Anthropic Claude Certified Architect - Foundations

QUESTION NO: 1
A chatbot frequently receives greetings, policy questions, and technical support requests. Which architecture improves maintainability?

A. Disable retrieval. B. Prompt routing based on request type. C. One prompt for every scenario. D. Maximum temperature.

Correct Answer: B

Explanation: Only visible for Pass4Test members. You can sign-up / login (it's free).

QUESTION NO: 2
Production reviews reveal inconsistent handling of uncertainty in final reports. Sometimes conflicting subagent findings are synthesized into a single confident statement (losing nuance), while other times reports over-hedge with excessive qualifications (becoming unhelpful). When the web search agent returns "industry analysts estimate $50B market size (methodology varies)" and the document analysis agent returns "peer-reviewed study estimates $35B (±$7B, 95% CI)," the coordinator either picks one arbitrarily or produces vague statements like "the market may be
$35B-$50B depending on factors." What systematic approach best addresses this?

A. Instruct the synthesis agent to structure reports with explicit sections distinguishing well- established findings from contested ones, preserving original source characterizations and methodological context. B. Configure subagents to only report findings meeting a high-confidence threshold, filtering uncertain information before it reaches the coordinator. C. Implement a confidence calibration layer that normalizes subagent uncertainty expressions to standardized probability scores (0.0-1.0), then weight-average findings by their calibrated confidence. D. Add a verification subagent that cross-references findings across sources, only passing claims to synthesis that are corroborated by at least two independent sources.

Correct Answer: A

Explanation: Only visible for Pass4Test members. You can sign-up / login (it's free).

QUESTION NO: 3
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. 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. C. 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. 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.

Correct Answer: C

Explanation: Only visible for Pass4Test members. You can sign-up / login (it's free).

QUESTION NO: 4
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 sees an unfamiliar error message "SYNC_CONFLICT: entity version mismatch detected" in production logs but doesn't know which of the 12 services in the codebase generates it. They ask the agent to help locate the source code. What exploration approach will most efficiently find the responsible code?

A. Use Grep to find all files that import the project's error handling module, then Read those files to locate custom error definitions. B. Use Glob to find files in directories commonly associated with error handling (such as errors/, exceptions/,or handlers/) across all services, then Read each matching file. C. Use Grep to search for distinctive text from the error message (like "SYNC_CONFLICT" or "entity version mismatch"), then Read the matching files to understand context. D. Read the project's README and service configuration files to understand the architecture, then systematically Read source files in each service directory.

Correct Answer: C

Explanation: Only visible for Pass4Test members. You can sign-up / login (it's free).

QUESTION NO: 5
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 orchestration layer automatically routes to the next tool based on the order's status field. B. The agent follows a pre-configured decision tree mapping order attributes to specific tool calls. C. The order details are added to the conversation and the model reasons about which action to take. D. The agent executes the remaining steps in a tool sequence planned at the start of the request.

Correct Answer: C

Explanation: Only visible for Pass4Test members. You can sign-up / login (it's free).

QUESTION NO: 6
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 is configuring MCP servers in Claude Code. You want to add a shared venue lookup server that all team members should have access to, and you personally want to add an experimental music playlist server that only you are testing. Which configuration approach correctly applies MCP server scopes?

A. Add both servers to your local ~/.claude.json B. Add venue server to ~/.claude.json and playlist server to .mcp.json C. Add venue server to .mcp.json and playlist server to ~/.claude.json. D. Add both servers to the project-level .mcp.json file

Correct Answer: C

Explanation: Only visible for Pass4Test members. You can sign-up / login (it's free).

QUESTION NO: 7
Your document extraction tool uses ML models to extract invoice fields (vendor, amount, date).
The models return confidence scores (0.0-1.0) for each extracted field. In production, you observe: (1) the agent proceeds with low-confidence extractions that are incorrect 23% of the time, and (2) the agent requests unnecessary human review for 31% of extractions that were actually correct. How should you restructure the tool's output?

A. Return fields with confidence scores, plus a request_review boolean computed using your tested confidence thresholds, along with a review_reasons array explaining which fields triggered review. B. Return fields organized into verified and needs_verification objects based on confidence thresholds. C. Compute an aggregate extraction_quality score across all fields and return it alongside the extracted values. Include a text summary describing the overall extraction reliability. D. Return fields with their raw confidence scores and add detailed few-shot examples to your system prompt demonstrating how to interpret different confidence ranges and when to request human review.

Correct Answer: A

Explanation: Only visible for Pass4Test members. You can sign-up / login (it's free).

QUESTION NO: 8
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 new payment processing module that must follow your project's established patterns for database transactions, error handling, and audit logging. You've identified three existing modules that exemplify these patterns: db_utils.py, error_handlers.py, and audit_logger.py. This is a one-off integration task - these patterns are well-documented in your team wiki and don't need additional project-level documentation. What's the most effective approach?

A. Use @ references to include the three modules directly in your prompt, giving Claude concrete code examples of the patterns to follow. B. Describe the patterns from the three modules in natural language in your prompt, explaining the transaction handling approach, error format, and logging conventions Claude should follow. C. Add documentation of each pattern to your CLAUDE.md file, establishing them as project conventions that Claude will apply automatically. D. Ask Claude to explore your codebase to find and understand the transaction, error handling, and logging patterns before generating the new module.

Correct Answer: A

Explanation: Only visible for Pass4Test members. You can sign-up / login (it's free).

Anthropic Claude Certified Architect - Foundations - CCAR-F Exam Questions