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Anthropic CCA-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q10-Q15):

NEW QUESTION # 10
Which of the following strategies are explicitly recommended for designing multi-instance and multi-pass review architectures? Choose 2 correct answers.

A. Having the same session review its newly generated code to preserve maximum architectural context.
B. Forcing consensus across 3 monolithic runs to suppress false positives in large multi-file PRs.
C. Using an independent Claude instance to review generated code without the generator's reasoning context.
D. Splitting multi-file reviews into focused per-file passes for local issues plus a separate cross-file integration pass.

Answer: C,D

Explanation:
Independent review instances without prior reasoning context effectively catch subtle issues without confirmation bias. Splitting reviews into focused per-file passes and integration passes addresses attention dilution. Same-session reviews and monolithic consensus runs are anti-patterns.

NEW QUESTION # 11
A financial services company plans to integrate Claude into an internal document analysis platform. The architects want to minimize exposure of confidential client information while maintaining high-quality responses. Which approach BEST aligns with Anthropic's recommended architecture?

A. Remove unnecessary sensitive information before sending prompts whenever possible.
B. Store every prompt permanently for auditing.
C. Send every document without preprocessing to maximize context.
D. Replace Claude with a smaller local model regardless of task complexity.

Answer: A

Explanation:
Minimizing unnecessary sensitive information before sending prompts follows the principle of data minimization. Claude performs best when provided only with relevant context. Removing unrelated confidential information reduces privacy risk while preserving model performance and supporting secure enterprise AI architecture.

NEW QUESTION # 12
An automated extraction pipeline reports 96% aggregate accuracy, but downstream users complain about frequent errors on complex contracts. What monitoring methodology resolves this visibility gap?

A. Increasing the overall sampling size randomly to capture a broader dataset.
B. Tracking accuracy by specific document type and field (stratified metrics).
C. Switching entirely to the Message Batches API for asynchronous processing.
D. Requiring a 100% human-in-the-loop review for all documents.

Answer: B

Explanation:
Aggregate accuracy metrics can mask severe per-document-type failures (e.g., contracts failing frequently while simple receipts succeed nearly 100% of the time). Tracking accuracy using stratified metrics (per document type and field) reveals these hidden failures.

NEW QUESTION # 13
When utilizing Claude Code's built-in tools for codebase exploration and modification, which of the following usages represent correct tool selections? Choose 2 correct answers.

A. Using Write to fix a minor typo in an existing, heavily documented configuration file.
B. Using Bash to execute find . "-name * .ts" to locate all TypeScript files.
C. Using Glob to find all file paths that end in .test.tsx within the project.
D. Using Grep to search for all occurrences of a deprecated function name across the src/ directory.

Answer: C,D

Explanation:
Grep is the correct tool for searching file contents for text patterns, and Glob is the correct tool for matching file naming patterns. Using Write to fix a typo in an existing file is wrong (Edit should be used), and using Bash to run 'find' is an anti-pattern because the native Glob tool should be used instead.

NEW QUESTION # 14
The agent verifies customer identity through a multi-step process before resetting passwords.
During testing, you notice that after the customer answers the third verification question, the agent asks them to provide their name again, as if the earlier exchange never happened. What's the most likely cause of this behavior?

A. The verification tool is clearing the agent's internal state after each successful validation step.
B. The prompt lacks instructions telling Claude to remember information across multiple exchanges.
C. Claude's memory retention is limited to two conversational turns by default, requiring explicit configuration to extend it.
D. The conversation history isn't being passed in subsequent API requests.

Answer: D

Explanation:
Claude relies on the conversation history included in each API request to maintain continuity across turns. If earlier messages are not passed forward, the agent will not have access to previously provided details and may ask for information again as though the exchange never occurred.

NEW QUESTION # 15
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