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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Claude Code Configuration & Workflows	20%	- CI/CD and development integration 1. Pipeline automation and review 2. Plan mode vs direct execution - Configuration files and structure 1. Custom commands and workflows 2. CLAUDE.md and rules system
Topic 2: Prompt Engineering & Structured Output	20%	- Advanced prompting techniques 1. Few-shot and chain-of-thought prompting 2. System prompts and role framing - Structured data generation 1. Output validation and reliability 2. JSON schema enforcement
Topic 3: 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
Topic 4: Context Management & Responsible AI	15%	- Safety and compliance 1. Refusal handling and risk mitigation 2. Constitutional AI principles - Context window optimization 1. Context retention and summarization 2. Token management and truncation strategies
Topic 5: 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

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q54-Q59):

NEW QUESTION # 54
To prevent Claude from fabricating values when a requested field is absent from the source document, how should you engineer your JSON extraction schema?

A. Design the field with "type" : [" string ,"null"] and mark it as optional/nullable.
B. Set the tool parameter tool_choice: " any".
C. Add a system prompt instruction to "never fabricate data".
D. Reduce the temperature to 0.0 to eliminate hallucinations.

Answer: A

Explanation:
If a field is strictly required but the data doesn't exist, the model may fabricate a value to satisfy the schema constraint. Making the field optional or nullable (e.g., allowing 'null') provides a valid escape hatch so the model can comply with the schema without hallucinating.

NEW QUESTION # 55
Why is implementing an escalation rule based on a customer's negative sentiment considered an architectural anti-pattern?

A. Negative sentiment triggers the model's Constitutional A1 safety filters, automatically terminating the session.
B. The system prompt naturally overrides sentiment triggers using the tool_choice: "auto" command.
C. Sentiment analysis requires fine-tuning a custom embedding model, which introduces latency.
D. Sentiment does not correlate with task complexity; an angry customer may have a straightforward request that the agent can resolve autonomously.

Answer: D

Explanation:
Escalating based on negative sentiment is an anti-pattern because sentiment does not equal task complexity. An angry customer asking for a password reset does not inherently require human intervention. Escalation should be based on objective criteria like policy gaps or capability limits.

NEW QUESTION # 56
To optimize a human-in-the-loop workflow with limited reviewer capacity, how should you determine which documents require manual review?

A. Have models output field-level confidence scores and calibrate review thresholds using labeled validation sets.
B. Route documents randomly based on a flat 10% sample rate.
C. Rely on the model's self-reported conversational confidence score in the output.
D. Escalate any document that exceeds 5 pages in length.

Answer: A

Explanation:
For effective human-in-the-loop routing, models should output structured, field-level confidence scores that are then calibrated against labeled validation sets. Self-reported conversational confidence is unreliable for routing.

NEW QUESTION # 57
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. Ask Claude to interview you about the caching requirements before implementing, surfacing considerations like invalidation strategies, cache layers, consistency guarantees, and failure modes.
D. 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.

Answer: C

Explanation:
Because the requirements are incomplete and production caching involves hidden design choices, an interview helps surface invalidation, consistency, failure handling, and other trade-offs before implementation. Anthropic recommends this approach for larger features when important requirements may not yet be identified.

NEW QUESTION # 58
When designing an independent code review instance to evaluate generated code at quicktechie.com, which specific context must be omitted to ensure an objective review?

A. The underlying JSON schemas of the affected components.
B. The list of available MCP tools.
C. The project's CLAUDE .md standards configuration.
D. The reasoning context and conversation history from the generation session.

Answer: D

Explanation:
An independent review instance must lack the prior reasoning context and conversation history of the generator. Supplying a fresh context prevents the reviewer from adopting the same flawed assumptions or confirmation bias that led to any generated bugs.

NEW QUESTION # 59
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