CCAR-F信息資訊 & CCAR-F認證指南

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Anthropic CCAR-F Exam Syllabus Topics:

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

>> CCAR-F信息資訊 <<

CCAR-F認證指南 & CCAR-F證照考試

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最新的 Claude Certified Architect CCAR-F 免費考試真題 (Q90-Q95):

問題 #90
Your agent uses three tools: get_property_details(property_id) returns data including street address, get_price_history(property_id) returns historical pricing, and get_neighborhood_info(address) returns area statistics. You observe that get_neighborhood_info always requires get_property_details first just to extract the address, even when users specify the property by ID. This creates unnecessary latency and failure coupling - if the first call fails, the neighborhood request also fails. What tool design change best addresses this?

A. Consolidate into a single get_property_with_neighborhood(property_id) tool returning both datasets.
B. Create a lookup_address(property_id) helper tool for retrieving addresses.
C. Change get_neighborhood_info to accept property_id, resolving the address internally.
D. Add retry logic and timeout handling to get_property_details.

答案：C

解題說明：
Allowing the neighborhood tool to accept the same identifier the user provides removes the need for the agent to chain through another tool just to extract an intermediate value. Resolving the address internally reduces latency, avoids unnecessary dependency on a prior tool call, and makes the workflow more reliable.

問題 #91
Your code-review prompts include both implementation changes and the corresponding test file, but the review comments fail to identify untested code paths. The model correctly flags functions that have no tests at all, but it fails to recognize when conditional branches or error-handling paths within tested functions lack coverage. What is the most effective way to improve branch- level gap detection without overcomplicating the pipeline?

A. Implement a two-pass pipeline in which one model call extracts all conditional branches and another cross-references them against test assertions.
B. Add explicit instructions requiring Claude to enumerate every conditional branch and exception path, then verify that each path has a corresponding test assertion.
C. Include few-shot examples showing code with an uncovered branch and the corresponding review comment identifying the missing test case.
D. Interleave the implementation and tests in the prompt, presenting each function immediately before its test cases.

答案：B

解題說明：
The current prompt asks for testing analysis at too high a level. Claude recognizes the obvious absence of an entire test but has not been instructed to construct a path-level inventory. Option B turns the desired behavior into an explicit verification procedure: enumerate each condition, alternative branch, early return, exception handler, and failure path, then locate a test assertion that exercises its behavior.
Anthropic's prompting best practices emphasize clear, specific instructions and explicit sequential steps when a task requires a defined analysis process. This change keeps the existing single review call while making the missing evaluation criterion unambiguous.

問題 #92
In addition to your CI pipeline, your organization has enabled Claude's managed Code Review through the Claude GitHub App on this repository, and reviews run automatically on every pull request. Reviews average
18 findings per pull request. Developer feedback reveals three categories of unwanted noise: (1) style and formatting issues already enforced by your CI linter, (2) findings on automatically generated template code under src/gen/*, and (3) rendering-helper patterns that are intentional project conventions but are flagged because they resemble common anti-patterns. Only approximately four findings per pull request are genuine logic bugs. What is the most effective way to reduce this noise while preserving the detection of real issues?

A. Configure separate GitHub Actions workflow files for each code area: one for generated code with findings suppressed, one for rendering code with custom instructions, and one general workflow for everything else.
B. Create a REVIEW.md file at the repository root containing skip rules for CI-enforced checks and generated files, together with a verification requirement that rendering-related findings cite a specific line demonstrating incorrect behavior.
C. Add custom review instructions to a GitHub Actions workflow file, using the action's prompt parameter to suppress duplicate lint findings, ignore generated template code, and impose stricter evidence requirements on rendering-related issues.
D. Add detailed explanations to the project's CLAUDE.md describing intentional patterns, stating that CI handles linting, and identifying src/gen/ as automatically generated code.

答案：B

解題說明：
Option A uses the dedicated configuration mechanism for Anthropic's managed Code Review service. The official Code Review documentation states that a root-level REVIEW.md is injected into every review agent as the highest-priority instruction block. It can redefine severity, suppress categories already enforced by CI, skip generated paths, and require concrete evidence before particular findings are reported.
The proposed rules map directly to the observed noise. Skipping lint and formatting findings eliminates duplication with deterministic CI checks. Excluding src/gen/* prevents comments on machine-generated code. Requiring a specific source line demonstrating incorrect behavior raises the verification threshold for rendering-helper findings without suppressing genuine defects.
Options B and C configure self-hosted GitHub Actions workflows, but the scenario concerns managed Code Review running through the Claude GitHub App on Anthropic's infrastructure. Those workflow prompts do not govern the managed reviewer. Option D supplies useful general project context, but CLAUDE.md applies to all Claude Code tasks and has lower review-specific authority. The documentation states that REVIEW.md is the stronger control for changing what managed Code Review flags and how findings are reported. It therefore provides the most targeted solution with the least unnecessary infrastructure.

問題 #93
After deploying the automated review, you notice high precision but low recall--real bugs are slipping through undetected. Investigation reveals that your review prompt instructs Claude to
"only report high-confidence issues you are certain about" and "err on the side of not commenting." Developers appreciate the low noise, but a race condition that caused a production outage was visible in a reviewed pull request and went unreported. You need to substantially improve bug detection while keeping false-positive rates manageable. What is the most effective approach?

A. Split the review into a finding stage whose objective is comprehensive coverage--reporting every potential issue with confidence and severity metadata--and a separate stage that verifies and thresholds those findings.
B. Expand the context to include related tests, recent Git history, and the module's dependency graph so Claude has richer evidence for judging severity.
C. Remove the conservative instructions and have Claude report every potential issue, then apply a programmatic filter that deduplicates findings and suppresses historically noisy categories.
D. Add detailed few-shot examples demonstrating bug categories Claude should flag--race conditions, null dereferences, and error-handling gaps--while retaining the high-confidence filtering instruction.

答案：A

解題說明：
The prompt's conservative reporting policy is directly causing the low recall. Claude may discover a legitimate race condition during analysis but suppress it because it cannot satisfy the instruction to report only issues about which it is certain. Option C separates two objectives that should not be conflated: broad defect discovery and strict acceptance filtering.
Anthropic's current code-review prompting guidance explicitly recommends reporting every issue, including uncertain or lower-severity findings, assigning confidence and severity metadata, and allowing a separate verification stage to filter them. This maximizes recall while retaining control over developer-facing noise.

問題 #94
Claude produces occasional factual inaccuracies despite clear prompts. Which architectural enhancement is MOST effective?

A. Remove examples.
B. Shorten responses only.
C. Increase temperature.
D. Add grounding through trusted retrieval.

答案：D

解題說明：
Grounding responses with trusted enterprise documents helps Claude generate answers supported by authoritative sources. Retrieval significantly reduces unsupported statements while improving factual consistency across knowledge-intensive applications.

問題 #95
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