https://www.passcert.com/CCAR-F.html Page 2 23 questions selected from source version V9.02 CLAUDE CERTIFIED ARCHITECT Question 1 You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports. A user expands the research system beyond its original web-search agent by adding specialized data sources. A financial API agent returns structured JSON containing revenue, margins, and growth rates. A news-monitoring agent returns prose summaries of recent developments. A patent-analysis agent returns structured lists of technology areas. The synthesis agent combines these results into executive briefings. Currently, it converts everything into bullet points, causing financial comparisons to lose tabular clarity and news summaries to lose their narrative flow. What change would most improve briefing quality? A. Standardize all subagent outputs as prose summaries with inline citations. B. Add a format-conversion layer that transforms every subagent output into a common intermediate representation. C. Update the synthesis agent to render each content type appropriately-for example, financial data as tables, news as prose, and patent areas as structured lists. D. Standardize all subagent outputs as JSON containing claim, evidence, source, and confidence fields. Answer: C Explanation Option C preserves the information structure that makes each source useful. Financial metrics share comparable fields and therefore benefit from rows, columns, aligned units, and reporting periods. News findings require connected prose to preserve chronology and causal relationships, while patent technology areas are naturally represented as categorized lists. Anthropic's output-consistency guidance recommends specifying the exact output format needed for the task rather than relying on an unspecified default. Anthropic' s discussion of its multi-agent research system also recognizes specialized output stages for reports, structured data, and visualizations because specialist prompts can produce better results than generic coordinator processing. Option A destroys the comparative structure of numerical data. Option D can provide a useful provenance contract internally but does not determine how the executive briefing should present heterogeneous content. Option B risks creating a lowest-common-denominator representation that discards source-specific advantages. The synthesis contract should preserve normalized facts and provenance internally while directing the report generator to select presentation forms according to the content's semantic structure and the executive reader's needs. https://www.passcert.com/CCAR-F.html Page 3 CLAUDE CERTIFIED ARCHITECT Question 2 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. 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. 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. Switch to using Grep to search for specific function names instead of reading full files, reducing the content loaded into context for remaining exploration. D. Document all current findings in a summary report, clear context completely, then use that report as the sole reference for continuing the investigation. Answer: A Explanation 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. https://www.passcert.com/CCAR-F.html Page 4 CLAUDE CERTIFIED ARCHITECT Question 3 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. A developer asks the agent to investigate why a specific API endpoint intermittently returns 500 errors. The codebase has 200+ files and the developer doesn't know which components are involved. The agent must trace the error through routing, middleware, business logic, and database layers. What task decomposition approach would be most effective? A. Have the agent first create a comprehensive plan mapping all code paths through the endpoint before beginning any file exploration or code reading. B. Define a fixed sequence of investigation steps upfront-grep for error patterns, then read error handlers, then check database queries, then examine middleware-executing each step regardless of intermediate findings. C. Run parallel worker agents that simultaneously investigate all four layers, then synthesize their findings to identify where the error originates. D. Have the agent dynamically generate investigation subtasks based on what it discovers at each step, adapting its exploration plan as new information about the error path emerges. Answer: D Explanation The investigation path cannot be reliably predetermined because the responsible files, components, and execution sequence are unknown. The agent should begin with available evidence-such as route definitions, stack traces, logs, or endpoint references-and use each discovery to decide the next search, file read, or diagnostic action. Anthropic distinguishes predefined workflows from agents that dynamically direct their own processes and tool usage. Agents are appropriate for open-ended problems where the required number and nature of the steps cannot be predicted or encoded as a fixed path. During execution, the agent should obtain ground truth from tool results and adapt its plan based on that environmental feedback. (https://www.anthropic.com /engineering/building-effective-agents) Option A requires a comprehensive plan before the agent has inspected the code, so the plan would rest on unsupported assumptions. Option B forces every investigation through the same sequence even when an early discovery makes later steps irrelevant or identifies a different dependency path. Option C assumes the four layers can be investigated independently; tracing an intermittent request failure usually involves dependencies revealed sequentially across layers. Option D implements an adaptive agent loop: inspect, form a hypothesis, use tools, evaluate the evidence, and generate the next subtask. The workflow should still include stopping conditions, testable hypotheses, and escalation when evidence remains inconclusive. Official references/topics: Adaptive