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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Prompt Engineering & Structured Output	20%	- Tool-augmented prompting 1. Tool-use driven reasoning patterns 2. Validation and retry loops - Instruction design 1. Few-shot prompting strategies 2. Deterministic output formatting (e.g., JSON)
Agentic Architecture & Orchestration	27%	- Multi-agent orchestration 1. Coordinator / sub-agent patterns 2. Parallel task decomposition - Agentic loops & lifecycle design 1. Tool use loop execution (request → tool_use → result → next step) 2. Stop reason handling and control flow
Claude Code Configuration & Workflows	20%	- Claude Code configuration 1. Project-level vs user-level configuration 2. CLAUDE.md hierarchy and scoping - CI/CD and workflow integration 1. Command vs plan mode usage 2. Automated pipeline integration
Context Management & Reliability	15%	- System reliability 1. Escalation strategies (fail vs retry vs human) 2. Error propagation handling - Context window optimization 1. "Lost in the middle" mitigation strategies 2. Managing long conversation degradation
Tool Design & MCP Integration	18%	- Model Context Protocol (MCP) 1. MCP server integration patterns 2. Tool selection and routing strategies - Tool interface design 1. Clear tool descriptions and boundaries 2. Structured tool input/output schemas

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q88-Q93):

NEW QUESTION # 88
When applying few-shot prompting to improve extraction quality for ambiguous scenarios, what is the optimal number of examples to provide?

A. 1 example to establish the JSON format.
B. 8 to 10 examples to cover all possible variations.
C. 2 to 4 examples, covering standard and edge cases.
D. 15 or more examples to thoroughly fine-tune the model's reasoning.

Answer: C

Explanation:
The golden rule of few-shot prompting is to provide 2-4 examples. Fewer than 2 does not establish a pattern, while providing more than 6 examples bloats the prompt without adding proportional benefit.

NEW QUESTION # 89
Your customer support agent needs a strict limit preventing it from processing refunds exceeding $500. What is the most deterministic architectural method to enforce this?

A. Add a capitalized warning in the system prompt: 'NEVER process a refund exceeding $500.'
B. Implement a PostToolUse hook to intercept the process_refund tool call and block it programmatically if the amount exceeds 500.
C. Provide several few-shot examples in the prompt showing the agent successfully refusing high-value refunds.
D. Set the tool_choice parameter to 'auto' so the model can dynamically reject the action.

Answer: B

Explanation:
Critical business rules requiring deterministic enforcement must use programmatic hooks (like PreToolUse or PostToolUse). Relying on prompt instructions or few-shot examples is probabilistic and leaves a non-zero chance that the LLM will ignore the rule.

NEW QUESTION # 90
In a coordinator-subagent research system, a web search subagent times out while attempting to retrieve information on a complex topic. How should this failure be propagated back to the coordinator agent?

A. The subagent should catch the timeout and return an empty JSON array L] marked as successful so the workflow continues smoothly.
B. Return structured error context to the coordinator, including the failure type, the attempted query, any partial results, and potential alternative approaches.
C. Implement automatic retry logic indefinitely within the subagent, returning a generic search unavailable status if it ultimately fails.
D. Propagate the timeout exception directly to a top-level handler to immediately terminate the entire research workflow.

Answer: B

Explanation:
Structured error propagation is critical. Returning structured error context (failure type, what was attempted, partial results) enables the coordinator to make an intelligent recovery decision. Silently suppressing the error by returning an empty result is a severe anti-pattern, and returning generic messages hides context needed for recovery.

NEW QUESTION # 91
A coordinator agent needs to delegate a broad research topic. How can the coordinator efficiently spawn multiple parallel subagents instead of executing them sequentially?

A. By emitting multiple Task tool calls within a single response turn.
B. By defining a PostToolUse hook that duplicates the prompt to multiple endpoints.
C. By invoking the parallel_execute method provided natively in the Agent SDK.
D. By using the fork_session command for each subagent in separate turns.

Answer: A

Explanation:
Parallel execution in a multi-agent system is achieved when the coordinator agent emits multiple Task tool calls in a single response turn. This allows the system to process the corresponding subagents simultaneously rather than across separate sequential turns.

NEW QUESTION # 92
Your codebase has test files spread throughout multiple directories alongside the components they test (e.g., Button. test .tsx next to Button. tsx). How can you ensure Claude automatically applies specific testing conventions whenever it generates or edits a test file?

A. Consolidate all testing conventions in the root CLAUDE .md file and rely on Claude's inference.
B. Define an MCP server resource to fetch testing standards dynamically.
C. Place a separate CLAUDE .md file in every subdirectory containing test files.
D. Create a rule file in . claude/rules/ with YAML frontmatter specifying paths: . test.tsx"].

Answer: D

Explanation:
Using . claude/rules/ files with glob patterns in the paths field allows conventions to be automatically and conditionally applied based on file paths. This ensures the testing rules load deterministically whenever matching test files are generated or edited.

NEW QUESTION # 93
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