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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Prompt Engineering & Structured Output	20%	- System prompt design and persona alignment - JSON schema design and structured output enforcement - Explicit criteria definition and few-shot prompting - Validation, parsing and retry loop strategies
Context Management & Reliability	15%	- Context window optimization and prioritization - Idempotency, consistency and failure resilience - Context pruning and summarization strategies - Token budget management and cost control
Agentic Architecture & Orchestration	27%	- Multi-agent patterns: coordinator-subagent and hub-and-spoke - Task decomposition and dynamic subagent selection - Error recovery, guardrails and safety patterns - Agentic loop design and stop_reason handling - Session state management and workflow enforcement
Claude Code Configuration & Workflows	20%	- CI/CD integration and non-interactive mode parameters - Custom slash commands and plan mode vs direct execution - CLAUDE.md hierarchy, precedence and @import rules - Hooks vs advisory instructions - Path-specific rules and .claude/rules/ configuration
Tool Design & MCP Integration	18%	- Error handling and tool response formatting - Tool schema design and interface boundaries - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool distribution and permission controls - MCP tool, resource and prompt implementation

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Free PDF Anthropic - CCAR-F –Efficient Reliable Practice Questions

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q88-Q93):

NEW QUESTION # 88
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.
A security audit requires updating your authentication library from v2 to v3. The migration guide documents breaking changes: authenticate() now returns a Promise instead of accepting a callback, the User type has restructured fields, and three deprecated methods were removed. Grep shows the library is imported in 45 files across several modules.
What's the most effective approach?

A. Update the dependency version, run the test suite, and use Claude Code to fix each failure as it appears.
B. Create a custom slash command encapsulating the migration transformations, then execute it against each file without prior codebase exploration.
C. Enter plan mode to explore library usage across modules, map affected code paths, then create a migration strategy before implementing.
D. Paste the migration guide's breaking changes into your prompt and use direct execution to update all usages across the 45 files.

Answer: C

NEW QUESTION # 89
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 sees the unfamiliar error message SYNC_CONFLICT: entity version mismatch detected in production logs but does not know which of the 12 services in the codebase generates it. The engineer asks the agent to locate the responsible source code.
What exploration approach will find the responsible code most efficiently?

A. Use Grep to locate every file importing the project's error-handling module, and then read those files to find custom error definitions.
B. Use Glob to find files in directories commonly associated with error handling, such as errors , exceptions , or handlers , and then read every matching file.
C. Read the project's README and service-configuration files, and then systematically read source files in every service directory.
D. Use Grep to search for distinctive text from the error message, such as SYNC_CONFLICT or entity version mismatch , and then read the matching files to understand the context.

Answer: D

Explanation:
Option A begins with the strongest available discriminator: the exact production error text. Anthropic's tools reference specifies that Grep searches file contents and returns matching lines, enabling the agent to locate a literal error constant or message without loading the full repository. Searching both the stable code SYNC_CONFLICT and the distinctive phrase protects against cases where the identifier and displayed message are defined separately. The agent can then read only the matching files and follow the surrounding call path, error construction, and service ownership. Option B assumes that errors are stored in conventionally named directories, which may not hold in a twelve-service repository. Option C consumes substantial context before using the most direct evidence. Option D narrows the search to consumers of a known shared error module, but the message may be locally defined, generated from configuration, or wrapped by another component. Grep followed by targeted Read operations follows Anthropic's broader context-engineering principle of retrieving relevant source material just in time instead of placing an entire codebase into the active context.

NEW QUESTION # 90
A customer sends: "This is frustrating. I've explained my issue twice and nothing is being resolved. I want to talk to a real person NOW." The agent has not yet called any tools to investigate the customer's account.
What should the agent do?

A. Immediately call escalate_to_human with the conversation history.
B. First call get_customer and lookup_order to gather account context, and then escalate to a human agent.
C. Acknowledge the frustration and ask one targeted question to understand the specific issue before escalating.
D. Briefly explain what the agent can help with and offer to resolve the issue quickly, escalating only if the customer repeats the request.