Agent Loops, Dynamic Task Decomposition, Tool Feedback, Open-Ended Coding Investigations. https://www.passcert.com/CCAR-F.html Page 5 CLAUDE CERTIFIED ARCHITECT Question 4 You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems. Your extraction pipeline processes contracts that frequently include amendments. When a contract contains both original terms and later amendments (e.g., original clause specifies "30-day payment terms" while Amendment 1 changes this to "45 days"), the model inconsistently extracts one value or the other with no indication of which applies. What's the most effective approach to improve extraction accuracy for documents with amendments? A. Preprocess documents with a classifier that identifies and removes superseded sections before the main extraction step. B. Redesign the schema so amended fields capture multiple values, each with source location and effective date. C. Add prompt instructions to always extract the most recent amendment value and ignore superseded original terms. D. Implement post-extraction validation using pattern matching to detect amendments and flag those extractions for manual review. Answer: B Explanation The document contains multiple factually valid values whose applicability depends on chronology and legal context. Collapsing those values into a single scalar field discards essential provenance. Option B corrects the data model by representing each term as a structured record containing the extracted value, source location, document or amendment identifier, and effective date. Anthropic's Structured Outputs feature is designed for data-extraction use cases in which nested objects and arrays must conform to a defined JSON Schema. (https://platform.claude.com/docs/en/build-with-claude /structured-outputs) Anthropic also recommends grounding factual outputs in direct source material and making claims auditable through supporting evidence. (https://docs.anthropic.com/en/docs/test-and-evaluate /strengthen-guardrails/reduce-hallucinations) A provenance-aware schema applies both principles: it retains the original clause and the amendment instead of forcing Claude to resolve a potentially complex legal precedence question during extraction. Option A is destructive because removing superseded text prevents auditing and may eliminate terms still relevant to earlier periods. Option C oversimplifies amendment logic; the newest document is not automatically controlling for every date, jurisdiction, or clause. Option D identifies risk but does not improve the extracted representation and unnecessarily sends all amendment cases to manual review. After extraction, deterministic business logic can select the value effective on a requested date while retaining the complete contractual history. Official references/topics: Structured Outputs; Nested Schema Design; Provenance and Source Grounding; Temporal Data Modeling. https://www.passcert.com/CCAR-F.html Page 6 CLAUDE CERTIFIED ARCHITECT Question 5 You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports. The synthesis agent completes its initial pass but flags that three key research questions remain unanswered because the web-search and document-analysis agents did not find relevant information on those specific subtopics. The coordinator currently proceeds directly to report generation, producing reports with incomplete coverage. What change would most effectively improve research completeness? A. Increase the initial breadth of queries sent to web search and document analysis to reduce the probability of missing relevant information. B. Have the coordinator evaluate the synthesis output for gaps, then re-delegate to web search and document analysis with targeted queries before invoking synthesis again. C. Have the report-generation agent note which research questions could not be answered, so users understand the limitations of the final output. D. Give the synthesis agent direct access to web-search tools so it can autonomously fill knowledge gaps without returning control to the coordinator. Answer: B Explanation Option B introduces an evaluator-and-refinement loop at the correct orchestration layer. The coordinator already owns the research plan and delegation decisions, so it should inspect the synthesis result against the required questions, identify coverage gaps, and issue focused follow-up assignments. Anthropic's description of its multi-agent research system follows this pattern: the lead agent synthesizes returned findings, determines whether additional research is required, and creates new subagents or refines its strategy before producing the final result. Increasing the initial query breadth, option A, may generate additional irrelevant material and cannot guarantee that unforeseen gaps will be covered. Option C merely documents the incompleteness instead of correcting it. Option D weakens role separation by giving the synthesis agent search capabilities, increasing tool complexity and bypassing the coordinator's centralized tracking. Targeted re-delegation preserves specialized responsibilities and creates an observable sequence of research, evaluation, refinement, and resynthesis. The coordinator should also maintain explicit coverage criteria and limit the number of refinement rounds so the system improves completeness without entering an uncontrolled research loop. CLAUDE CERTIFIED ARCHITECT Question 6 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. 