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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Tool Design & MCP Integration	18%	- Tool definition and best practices 1. Error handling and validation 2. Tool descriptions and selection logic - Model Context Protocol (MCP) 1. Tools, resources, and prompts integration 2. MCP server and client setup
Topic 2: Context Management & Responsible AI	15%	- Context window optimization 1. Context retention and summarization 2. Token management and truncation strategies - Safety and compliance 1. Refusal handling and risk mitigation 2. Constitutional AI principles
Topic 3: Agentic Architecture & Orchestration	27%	- Anthropic Agent SDK usage 1. Session state and context handling 2. Task spawning and tool management - Agent design patterns 1. Agent loops and control flow 2. Hub-and-spoke multi-agent systems
Topic 4: Claude Code Configuration & Workflows	20%	- CI/CD and development integration 1. Pipeline automation and review 2. Plan mode vs direct execution - Configuration files and structure 1. CLAUDE.md and rules system 2. Custom commands and workflows
Topic 5: 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

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q57-Q62):

NEW QUESTION # 57
You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
Your agent needs to insert a new helper function into the middle of a 150-line utility module, between two existing functions. The Edit tool fails because its old_string parameter cannot find unique text to match - the file has repetitive docstrings, variable names, and structural patterns.
What's the most reliable way to complete this insertion?

A. Use Edit with an extremely long old_string capturing 30+ lines of context to guarantee uniqueness
B. Use Read to load the file, add the function at the appropriate location, then Write the updated file
C. Use Bash to append the function definition to the end of the file using heredoc syntax
D. Use Edit's replace_all parameter to target a common pattern and embed the new function in the replacement text

Answer: B

Explanation:
When Edit cannot identify a unique match, rewriting the fully read file is the most reliable way to control the exact insertion point. The other options are brittle, place the function incorrectly, or risk unintended replacements.

NEW QUESTION # 58
To maintain a clean and modular configuration, you want to split a monolithic CLAUDE.md file into distinct, topic-specific rule files. How can you include these external rule files into your main project configuration?

A. Option C
B. Option A
C. Option B
D. Option D

Answer: B

Explanation:
The @import syntax is the standard mechanism in Claude Code to reference external markdown files. It allows teams to keep their project-level CLAUDE.md modular by importing specific standards files logically.

NEW QUESTION # 59
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. Create a rule file in . claude/rules/ with YAML frontmatter specifying paths: . test.tsx"].
C. Place a separate CLAUDE .md file in every subdirectory containing test files.
D. Define an MCP server resource to fetch testing standards dynamically.

Answer: B

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 # 60
Which of the following are valid iterative refinement techniques used to achieve progressive improvement in output quality? Choose 2 correct answers.

A. Relying on the tool_choice: "any" parameter to automatically fix failed code logic.
B. Using the Interview Pattern to have Claude surface design considerations before implementing.
C. Executing same-session self-reviews to retain reasoning context and confirmation bias.
D. Providing specific test cases with example input and expected output to correct edge-case handling failures.

Answer: B,D

Explanation:
Valid iterative refinement techniques include providing concrete examples/test cases for edge case correction, and using the Interview Pattern to refine requirements beforehand. Same-session self-review is an anti-pattern, and tool_choice does not magically fix code logic.

NEW QUESTION # 61
In a multi-pass review architecture, how can you effectively automate the routing of identified findings for human review calibration?

A. Use the Message Batches API to delay findings until the end-of-week manual review.
B. Escalate any finding that flags more than 5 lines of code.
C. Rely strictly on the model's self-reported conversational confidence score in the output.
D. Run verification passes where the model outputs field-level confidence scores alongside findings to enable calibrated review routing.

Answer: D

Explanation:
To optimize human-in-the-loop workflows, models should output structured, field-level confidence scores that are calibrated against labeled validation sets. This allows reliable routing of low-confidence or high-risk findings for human review.

NEW QUESTION # 62
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