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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Agentic Architecture & Orchestration	27%	- Agentic architecture patterns 1. Agent orchestration 2. Planning and execution strategies 3. Single-agent and multi-agent architectures 4. Workflow design
Topic 2: Claude Code Configuration & Workflows	20%	- Claude Code 1. Code generation and automation 2. Agent skills 3. Development workflows 4. Configuration and project setup
Topic 3: Prompt Engineering & Structured Output	20%	- Prompt design 1. Prompt engineering techniques 2. Few-shot prompting 3. Structured output and JSON schemas 4. Output validation
Topic 4: Tool Design & MCP Integration	18%	- Tool integration 1. Model Context Protocol (MCP) 2. Tool selection and safety 3. Resource and server integration 4. Tool interface design
Topic 5: Context Management & Reliability	15%	- Context handling 1. Memory strategies 2. Reliability and evaluation 3. Cost and performance optimization 4. Context window management

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q128-Q133):

NEW QUESTION # 128
Production logs show that when the agent handles complex billing disputes requiring 6+ tool calls, it sometimes exhausts its max_turns limit after gathering data but before completing resolution or escalating. The team's goal is to guarantee that every customer interaction ends with either a completed resolution or a human handoff, regardless of how the agent loop terminates. Which approach achieves this guarantee?

A. Implement a pre-tool-use hook that counts tool invocations and terminates the loop with an automatic escalation once the agent reaches 80% of its max_turns limit.
B. Add system prompt instructions telling the agent to call escalate_to_human with a summary of its findings whenever it determines it cannot complete resolution within its remaining actions.
C. Split the workflow into two sequential agent invocations - a first agent gathers information via get_customer and lookup_order, then a second agent receives that data and handles process_refund or escalate_to_human, each with separate turn budgets.
D. Add orchestration-layer code that checks the agent's outcome after each loop termination - if the loop ended without a completed resolution or escalation, programmatically call escalate_to_human with the accumulated conversation context and tool results.

Answer: D

Explanation:
A system prompt cannot guarantee compliance, and turn-count hooks may escalate prematurely.
An orchestration-level fallback runs regardless of why the agent loop stopped, ensuring every interaction ends in either resolution or human escalation.

NEW QUESTION # 129
Your infrastructure-as-code repository includes Terraform modules (/terraform/), Kubernetes manifests (/kubernetes/), and CI/CD pipeline scripts (/pipelines/). Each requires different conventions, but your single root CLAUDE.md has grown to 500+ lines. When developers work on Kubernetes files, Terraform-specific rules load into context unnecessarily, consuming tokens.
What is the best approach to reorganize so only relevant guidance loads when editing specific file types?

A. Create files in .claude/rules/ with YAML frontmatter path-scoping (e.g., paths: ["terraform/**/*"]), loading rules only when editing matching files.
B. Keep the root CLAUDE.md and use @path/to/import syntax to modularly include tool-specific guidance files from separate documents.
C. Split content into subdirectory CLAUDE.md files (/terraform/CLAUDE.md,
/kubernetes/CLAUDE.md), so Claude loads directory-specific guidance.
D. Restructure the root CLAUDE.md into clearly labeled sections with headers (e.g, "## Terraform Conventions"), improving organization and readability.

Answer: A

Explanation:
The paths field conditionally loads each rule only when Claude works with matching files, preventing unrelated Terraform, Kubernetes, or pipeline guidance from consuming context.
Subdirectory CLAUDE.md files are directory-based, while path-scoped rules provide precise file- pattern targeting.

NEW QUESTION # 130
Which statement BEST describes Retrieval-Augmented Generation?

A. It changes token pricing.
B. It supplies relevant external knowledge during inference.
C. It compresses model weights.
D. It permanently retrains Claude.

Answer: B

Explanation:
RAG retrieves relevant documents at inference time and includes them within the prompt. The model itself is unchanged, allowing organizations to incorporate current enterprise knowledge without retraining foundation models.

NEW QUESTION # 131
You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
Your team has three requirements for Claude Code's behavior in your project:
* Claude must never modify files in the db/migrations/ directory.
* Claude should prefer your custom logging module over console.log .
* All TypeScript files must be auto-formatted with Prettier after every edit.
All three are currently written as instructions in your project's CLAUDE.md. During a complex refactoring session, a developer discovers that Claude edited a migration file, violating requirement #1.
How should you restructure these requirements across Claude Code's configuration mechanisms?