1.5An engineer asks the agent to understand how the caching layer works before adding a new cache invalidation trigger. After initial Grep searches, the agent has identified that caching logic spans 15 files including decorators, middleware, and service classes (~6,000 lines total). What's the most effective next step for building understanding while managing context constraints? A. Use Grep to search for "invalidate" and "expire" patterns across all files, then Read only those specific line ranges with minimal surrounding context. B. Use the Read tool to sequentially load all 15 files, building complete understanding across the full caching https://www.passcert.com/CCAR-F.html Page 7 Question 6 continued implementation. C. Use Glob to find files matching common caching patterns (cache*.py, caching/), prioritize the largest files by reading them first, then check smaller files for gaps. D. Analyze imports and class hierarchies to identify the base cache class. Read that file to understand the interface, then trace specific invalidation implementations. Answer: D Explanation The correct objective is to construct an architectural map before consuming the full implementation. Identifying the base cache abstraction, its interface, and the classes that implement or invoke it gives the agent a dependency-guided path through the code. It can then inspect only the invalidation implementations and integration points relevant to the proposed trigger. This approach protects the context window. Anthropic states that every file read occupies context and that model performance can deteriorate as the window fills. Its Claude Code guidance warns against unbounded investigation that reads large numbers of files and recommends narrowing the exploration or delegating it. (https://code.claude.com/docs/en/best-practices) Option A is too lexical: searching only for invalidate or expire can miss event-driven invalidation, overridden methods, cache-key mutation, and generic interface calls. Option B loads approximately 6,000 lines without first establishing relevance. Option C assumes that filename patterns and file size correlate with architectural importance; the largest files may contain incidental code while a small interface defines the entire design. Option D follows control and type relationships rather than arbitrary file order. After reading the base class, the agent can search for subclasses, imports, construction sites, middleware hooks, and calls to the invalidation contract, progressively expanding only where evidence requires it. Official references/topics: Context-Efficient Exploration; Dependency-Guided Reading; Architectural Interfaces; Narrowly Scoped Investigation. CLAUDE CERTIFIED ARCHITECT Question 7 You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives. Your pipeline includes a release-notes generation step that classifies and summarizes approximately 200 commits at the end of each weekly release cycle. Each commit is currently sent as a separate Messages API request using a Sonnet-tier Claude model. The release notes are not needed until the following morning, providing approximately 12 hours of acceptable latency. Your team must reduce the per-token API cost while retaining the same model, prompts, and output quality. Which approach satisfies all these constraints? A. Issue the 200 Messages API requests concurrently because parallel execution reduces the per-token price. B. Submit the 200 requests through the Message Batches API with unique custom_id values and retrieve the results after the batch finishes. C. Concatenate all 200 commit messages into one Messages API request because reducing the number of requests always reduces token costs. D. Replace the Sonnet-tier model with a Haiku-tier model to obtain a lower per-token price. Answer: B https://www.passcert.com/CCAR-F.html Page 8 Question 7 continued Explanation Option B applies Anthropic's dedicated asynchronous bulk-processing mechanism while preserving the existing model and prompt for every commit. The Message Batches API accepts independent Messages API requests, each identified by a unique custom_id, and charges both input and output usage at 50% of standard API prices. The approximately 12-hour latency allowance makes the release-notes workload well suited to batching because immediate results are unnecessary. Option A may reduce wall-clock completion time, but concurrency does not alter the API's per-token price. Option C changes the task structure and risks exceeding context or output limits, mixing commit-level classifications, complicating retries, and making it harder to associate errors with individual commits. A single large request also does not automatically consume fewer tokens because the model must still process all commit content. Option D violates the requirement to retain the same model tier and output-quality profile. Batch results may arrive in an order different from submission order, so the pipeline must associate every response with its original commit through custom_id. This provides lower cost without changing the individual review prompts. Anthropic Message Batches documentation CLAUDE CERTIFIED ARCHITECT Question 8 You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports. After the web-search and document-analysis subagents complete their tasks, the coordinator needs to spawn the synthesis subagent to synthesize the findings. What is the correct approach for providing the synthesis subagent with the information it needs? A. Provide the subagent with tool definitions that allow it to request outputs from other subagents through callbacks. B. Include the complete findings from both subagents directly in the synthesis subagent's prompt. C. Spawn the subagent with only a brief task description, relying on automatic context inheritance from the coordinator. D. Pass reference identifiers and configure the subagent with read access to a shared memory store where the other subagents deposited their results. Answer: B Explanation Option B follows the Claude Agent SDK's subagent context model. A normal subagent begins with a fresh context window and does not inherit the parent agent's conversation history or prior tool results. Anthropic's Subagents in the SDK documentation states that the information passed from parent to subagent is the spawning tool's prompt string; required file paths, decisions, errors, or findings must therefore be included in that prompt. In this scenario, the coordinator should supply both agents' relevant findings, source