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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
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
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. CLAUDE.md and rules system 2. Custom commands and workflows
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
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
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

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q29-Q34):

NEW QUESTION # 29
When extracting financial summaries from unstructured documents, how can you design a self-correction validation flow directly within the extraction process?

A. Increase the max_tokens limit to give the model more space to reason about the math.
B. Execute the extraction inside a PreToolUse hook.
C. Provide few-shot examples of flawless mathematical calculations.
D. Extract a calculated_total alongside the stated_total to programmatically flag discrepancies.

Answer: D

Explanation:
Designing self-correction validation flows involves structuring the schema to extract cross-check values (like extracting a calculated total alongside a stated total). This allows downstream business logic to easily flag discrepancies and trigger a targeted retry loop.

NEW QUESTION # 30
You are designing an MCP interface for a backend system. When should you choose to implement an MCP Resource instead of an MCP Tool?

A. When you need to execute bash commands on the local machine.
B. When you want to expose a static content catalog or documentation that can be fetched to provide context without requiring exploratory tool calls.
C. When you need the model to execute side-effects, such as updating a database record.
D. When you need to provide pre-built conversation templates.

Answer: B

Explanation:
MCP Resources are used to expose content catalogs (like documentation, static data, or schemas) allowing clients to fetch data directly to inject into prompts. Tools are used when you want the model to perform actions, execute logic, or mutate state.

NEW QUESTION # 31
You must enforce a strict business rule that the agent escalates any refund request exceeding $500. What is the most deterministic method to implement this?

A. Configure tool_choice to 'auto' so the model can dynamically assess the financial risk.
B. Add a capitalized warning in the system prompt explicitly forbidding high-value refunds.
C. Provide 5 negative few-shot examples showing the agent refusing high-value refunds.
D. Use an Agent SDK hook (e.g., PreToolUse or PostToolUse) to programmatically intercept and block the action.

Answer: D

Explanation:
For critical business rules requiring deterministic compliance, programmatic enforcement via Agent SDK hooks is required. Prompt-based instructions (including warnings and few-shot examples) are probabilistic and can occasionally be ignored by the LLM.

NEW QUESTION # 32
During data extraction, a semantic validation check fails because the extracted line items do not sum to the total invoice amount. What is the recommended best practice for implementing the validation-retry loop?

A. Append specific validation error details to the prompt, stating which field failed and the nature of the discrepancy.
B. Rely entirely on the model's self-reported confidence scores to automatically adjust the extraction.
C. Send a generic retry message such as 'There were errors in your extraction. Please try again.' to reset the model's reasoning.
D. Increase the max_tokens limit on the retry request to give the model more space to self-correct.

Answer: A

Explanation:
Generic retry messages give the model no signal for what to fix. The recommended practice is to use specific error feedback (e.g., 'Line items total $450 doesn't match the stated subtotal $500'). This explicit feedback gives the model a clear correction target during the retry loop.

NEW QUESTION # 33
A data extraction pipeline uses few-shot examples to teach Claude how to extract invoice line items. To ensure the model handles unexpected formats reliably, what must be included in the few-shot examples?

A. Only examples of perfectly formatted, standard invoices to establish a strong baseline.
B. At least one example that demonstrates how to handle an ambiguous scenario or edge case.
C. Extensive XML tags defining the model's underlying neural architecture.
D. A negative example showing the model generating conversational text instead of JSON.

Answer: B

Explanation:
Few-shot examples should demonstrate ambiguous-case handling and edge case coverage. At least one example should demonstrate an ambiguous or edge case to establish the expected reasoning pattern.

NEW QUESTION # 34
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