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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. 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
Topic 2: Claude Code Configuration & Workflows	20%	- CI/CD and workflow integration 1. Command vs plan mode usage 2. Automated pipeline integration - Claude Code configuration 1. CLAUDE.md hierarchy and scoping 2. Project-level vs user-level configuration
Topic 3: 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)
Topic 4: Context Management & Reliability	15%	- Context window optimization 1. "Lost in the middle" mitigation strategies 2. Managing long conversation degradation - System reliability 1. Escalation strategies (fail vs retry vs human) 2. Error propagation handling
Topic 5: Agentic Architecture & Orchestration	27%	- Agentic loops & lifecycle design 1. Tool use loop execution (request → tool_use → result → next step) 2. Stop reason handling and control flow - Multi-agent orchestration 1. Parallel task decomposition 2. Coordinator / sub-agent patterns

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q19-Q24):

NEW QUESTION # 19
While configuring prompt engineering standards for a production application, you must decide when to implement few-shot examples. Which of the following scenarios represent the most effective use cases for few-shot prompting? Choose 2 correct answers.

A. Tasks requiring a custom, non-standard output format that Claude does not handle natively.
B. Simple, well-defined data extraction tasks with clear objective criteria.
C. Ambiguous classification tasks where the boundaries between categories are fuzzy or subjective.
D. Tasks requiring standard output formats like valid JSON, where tool_use is already implemented.

Answer: A,C

Explanation:
Few-shot prompting is most valuable when the task has ambiguous boundaries (like detecting sarcasm) or requires custom output formats that aren't standard. It is generally unnecessary for simple, well-defined tasks or standard formats like JSON which are better handled natively or via tool_use.

NEW QUESTION # 20
Why is it considered an architectural anti-pattern to configure the exact same Claude session to review its newly generated code?

A. It exceeds the maximum token limit of the context window during the generation phase.
B. The Message Batches API does not support synchronous self-review workflows.
C. The reviewer retains the reasoning context and assumptions from the generation phase, creating confirmation bias.
D. Tools invoked during the first pass are permanently locked and cannot be reused in the review pass.

Answer: C

Explanation:
Same-session self-review is an anti-pattern because the model retains the context and reasoning from the generation phase, leading to a blind spot or confirmation bias. Independent review instances with fresh contexts are much more effective at objectively evaluating code.

NEW QUESTION # 21
A company wants Claude to summarize thousands of support tickets efficiently. Which design scales BEST?

A. Increase temperature.
B. Process tickets individually and combine summaries.
C. Send all tickets in one request.
D. Remove system prompts.

Answer: B

Explanation:
Hierarchical summarization is an effective strategy for large datasets. Claude summarizes smaller batches first, then combines intermediate summaries into higher-level reports, improving scalability while maintaining important information.

NEW QUESTION # 22
Your update_user_profile tool accepts a user_id (required) and an optional fields_to_update object. In testing, Claude frequently omits user_id or passes incorrectly structured data. What is most critical for helping Claude understand what parameter values to provide?

A. Clear parameter descriptions explaining expected format, such as "user_id: UUID of the user to update (required)"
B. Verbose parameter names encoding format hints, such as user_id_string_uuid_format
C. Detailed error responses explaining why invalid parameter values were rejected
D. Strict JSON Schema type constraints marking user_id as required and defining fields_to_update as an object type

Answer: D

Explanation:
Enforcing required fields and type constraints through a JSON Schema gives the agent clear rules about what parameters must be provided and their expected structure, preventing omissions or incorrectly formatted data.

NEW QUESTION # 23
A pull request modifies 14 files across a repository. A single-pass automated review produces inconsistent results, catching deep bugs in some files while missing obvious ones in others. How should the review architecture be redesigned to fix this?

A. Switch to a higher-tier model with a larger context window to process all files simultaneously.
B. Run three independent reviews on the full PR and only flag issues that reach consensus.
C. Split the review into focused passes: analyze each file individually for local issues, followed by a separate integration pass for cross-file data flow.
D. Require developers to manually split the PR into smaller submissions of 3-4 files.

Answer: C

Explanation:
Splitting large code reviews into per-file local analysis passes (to catch syntax/local logic bugs) plus a separate cross-file integration pass prevents attention dilution. Relying solely on larger context windows or requiring consensus across full monolithic runs are anti-patterns.

NEW QUESTION # 24
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