metadata, and explicit synthesis instructions. "Complete findings" means the full required result artifacts, not every intermediate search trace. Option C incorrectly assumes automatic context inheritance. Option A introduces callbacks that are neither necessary nor the standard handoff mechanism. Option D can be a valid custom architecture when a shared store has deliberately been implemented, but the question does not establish such infrastructure, and identifiers alone do not give the subagent information. The prompt should use clear sections or structured objects to distinguish web findings, document findings, sources, unresolved conflicts, and expected output. This preserves context isolation while providing everything the synthesis task actually requires. https://www.passcert.com/CCAR-F.html Page 9 CLAUDE CERTIFIED ARCHITECT Question 9 You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives. Your pipeline runs: PROMPT= ' You are a code reviewer. Analyze the provided diff for bugs, security issues, and style violations. ' claude -p \ --dangerously-skip-permissions \ --system-prompt " $PROMPT " \ < diff.txt The reviews complete and return feedback, but Claude only comments on the piped diff text-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 inspect related repository files while still applying your custom review instructions? A. Remove --system-prompt entirely and place the review instructions in a CLAUDE.md file, because -- system-prompt is incompatible with tool use under -p. B. Keep --system-prompt and add --allowedTools " Read, Glob, Grep ", because non-interactive -p mode otherwise disables filesystem tools. C. Stop piping the diff through standard input and embed it inside the prompt, so Claude Code treats the invocation as an agentic session. D. Replace --system-prompt with --append-system-prompt and explicitly instruct Claude to inspect related repository files whenever broader context is needed. Answer: D Explanation Option D preserves Claude Code's standard coding-agent instructions while adding the specialized review criteria. Anthropic documents that --system-prompt replaces the entire default system prompt, including its tool guidance, safety instructions, and coding conventions. It does not technically disable tools, but removing that guidance can make the invocation behave like a narrowly scoped text processor. --append-system-prompt retains the default behavior and layers the review instructions on top. The prompt should explicitly direct Claude to use Read, Glob, and Grep to inspect definitions, callers, tests, and related modules whenever the diff alone is insufficient. Option A is incorrect because --system-prompt is not incompatible with tools. Option B is also inaccurate: --allowedTools pre-approves tool execution; it does not make tools available when -p would otherwise disable them. In this command, --dangerously-skip-permissions already bypasses permission prompts. Option C is false because Claude Code officially supports piped standard input in non-interactive mode. The repaired invocation should therefore use --append-system-prompt and include an explicit repository-exploration requirement. Claude Code programmatic usage, CLI system-prompt reference https://www.passcert.com/CCAR-F.html Page 10 CLAUDE CERTIFIED ARCHITECT Question 10 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 has connected a custom MCP server that provides DevOps workflow templates. The server exposes several MCP prompts (such as deploy_checklist and incident_response) in addition to tools. How do these MCP prompts become accessible within Claude Code? A. They are automatically prepended to every conversation as additional system-level context, influencing Claude's behavior throughout the session. B. They are added to Claude Code's tool registry alongside the server's tools, invoked automatically by the model when relevant to the task. C. They are surfaced as @ -mentionable resources alongside files, fetched and attached to your message when referenced. D. They appear as slash commands (e.g., /mcp__servername__deploy_checklist) that you can invoke, with arguments passed after the command name. Answer: D Explanation MCP prompts are exposed as user-invoked commands rather than autonomous tools or permanently loaded system instructions. Claude Code dynamically discovers prompts from connected MCP servers and displays them in the command list using the naming convention /mcp__servername__promptname. Arguments are supplied as space-separated values after the command. When executed, the MCP server resolves the prompt and its returned content is injected into the active conversation. Anthropic's official documentation provides examples such as /mcp__github__list_prs and /mcp__jira__create_issue " Bug in login flow " high. (https://code.claude.com/docs/en/mcp) Option A would consume context continuously and incorrectly treat optional workflow templates as mandatory system instructions. Option B confuses MCP prompts with MCP tools: tools are model-callable operations, while prompts are reusable prompt templates invoked as commands. Option C describes MCP resources, which can be referenced and attached but are a distinct MCP capability. For the stated server, the team could invoke commands such as /mcp__devops__deploy_checklist or/mcp__devops__incident_response service-name. The exact server segment is derived from the configured server name, with normalization applied where necessary. Official references/topics: MCP Prompts; Dynamic Prompt Discovery; MCP Slash-Command Naming; Prompt Arguments. https://www.passcert.com/CCAR-F.html Page 11 CLAUDE CERTIFIED ARCHITECT Question 11 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. Create a custom slash command encapsulating the migration transformations, then execute it against each file without prior codebase exploration. B. Update the dependency version, run the test suite, and use Claude Code to fix each failure as it appears. 