Answer: A

Explanation:
Option C respects the customer's explicit request for human assistance. The agent should not attempt to retain the interaction, gather additional account information, or require the customer to repeat the request. Because the escalation decision has already been made by the customer, further autonomous investigation would create unnecessary delay and disregard clear human direction.
Anthropic's trustworthy-agent framework emphasizes maintaining meaningful human control over agent autonomy. Anthropic's customer-support implementation guidance also recommends defining interaction branches and measuring escalation efficiency as part of the system's success criteria.
Option A deliberately delays escalation despite an unambiguous request. Option B performs unnecessary tool calls and may expose or retrieve account information that is not required before transferring the conversation.
Option D similarly introduces another conversational barrier. Since the current exchange is short and contains no tool-generated evidence, passing the available conversation history gives the human agent the immediate context-the customer's frustration, repeated unsuccessful attempts, and explicit transfer request-without pretending that account investigation has occurred.

NEW QUESTION # 91
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.
You're implementing the escalation logic for when the agent should call escalate_to_human . Your team proposes four different approaches for triggering escalation.
Which approach will most reliably identify cases that genuinely require human intervention?

A. Implement sentiment analysis that monitors for frustration indicators (negative language, repeated questions, exclamation marks) and triggers escalation when the frustration score exceeds a configured threshold.
B. Build a rules engine that maps specific issue types, customer segments, and product categories to escalation decisions, removing the need for model judgment calls.
C. Instruct the agent to escalate when the customer requests a human, when the issue requires policy exceptions, or when the agent cannot make meaningful progress.
D. Configure the agent to escalate after three consecutive tool calls that fail to resolve the customer's stated issue, ensuring a reasonable attempt before involving a human.

Answer: C

Explanation:
Option B identifies escalation through direct operational criteria rather than indirect proxies. A customer's explicit request for a person is unambiguous. A required policy exception defines an authorization boundary.
An inability to make meaningful progress identifies a case where continued autonomous execution is no longer productive.
Anthropic describes agents as systems that act, observe results, adjust, and continue until the task is completed or human input is required. Its trustworthy-agent guidance also emphasizes that agents must recognize when to pause rather than pushing through uncertainty or decisions that only a human can settle. (
https://www.anthropic.com/research/trustworthy-agents )
Option A is too rigid for high-ambiguity support cases. Maintaining exhaustive mappings for every issue, product, and customer segment recreates the brittleness that agentic reasoning is intended to avoid. Option C treats repeated tool use as a substitute for semantic progress; one definitive authorization failure may require immediate escalation, while several legitimate diagnostic calls may not. Option D confuses emotional tone with operational necessity. A calm customer may require a policy exception, while a frustrated customer may still have a straightforward automated resolution.
The escalation criteria should be encoded in the system instructions and supported by evaluations covering explicit requests, authorization limits, unresolved ambiguity, repeated non-progress, and successful self- service cases.
Official references/topics: Human-control checkpoints, escalation criteria, authorization boundaries, progress-aware agents.

NEW QUESTION # 92
Your automated review generates many findings per pull request, but developer feedback shows that roughly half are dismissed as "not worth addressing." Analysis reveals that dismissed findings are often technically accurate but involve minor style preferences or patterns that are acceptable in your codebase. Before adding infrastructure complexity, what prompt-design change could most effectively reduce dismissals while maintaining the detection of genuine issues?

A. Implement a secondary classification model that filters Claude's findings according to predicted developer acceptance.
B. Add explicit criteria defining which issues to report, such as bugs and security defects, and which issues to skip, such as minor style preferences and accepted local patterns.
C. Ask Claude to rate every finding's confidence from 1 to 10 and include only findings rated 8 or higher.
D. Append instructions telling Claude to "only report findings you are highly confident are genuine problems."

Answer: B

Explanation:
The problem is an undefined relevance threshold. Claude is finding technically valid observations, but the prompt does not clearly distinguish actionable defects from acceptable conventions and low-value style preferences. Option A establishes explicit positive and negative reporting criteria, allowing the model to apply the team's actual definition of a useful finding during generation.
Anthropic's prompt-engineering guidance emphasizes clear, specific instructions and well-defined success criteria. The managed Code Review documentation similarly recommends defining skipped categories, generated paths, severity rules, and evidence requirements. Reporting "bugs affecting correctness or security" while excluding "formatting handled by CI and documented local conventions" is substantially more precise than asking the model to be generally conservative.

NEW QUESTION # 93
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