resources, and prompts integration 2. MCP server and client setup
Claude Code Configuration & Workflows	20%	- Configuration files and structure 1. CLAUDE.md and rules system 2. Custom commands and workflows - CI/CD and development integration 1. Plan mode vs direct execution 2. Pipeline automation and review
Prompt Engineering & Structured Output	20%	- Structured data generation 1. JSON schema enforcement 2. Output validation and reliability - Advanced prompting techniques 1. System prompts and role framing 2. Few-shot and chain-of-thought prompting
Agentic Architecture & Orchestration	27%	- Anthropic Agent SDK usage 1. Session state and context handling 2. Task spawning and tool management - Agent design patterns 1. Hub-and-spoke multi-agent systems 2. Agent loops and control flow

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q34-Q39):

NEW QUESTION # 34
A customer support agent handles high-ambiguity requests. Which of the following is a valid, architecturally sound trigger for escalating a case to a human agent?

A. The conversation exceeds 10 turns.
B. The model's self-reported confidence score drops below 0.7.
C. The customer exhibits highly negative sentiment.
D. The agent detects a policy gap not covered by its existing rules.

Answer: D

Explanation:
Valid escalation triggers include policy gaps (situation not covered by rules), explicit customer requests for a human, capability limits, and business thresholds. Sentiment and self-reported confidence are unreliable metrics for escalation.

NEW QUESTION # 35
The configuration hierarchy in Claude Code resolves settings from multiple levels. Which of the following are valid configuration layers where CLAUDE.md files can be placed? Choose 2 correct answers.

A. Directory-level (e.g., src/api/CLAUDE .md) for module-scoped conventions.
B. User-level (e.g., 4. claude/CLAUDE. md) for personal preferences.
C. Global system-level (e.g., /etc/c1aude/CLAl_JDE. md) for machine-wide mandates.
D. Cloud-level (e.g., s3 : //config/CLAUDE .md) for distributed synchronization.

Answer: A,B

Explanation:
The three valid configuration layers in Claude Code are User-level (-/.claude/CLAUDE.md), Project-level (.claude/CLAUDE.md), and Directory-level (scoped to specific subdirectories). Global system-level and cloud-level configs are not standard mechanisms.

NEW QUESTION # 36
What is the primary architectural difference between programmatic enforcement and prompt-based guidance in a multi-step workflow?

A. Programmatic enforcement provides deterministic compliance (such as prerequisite gates), whereas prompt instructions have a non-zero failure rate.
B. Prompt-based guidance runs before tool execution, whereas programmatic enforcement only runs after tool execution.
C. Programmatic enforcement executes synchronously, while prompt guidance requires the Message Batches API.
D. Prompt-based guidance is 100% reliable, whereas programmatic enforcement relies on probabilistic LLM reasoning.

Answer: A

Explanation:
Programmatic enforcement (such as hooks and prerequisite gates) executes as code, providing deterministic, 100% reliable guarantees. Prompt-based guidance relies on the LLM's probabilistic pattem matching, carrying a non-zero failure rate where the model may ignore the instructions.

NEW QUESTION # 37
A quicktechie.com development team wants to run a comprehensive codebase audit analyzing technical debt every weekend. Which API approach is most architecturally sound and cost-effective?

A. The Message Batches API.
B. A single, long-running extended session using the- -resume flag.
C. Multi-agent orchestration running locally.
D. Synchronous API calls executed in parallel.

Answer: A

Explanation:
The Message Batches API is ideal for non-urgent, latency-tolerant workloads like overnight or weekend technical debt audits. It processes requests asynchronously within a 24-hour window and offers a 50% cost savings over synchronous calls.

NEW QUESTION # 38
The document analysis agent has a single analyze_documnet tool that takes a document and a free-text instruction parameter. During evaluation, requests like "extract the key financial metrics" often return narrative summaries, while "summarize the methodology" sometimes returns raw data tables. The synthesis agent reports that 35% of analysis results require re-requests with clarified instructions. What's the most effective way to improve reliability?

A. Have the coordinator pre-classify each analysis request before passing instructions to the document analysis agent
B. Keep the single tool but add an analyze_type enum parameter requiring explicit selection between extraction, summarization, and verification modes
C. Enhance the tool description with detailed examples showing how different instruction phrasings should map to different output formats
D. Split the generic tool into purpose-specific tools - extract_data_points, summarize_content, verify_claim_against_source - each with defined input/output contracts

Answer: D

Explanation:
Purpose-specific tools with clear input and output contracts remove ambiguity about what kind of analysis is expected. Separate extraction, summarization, and verification tools make the agent choose the correct operation and produce outputs in the format downstream agents can reliably use.

NEW QUESTION # 39
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