CCAR-F考試指南 & CCAR-F最新考古題

沒有人除外，我們VCESoft保證你100%的比例，今天你選擇VCESoft，選擇你要開始的訓練，並通過你的下一次的考題，你將得到最好的資源與市場的相關性和可靠性保證。VCESoft Anthropic的CCAR-F考題和答案反映的問題問CCAR-F考試。

Anthropic CCAR-F Exam Syllabus Topics:

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

>> CCAR-F考試指南 <<

Anthropic CCAR-F最新考古題，CCAR-F最新考題

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最新的 Claude Certified Architect CCAR-F 免費考試真題 (Q131-Q136):

問題 #131
Your pipeline runs:
PROMPT= " You are a code reviewer. "
PROMPT= " $PROMPT Analyze the provided diff "
PROMPT= " $PROMPT for bugs, security issues, "
PROMPT= " $PROMPT and style violations. "
claude -p \
--dangerously-skip-permissions \
--system-prompt " $PROMPT " < diff.txt
The reviews complete and return feedback, but Claude comments only on the piped diff-it never reads surrounding files in the checked-out repository to understand broader context, even when the diff modifies a function called by many other modules. Which change to the invocation will cause Claude to read related repository files while still applying your custom review instructions?

A. Keep --system-prompt and add --allowedTools " Read, Glob, Grep " because non-interactive -p mode otherwise disables filesystem tools.
B. Replace --system-prompt with --append-system-prompt so the review instructions are added to Claude Code's default prompt instead of overwriting its built-in file-reading and code-navigation guidance.
C. Remove --system-prompt entirely and place the review instructions in a root-level CLAUDE.md because --system-prompt is incompatible with tool use under -p.
D. Stop piping the diff through standard input and embed it in the prompt string so Claude Code treats the invocation as an agentic session rather than a stream-processing operation.

答案：B

解題說明：
The --system-prompt flag replaces Claude Code's complete default system prompt. Although this does not technically remove the available tools, it discards the default coding-agent guidance that tells Claude how to gather repository context, navigate code, use tools, and verify findings. The replacement prompt says only to analyze the supplied diff, so the observed diff-only behaviour is consistent with the configured instructions.
Option B preserves the standard Claude Code identity and tool guidance while adding the custom review criteria. The Claude Code CLI reference states that --append-system-prompt appends instructions to the default prompt, whereas --system-prompt replaces it. Anthropic specifically recommends appending when Claude should remain a coding assistant that follows additional per-invocation rules.
Option A is incorrect because print mode does not inherently disable Read, Glob, or Grep; --allowedTools restricts or pre-authorizes tools but does not restore overwritten behavioural guidance. Option C is incorrect because replacement system prompts remain compatible with tools, although the replacement must supply suitable tool instructions. Option D changes where the diff appears but does not restore Claude Code's default code-navigation behaviour. Appending the review instructions directly addresses the configuration error.

問題 #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 used the agent yesterday to analyze a legacy authentication module, identifying two distinct refactoring approaches: extracting a microservice versus refactoring in-place. Today, they want to explore both approaches in depth-having the agent propose specific code changes for each-before deciding which to implement.
What's the most effective way to structure this exploration?

A. Resume yesterday's session and explore both approaches sequentially within the same conversation thread.
B. Resume yesterday's session to explore the first approach, then start a new session for the second, manually recreating the original context.
C. Use fork_session to create two branches from yesterday's analysis, exploring one approach in each fork.
D. Start two fresh sessions, manually providing a summary of yesterday's analysis findings to establish context.

答案：C

解題說明：
Forking is specifically designed for exploring alternative directions from a shared body of prior analysis. Each fork starts with a copy of yesterday's conversation history, including the files read, architectural observations, dependency findings, and decisions already recorded. The microservice approach and the in-place refactoring approach can then develop independently under separate session IDs.
Anthropic's Agent SDK documentation states that a fork creates a new session from a copy of the original history while leaving the original session unchanged. Each resulting session can subsequently be resumed independently. The documented implementation combines resume with fork_session=True in Python or forkSession: true in TypeScript. ( https://code.claude.com/docs/en/agent-sdk/sessions ) Option B allows conclusions, assumptions, and proposed edits from the first approach to contaminate the evaluation of the second. Option C preserves context for only one branch and forces the engineer to reconstruct context manually for the other. Option D discards the detailed analysis already captured in the session and depends on potentially incomplete summaries.
Two forks provide equivalent starting conditions, preserve the parent investigation, and support a fair comparison of scope, migration risk, operational complexity, and required code changes. Filesystem edits should still be isolated through worktrees or checkpointing because session forking branches conversation history, not the working directory.
Official references/topics: Agent SDK Sessions; Session Forking; Alternative-Approach Exploration; Context Preservation.

問題 #133
Users report that final reports sometimes lack depth on specific subtopics. Investigation shows that the document analysis agent frequently identifies gaps - for instance, noting "the retrieved sources discuss API authentication but lack details on token refresh patterns" - but under the current strict pipeline, this insight isn't actionable since search has already completed. What's the most effective architectural change?

A. Have the analysis agent report specific gaps to the coordinator, which triggers targeted searches and re-invokes analysis until sufficient.
B. Have the coordinator review analysis output for gap indicators and re-invoke search with gap- informed queries when gaps are detected.
C. Add a research planning agent before the search phase that decomposes topics into specific sub- questions.
D. Have the synthesis agent attach confidence scores to each section and flag areas with insufficient coverage for manual review.

答案：B

解題說明：
Making the coordinator gap-aware allows it to dynamically re-delegate targeted searches based on analysis feedback. This iterative loop ensures subtopics with insufficient coverage are addressed before synthesis, improving the depth and completeness of final reports.

問題 #134
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. Switch to using Grep to search for specific function names instead of reading full files, reducing the content loaded into context for remaining exploration.
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. Document all current findings in a summary report, clear context completely, then use that report as the sole reference for continuing the investigation.
D. 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.

答案：D

解題說明：
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.

問題 #135
When the agent calls lookup_order and receives order details showing the item was purchased
45 days ago, how does the agentic loop determine whether to call process_refund or escalate_to_human next?

A. The agent executes the remaining steps in a tool sequence planned at the start of the request.
B. The orchestration layer automatically routes to the next tool based on the order's status field.
C. The agent follows a pre-configured decision tree mapping order attributes to specific tool calls.
D. The order details are added to the conversation and the model reasons about which action to take.

答案：D

解題說明：
In an agentic loop, tool results are returned to the model as context. The model then reasons over the updated information, such as the purchase age, and decides the next appropriate action based on the available tools, policies, and task objective.

問題 #136
......

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CCAR-F最新考古題: https://www.vcesoft.com/CCAR-F-pdf.html