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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Agentic Architecture & Orchestration	27%	- Anthropic Agent SDK usage 1. Task spawning and tool management 2. Session state and context handling - Agent design patterns 1. Hub-and-spoke multi-agent systems 2. Agent loops and control flow
Topic 2: 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. MCP server and client setup 2. Tools, resources, and prompts integration
Topic 3: Claude Code Configuration & Workflows	20%	- CI/CD and development integration 1. Plan mode vs direct execution 2. Pipeline automation and review - Configuration files and structure 1. Custom commands and workflows 2. CLAUDE.md and rules system
Topic 4: Context Management & Responsible AI	15%	- Context window optimization 1. Token management and truncation strategies 2. Context retention and summarization - Safety and compliance 1. Constitutional AI principles 2. Refusal handling and risk mitigation
Topic 5: Prompt Engineering & Structured Output	20%	- Structured data generation 1. JSON schema enforcement 2. Output validation and reliability - Advanced prompting techniques 1. Few-shot and chain-of-thought prompting 2. System prompts and role framing

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q87-Q92):

NEW QUESTION # 87
During an extended codebase exploration session in Claude Code, context degradation begins to occur. What is the most effective strategy to persist key findings across context boundaries?

A. Increase the model's temperature parameter to recall lost context.
B. Use the - -json-schema flag to aggressively compress output.
C. Continuously re-feed the entire codebase into the prompt every 5 turns.
D. Have agents maintain scratchpad files to record key findings and reference them in subsequent questions.

Answer: D

Explanation:
Context degradation in extended sessions causes models to lose track of earlier details. Using scratchpad files to persist critical intermediate state externally ensures that key findings survive context compression and session boundaries.

NEW QUESTION # 88
What is the primary architectural difference between enforcing a business rule via a system prompt versus using an Agent SDK hook?

A. Prompts are deterministic, while hooks rely on the model's probabilistic reasoning.
B. Prompts execute after the tool runs, while hooks execute before the prompt is processed.
C. Prompts require an active MCP server connection, while hooks run strictly within the Claude desktop application.
D. Hooks provide 100% reliable deterministic enforcement, whereas prompts are probabilistic and can occasionally be ignored by the LLM.

Answer: D

Explanation:
The critical distinction is that hooks execute as deterministic code, making them 100% reliable for critical policies, compliance, and security blocks. Prompts rely on the LLM's probabilistic pattern matching, meaning they can and occasionally will be ignored by the model.

NEW QUESTION # 89
In a large organization, skill naming conflicts can occur. If a personal skill, a project-level skill, a plugin skill, and an enterprise managed skill all share the identical name, what is the correct priority order (from highest to lowest) that Claude Code uses to resolve the conflict?

A. Plugins > Project > Personal > Enterprise
B. Project > Enterprise > Personal > Plugins
C. Personal > Enterprise > Project > Plugins
D. Enterprise > Personal > Project > Plugins

Answer: D

Explanation:
Claude Code resolves skill name conflicts following a strict priority hierarchy: Enterprise (managed settings) take highest priority, followed by Personal skills, Project skills, and finally Plugins at the lowest priority.

NEW QUESTION # 90
When building an agent that queries a backend order system, the lookup_order tool returns a JSON object with 40+ fields, but the age only needs the order status, shipping date, and tracking number. What context optimization technique should be applied?

A. Use a PreToolUse hook to block the tool call entirely and escalate to a human.
B. Implement a PostToolUse hook to trim the verbose tool outputs, returning only the relevant fields to the agent's context.
C. Utilize the /compact command immediately after every tool call.
D. Prompt the model to ignore the irrelevant fields after the full JSON object is appended to the conversation history.

Answer: B

Explanation:
Tool results accumulate in context and consume tokens disproportionately. Trimming verbose tool outputs to only relevant fields before they accumulate in the context window prevents context exhaustion and improves reasoning focus.

NEW QUESTION # 91
Your structured data extraction pipeline implements a validation-retry loop. When an extracted value fails a business logic check (e.g., line items do not sum to the total), how should the retry prompt be structured?

A. Automatically increase the model temperature and resubmit the original prompt.
B. Send a follow-up request including the original document, the failed extraction, and the specific validation error details.
C. Switch to a more capable model and restart the session without the failed extraction history.
D. Return a generic message such as There were errors in your extraction. Please try again.

Answer: B

Explanation:
A successful validation-retry loop requires appending specific error details (not generic messages) to the prompt and supplying both the source document and the failed extraction. Generic retry messages give the model no signal for what to fix.

NEW QUESTION # 92
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