A. Rewrite all three requirements in CLAUDE.md using stronger directive language and add few-shot examples that demonstrate Claude refusing to edit migration files and running Prettier after edits.
B. Move all three requirements into .claude/rules/ as path-scoped rules: one targeting db/migrations/** that forbids editing those files, and others targeting **/*.ts for the logging convention and formatting instruction.
C. Configure hooks for all three: a PreToolUse hook script that blocks Edit calls targeting db/migrations/ , a PreToolUse hook script that adds logging convention context before edits, and a PostToolUse hook that runs Prettier after TypeScript edits.
D. Add Edit(./db/migrations/**) to permissions.deny in the project settings, keep the logging preference in CLAUDE.md, and add a PostToolUse hook to run Prettier after TypeScript edits.

Answer: D

Explanation:
Each requirement belongs in the mechanism matching its enforcement semantics. The migration restriction is a hard safety boundary, so it should be implemented through permissions.deny , not merely expressed as contextual guidance. Anthropic explicitly states that CLAUDE.md content is treated as context rather than enforced configuration. Permission rules use the Tool(specifier) form, evaluate deny rules before ask or allow rules, and support path-based Edit(...) restrictions. ( https://code.claude.com/docs/en/memory ) The custom logging convention is a persistent project preference, making CLAUDE.md the appropriate location. It should guide Claude's code-generation decisions but does not require deterministic interception.
Prettier formatting, by contrast, is deterministic automation. Anthropic documents the exact pattern of using a PostToolUse hook with an Edit|Write matcher to run Prettier automatically after file modifications. (
https://code.claude.com/docs/en/hooks-guide )
Option A incorrectly treats path-scoped rules as a security control; rules remain instructions. Option B overuses hooks for a semantic coding preference that belongs in CLAUDE.md. Option C relies entirely on stronger prompting, which cannot guarantee that protected files remain untouched. Option D correctly separates prohibition, preference, and automatic enforcement.
Official references/topics: Permission rules, CLAUDE.md semantics, PostToolUse formatting hooks.

NEW QUESTION # 132
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.
An engineer asks your agent to identify untested code paths in a legacy payment processing module spanning
45 files. After reading the first 8 source files, the agent's responses are becoming noticeably less accurate-it' s forgetting previously discussed code patterns and hasn't yet located all test files or traced critical payment flows.
What's the most effective approach to complete this investigation?

A. Document all current findings in a summary report, clear context completely, then use that report as the sole reference for continuing the investigation.
B. Clear context with /clear , then selectively re-read only the most critical files discovered so far, writing key findings to a scratchpad file that persists between context resets.
C. Spawn subagents to investigate specific questions (e.g., "find all test files for payment processing,"
"trace refund flow dependencies") while the main agent coordinates findings and preserves high-level understanding.
D. Switch to using Grep to search for specific function names instead of reading full files, reducing the content loaded into context for remaining exploration.

Answer: C

Explanation:
The investigation contains several bounded research questions that can be delegated independently: locating the complete test suite, tracing payment and refund flows, identifying conditional branches, and mapping external dependencies. Each subagent can read the relevant files in its own context and return a focused summary to the coordinating agent.
Anthropic recommends subagents for codebase exploration because extensive file reading rapidly consumes the main context window. Subagents isolate that volume and return only their conclusions, preserving the main conversation for synthesis and implementation. ( https://docs.anthropic.com/en/docs/claude-code
/common-workflows ) Anthropic also describes parallel research as appropriate when separate investigation paths can proceed independently and the main agent can synthesize the results afterward. ( https://docs.
anthropic.com/en/docs/claude-code/sub-agents )
Option B sacrifices the current conversational state and requires reconstruction after /clear . Option C may reduce token usage, but isolated text matches cannot reliably reveal full execution paths, indirect calls, or test coverage relationships. Option D converts the current analysis into a single lossy summary and risks omitting details needed later.
Option A directly addresses the demonstrated context degradation while retaining a high-level coordinating thread. The subagent prompts should be narrowly scoped and require concrete outputs such as file paths, uncovered branches, call-chain evidence, and existing tests associated with each flow.
Official references/topics: Subagent Context Isolation; Parallel Research; Context Preservation; Coordinated Codebase Analysis.

NEW QUESTION # 133
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