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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
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
Context Management & Responsible AI	15%	- Context window optimization 1. Context retention and summarization 2. Token management and truncation strategies - Safety and compliance 1. Constitutional AI principles 2. Refusal handling and risk mitigation
Agentic Architecture & Orchestration	27%	- Anthropic Agent SDK usage 1. Session state and context handling 2. Task spawning and tool management - Agent design patterns 1. Agent loops and control flow 2. Hub-and-spoke multi-agent systems
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. 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

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q32-Q37):

NEW QUESTION # 32
A customer support agent built with the Claude Agent SDK needs to correctly identify when to resolve a case autonomously versus when to trigger human escalation. Which of the following represents an architectural anti-pattern for triggering escalation?

A. Escalating based on negative customer sentiment or self-reported low model confidence scores.
B. Escalating when a deterministic programmatic hook blocks a business operation that exceeds the agent's permission limits.
C. Escalating immediately when the customer explicitly demands to speak to a human representative.
D. Escalating when the agent detects a policy gap not covered by its existing knowledge base.

Answer: A

Explanation:
Using sentiment analysis or the LLM's self-reported confidence score for escalation is a heavily tested anti-pattern. Sentiment does not correlate with task complexity (e.g., an angry customer may just need a simple password reset), and model confidence scores are poorly calibrated for routing decisions.

NEW QUESTION # 33
In a long-running codebase debugging session, the agent makes important intermediate discoveries but starts forgetting earlier constraints as the context window fills. How can you ensure these intermediate discoveries survive context compression boundaries?

A. Use the - -resume flag to append the new context to the old session.
B. Apply the 'lost in the middle' effect by placing the discoveries in the exact center of the prompt.
C. Declare global variables in the system prompt to hold state.
D. Persist critical intermediate state to extemal scratchpad files, which survive session boundaries and context resets.

Answer: D

Explanation:
Scratchpad files are used to persist important intermediate state to external files. Because they are external, they survive context compression (like the /compact command) and allow the agent to re-read its progress when needed.

NEW QUESTION # 34
During the final synthesis phase, the coordinator receives conflicting statistical data from two different subagents (e.g., verified database vs. outdated PDF). What is the correct architectural pattern for handling this conflict?

A. Maintain structured claim-source mappings to preserve information provenance (source, confidence, timestamp) for informed resolution.
B. Silently average the conflicting numerical values to provide a balanced estimate.
C. Halt the synthesis process and prompt the user to manually input the correct statistic.
D. Arbitrarily select the value from the most recently updated source to resolve the conflict.

Answer: A

Explanation:
When subagents provide conflicting data, the system must track information provenance (source, confidence level, timestamp) through structured claim-source mappings. This metadata enables informed conflict resolution and audit trails. Arbitrary selection or silently averaging data are architectural anti-patterns.

NEW QUESTION # 35
A developer wants Claude to explain every reasoning step internally before answering. Why might this request be inappropriate?

A. Claude requires GPU acceleration.
B. Internal reasoning should not be relied upon as an application feature.
C. Temperature prevents explanations.
D. JSON cannot contain reasoning.

Answer: B

Explanation:
Applications should evaluate Claude based on observable outputs rather than expecting access to internal reasoning processes. Prompting should request useful explanations or evidence instead of relying on hidden reasoning mechanisms.

NEW QUESTION # 36
A quicktechie.com team wants to run a nightly code quality audit across the entire codebase using Claude Code to reduce API costs.
Which approach is most cost-effective and architecturally sound?

A. Run multiple Claude Code instances in parallel.
B. Run claude -p synchronously for each file in a loop.
C. Schedule a single long-running Claude Code session overnight.
D. Use the Message Batches API for non-urgent batch processing to achieve 50 percent cost savings.

Answer: D

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

NEW QUESTION # 37
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