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Anthropic CCA-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q18-Q23):

NEW QUESTION # 18
After executing a requested tool in an agentic loop, how must the application handle the follow-up request to ensure the model can accurately reason about the next action?

A. Append a user message containing a tool_result block with the matching tool_use_ld, and include the full prior conversation history.
B. Pass only the new tool result to save token space in the context window.
C. Pass the tool result inside an assistant message equipped with an is_error flag.
D. Use the /compact command to summarize the tool output before returning it to the loop.

Answer: A

Explanation:
Because Claude is stateless, the application must provide the entire conversation history. When returning a tool result, it must be appended as a user message containing a tool_result block that explicitly includes the specific tool_use_id matching the original request.

NEW QUESTION # 19
When orchestrating complex workflows using the Claude Agent SDK, in which scenario is dynamic adaptive decomposition preferred over fixed sequential prompt chaining?

A. When performing predictable, multi-aspect code reviews where the sequence of analysis is entirely static.
B. When validating a strictly structured JSON extraction against predefined business rules.
C. When executing a daily technical debt audit across a predefined list of target directories.
D. When executing open-ended investigation tasks where intermediate discoveries dictate the next required steps.

Answer: D

Explanation:
Dynamic adaptive decomposition is the optimal strategy for unpredictable, open-ended tasks (like investigations) where the agent must decide its next steps based on intermediate findings. Prompt chaining (fixed sequential pipelines) is better suited for predictable, linear workflows with static sequences.

NEW QUESTION # 20
After the web search agent finds 25 sources (120K tokens of raw content), the document analysis agent extracts key insights (15K tokens), and the synthesis agent produces a coherent narrative draft (3K tokens), the coordinator must pass context to the report generation agent for the final output with proper source citations. What context-passing strategy provides the best balance of completeness and efficiency?

A. Pass a condensed summary of all prior stages that preserves the main findings and attributes them to sources by name only.
B. Pass the synthesis draft along with a structured source index that maps key claims to their source URLs and relevant excerpts.
C. Pass the full accumulated context from all prior agents.
D. Pass only the synthesis draft and have a separate post-processing pipeline match claims to sources and insert citations after the report is generated.

Answer: B

Explanation:
Passing the synthesis draft with a structured source index gives the report generation agent enough information to produce a complete final report while avoiding the cost and noise of full raw context. Mapping key claims to source URLs and relevant excerpts preserves attribution, supports accurate citations, and keeps the final generation step efficient.

NEW QUESTION # 21
A CI pipeline uses Claude to generate missing test cases for new pull requests. However, it frequently suggests test scenarios that are already covered elsewhere. How should this be resolved?

A. Instruct the model to only generate tests for functions with zero coverage.
B. Set the model's temperature to 0.0 to ensure deterministic test generation.
C. Use a PostToolUse hook to deduplicate the generated tests against the existing codebase.
D. Provide existing test files in the context so the model can avoid suggesting duplicate scenarios.

Answer: D

Explanation:
To prevent Claude from generating duplicate or overlapping tests, you must provide the existing test files in the context window. This ensures test generation adds net-new value and accurately supplements the current test suite.

NEW QUESTION # 22
A developer wants Claude to consistently answer as an experienced cybersecurity analyst regardless of the user question. Which prompt component should contain this instruction?

A. System prompt
B. Assistant response
C. Retrieved document
D. User message

Answer: A

Explanation:
The system prompt establishes persistent behavioral instructions that apply throughout the conversation. Role definition, response style, and organizational policies belong there because Claude prioritizes system instructions above ordinary user requests whenever possible.

NEW QUESTION # 23
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