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Anthropic CCAR-F Exam Syllabus Topics:

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

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Free PDF Quiz Anthropic - CCAR-F - The Best Claude Certified Architect - Foundations Prepaway Dumps

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q117-Q122):

NEW QUESTION # 117
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, and Glob-and integrates with Model Context Protocol (MCP) servers.
An engineer who recently joined the team asks the agent to explain the authentication and authorization architecture before making security improvements. The codebase contains more than 800 files across multiple services.
What exploration strategy will most effectively build understanding while respecting context limits?

A. Launch parallel subagents to explore every service simultaneously, and then synthesize their findings into an architectural overview.
B. Use Grep to locate authentication entry points, read those files, and then follow imports and function calls incrementally to map the authentication flow.
C. Read all CLAUDE.md and README files first, and then ask the engineer to identify the 10-15 most important authentication files.
D. Read all files containing auth , login , permission , or token in their filenames or contents.

Answer: B

Explanation:
Option D builds an evidence-based architectural map without indiscriminately loading hundreds of files.
Authentication entry points may include route handlers, middleware registration, token-validation functions, session constructors, or identity-provider callbacks. After Grep identifies those anchors, targeted Read operations can establish their responsibilities, while imports, call sites, and configuration references reveal the downstream authorization flow. Anthropic's large-codebase guidance recommends scoping Claude to the portion of a repository touched by the task because unrelated instructions and file reads consume tokens and degrade performance. Its common workflows guidance similarly recommends beginning broadly and narrowing into specific components. Option A launches agents before the relevant service boundaries are known and may produce overlapping, inconsistent investigations. Option B is an uncontrolled lexical sweep that will include tests, documentation, unrelated tokens, and incidental terminology. Option C appropriately checks project guidance but incorrectly delegates technical file selection to a new engineer who may not know the architecture. Incremental dependency tracing maintains context efficiency while producing a verifiable end-to-end map grounded in actual code paths.

NEW QUESTION # 118
Production logs reveal inconsistent error handling: when lookup_order fails, the agent sometimes retries 5+ times (wasteful when the order ID doesn't exist), sometimes escalates immediately (premature for temporary network issues), and sometimes asks users for clarification (inappropriate when the issue is a backend permission error). Investigation shows your MCP tool returns uniform error responses: {"isError": true, "content": [{"type": "text", "text": "Operation failed"}]}. The agent cannot distinguish between error types. What's the most effective improvement?

A. Add few-shot examples to the system prompt demonstrating how to interpret error message patterns and select appropriate responses for each.
B. Implement retry logic with exponential backoff in your MCP server for all errors, returning to the agent only after retries are exhausted.
C. Enhance error responses with structured metadata: include errorCategory (transient/validation/permission), isRetryable boolean, and a description of what caused the failure.
D. Create an analyze_error MCP tool the agent calls after any failure to determine the error category and recommended action.

Answer: C

Explanation:
Structured error metadata gives the agent the information needed to choose the right recovery path. By distinguishing transient, validation, and permission failures, and by explicitly indicating whether retry is appropriate, the agent can avoid wasteful retries, premature escalation, and unnecessary user clarification.

NEW QUESTION # 119
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 code review assistant needs to analyze pull requests and provide feedback on three aspects: code style compliance, potential security issues, and documentation completeness.
Each aspect requires reading files, running analysis tools, and generating a report section. The review process follows the same three-step workflow for every PR. Which task decomposition pattern is most appropriate for this workflow?

A. Orchestrator-workers - have a central LLM analyze each PR to dynamically determine which checks are needed, then delegate to specialized worker LLMs for each identified subtask.
B. Routing - classify each PR by type (feature, bugfix, refactor) first, then route to different review prompts optimized for that category.
C. Prompt chaining - break the review into sequential steps where each aspect (style, security, documentation) is analyzed separately, with outputs combined in a final synthesis step.
D. Single comprehensive prompt - include all instructions in one prompt and let the model handle all three aspects simultaneously.

Answer: C

Explanation:
The review always follows the same predefined stages-style, security, and documentation-so each can be analyzed separately and then combined into a final report. Orchestrator-workers is better when subtasks must be determined dynamically.

NEW QUESTION # 120
You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high-ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer, lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
After expanding the agent's MCP tools with delivery-specific capabilities ( check_delivery_status , contact_driver , issue_credit , apply_promo_code , update_delivery_address , reschedule delivery ), the total tool count has grown from 4 to 10. Your evaluation suite shows tool selection accuracy has dropped from 88% to 71%. Log analysis reveals the majority of errors involve the agent selecting between semantically overlapping tools - calling issue_credit when process_refund was correct, and calling check_delivery_status when lookup_order already returns the needed data. Which approach structurally eliminates the semantic overlap identified in the logs as the error source?

A. Add few-shot examples to the system prompt demonstrating correct selection for each ambiguous tool pair, such as showing when issue_credit applies versus when process_refund is appropriate.
B. Split the tools across two sub-agents - a "financial resolution" agent with process_refund , issue_credit ,and apply_promo_code , and a "delivery operations" agent with the remaining delivery tools - with a coordinator routing between them.
C. Enable the tool search tool with defer_loading on the six new tools, keeping the original four always loaded, so the agent dynamically discovers specialized tools only when needed.
D. Consolidate semantically overlapping tools - merge issue_credit and process_refund into a single resolve_compensation tool with an action parameter, and fold check_delivery_status into lookup_order with an optional include_tracking flag.

Answer: D

Explanation:
Merging issue_credit and process_refund into one compensation tool, and folding delivery-status lookup into lookup_order, removes competing tools with overlapping purposes. This directly eliminates the ambiguity causing incorrect tool selection, rather than merely hiding tools, routing between agents, or adding examples.

NEW QUESTION # 121
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.
You're implementing a complex graph traversal algorithm with specific performance requirements and edge cases to handle (disconnected nodes, cycles, weighted edges). You want to structure your workflow for efficient iterative refinement with Claude. What approach will most effectively enable progressive improvement across multiple iterations?

A. Have Claude extensively research the algorithm and create a detailed implementation plan using extended thinking, then implement the complete solution based on that plan.
B. Provide Claude with a reference implementation from documentation, then ask it to rewrite the code to match your codebase style and add the required edge case handling, comparing outputs against the reference.
C. Write a test suite covering expected behavior, edge cases, and performance requirements before implementation. Ask Claude to write code that passes the tests, then iterate by sharing test failures with each refinement request.
D. Provide Claude with a detailed natural language specification of the algorithm, including all requirements and edge cases. Review each output manually and provide descriptive feedback on what behavior needs to change.

Answer: C

Explanation:
Tests provide an executable specification and objective feedback for each iteration. Claude can implement against the suite, run it, inspect failures, and progressively refine the algorithm until correctness and performance requirements are met. Anthropic recommends running tests and fixing failures as an iterative Claude Code workflow.

NEW QUESTION # 122
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