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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. JSON schema enforcement 2. Output validation and reliability
Topic 2: 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
Topic 3: Tool Design & MCP Integration	18%	- Model Context Protocol (MCP) 1. Tools, resources, and prompts integration 2. MCP server and client setup - Tool definition and best practices 1. Tool descriptions and selection logic 2. Error handling and validation
Topic 4: 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 5: 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

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q63-Q68):

NEW QUESTION # 63
When implementing a structured error response for an MCP tool, which property explicitly indicates to the agent whether it should attempt the exact same tool call again?

A. isError
B. tool_choice
C. isRetryable
D. errorCategory

Answer: C

Explanation:
The isRetryable boolean explicitly tells the agent whether retrying the same call might succeed (e.g., true for a transient timeout, false for a strict authorization failure or missing API key).

NEW QUESTION # 64
During a multi-agent synthesis phase, the coordinator receives conflicting financial statistics from a web search subagent and a document analysis subagent. What is the required architectural pattern for handling this conflict?

A. Maintain structured claim-source mappings to preserve information provenance (source, confidence, timestamp) allowing for an informed resolution.
B. Average the two conflicting values together to provide a balanced estimate.
C. Discard both values and prompt the user to manually input the correct statistic.
D. Arbitrarily select the value from the document subagent, as internal documents are generally more reliable.

Answer: A

Explanation:
When subagents provide conflicting data, the system must track information provenance (source, confidence level, timestamp) through claim-source mappings. This metadata enables informed conflict resolution and audit trails. Arbitrary selection or averaging without tracking provenance are anti-patterns.

NEW QUESTION # 65
During code generation, Claude introduces several bugs. Two bugs are highly coupled, interacting with the same state object, while a third bug is a minor typo in an unrelated utility file. What is the recommended strategy for addressing these issues through iterative refinement?

A. Fix the independent typo first, run the /compact command, and then address the interacting bugs.
B. Submit all three issues in a single prompt to save tokens.
C. Use a PostToolUse hook to programmatically decouple the bugs before sending them to Claude.
D. Fix the interacting problems in a single detailed message, and use sequential iteration to fix the independent issue.

Answer: D

Explanation:
When dealing with multiple issues during iterative refinement, interacting problems (coupled bugs) should be addressed in a single detailed message so the model can resolve the conflicts holistically. Independent problems (like an unrelated typo) are better addressed through sequential iteration.

NEW QUESTION # 66
What is a primary architectural limitation of using a fixed prompt chaining pipeline for a complex debugging task?

A. It cannot dynamically adapt its approach if an intermediate analysis step reveals an unexpected root cause.
B. It inherently exceeds the maximum token limit of the context window faster than single prompts.
C. It requires the use of the Message Batches API, adding a 24-hour latency to the execution.
D. It forces the agent to use tool_choice: any, bypassing deterministic programmatic hooks.

Answer: A

Explanation:
Fixed sequential pipelines (prompt chaining) cannot adapt to new information. If step 2 of a debugging pipeline discovers the issue is actually in the database rather than the frontend, a static chain cannot dynamically alter its remaining steps, making it unsuitable for unpredictable tasks.

NEW QUESTION # 67
Claude produces occasional factual inaccuracies despite clear prompts. Which architectural enhancement is MOST effective?

A. Remove examples.
B. Increase temperature.
C. Add grounding through trusted retrieval.
D. Shorten responses only.

Answer: C

Explanation:
Grounding responses with trusted enterprise documents helps Claude generate answers supported by authoritative sources. Retrieval significantly reduces unsupported statements while improving factual consistency across knowledge-intensive applications.

NEW QUESTION # 68
......

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