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 Explanation This migration is a high-impact, cross-module change with several independent breaking changes. Before editing, Claude must determine how the callback-based API is currently used, where the restructured User fields propagate, whether deprecated methods are wrapped or re-exported, and which downstream modules depend on the affected behavior. Plan mode is therefore the correct starting point because it allows Claude to inspect the repository, identify affected code paths, and produce an implementation strategy without modifying source files. Anthropic recommends separating exploration and planning from implementation when the approach is uncertain, the change affects multiple files, or the developer is unfamiliar with the impacted code. Direct execution is better reserved for small, clearly scoped changes that can be described as a simple diff. (https://code.claude.com/docs/en/best-practices) Option A applies transformations before establishing whether every usage follows the same pattern. Option B turns the test suite into a reactive discovery mechanism and may miss untested behavior. Option D provides useful migration documentation but assumes all 45 files can be changed uniformly. The reliable sequence is exploration, impact mapping, migration planning, implementation, and verification against tests and type checks. Official references/topics: Plan Mode; Explore-Plan-Implement Workflow; Multi-File Migration Planning; Verification. https://www.passcert.com/CCAR-F.html Page 12 CLAUDE CERTIFIED ARCHITECT Question 12 You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives. 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 linter in CI, (2) findings on automatically generated template code under src/gen/, and (3) rendering-helper patterns that are intentional project conventions but get 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 genuine issues? A. 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. 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 apply stricter evidence requirements to rendering-related issues. C. Add detailed explanations to the project's CLAUDE.md describing which patterns are intentional, that linting is handled separately by CI, and that the src/gen/directory contains automatically generated template code. Answer: A Explanation Option A uses the dedicated control surface for managed Claude Code Review. Anthropic's Code Review documentation states that a root-level REVIEW.md is injected into every review agent as the highest-priority instruction block. It can define skip paths, suppress categories already enforced by CI, recalibrate severity, cap nit volume, and require source evidence before reporting particular findings. The documentation explicitly identifies generated code, linting, and verification requirements as appropriate uses. Option B configures a self-hosted GitHub Actions workflow, but the scenario concerns the separate managed Code Review service running on Anthropic's infrastructure. Instructions in that workflow do not control the managed reviewer. Option C provides useful general project context, but CLAUDE.md has lower review-specific authority: managed Code Review treats violations of it primarily as nit-level findings. REVIEW.md is the stronger and more precise mechanism for changing what the managed service reports. The file should skip src/gen/**, suppress style issues already enforced by CI, and require concrete behavioral evidence for rendering-helper warnings while continuing to report verified correctness and security defects. https://www.passcert.com/CCAR-F.html Page 13 CLAUDE CERTIFIED ARCHITECT Question 13 You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports. Production monitoring shows that follow-up queries such as "summarize what we learned about market trends" consistently take more than 40 seconds. Investigation reveals that the coordinator spawns the synthesis subagent for each summarization request, passing more than 80,000 tokens of accumulated findings. The coordinator already has these findings in its context from orchestrating the research. What is the most effective way to improve response time for these follow-up summaries? A. Spawn the synthesis subagent with reduced context and have it request specific findings from the coordinator on demand. B. Have the coordinator handle straightforward summarization requests directly using its existing context, reserving subagent spawning for complex analysis. C. Pre-generate and cache summaries at multiple granularities whenever new findings accumulate. D. Enable prompt caching on the synthesis subagent to reduce the overhead of repeatedly transferring the same research findings. Answer: B Explanation Option B eliminates an unnecessary agent boundary. The coordinator already possesses the accumulated findings and can answer a straightforward follow-up without serializing more than 80,000 tokens into a fresh subagent context, waiting for another model execution, and receiving the result back. Anthropic's multi-agent research engineering guidance emphasizes scaling effort to task complexity: simple fact-finding or lightweight processing should use substantially fewer agents and tool calls than complex research. It also reports that multi-agent systems consume far more tokens than ordinary interactions, making avoidable delegation expensive and slow. Option A adds an interactive retrieval protocol between agents and additional round trips. Option C spends compute proactively on summaries that users may never request and creates cache-invalidation problems whenever findings change. Option D could reduce repeated input cost where caching is applicable, but the subagent still receives and processes an unnecessarily large context and still incurs spawning latency. Delegation is valuable when an isolated context or specialized capability materially improves the result. For a direct summary already supported by the coordinator's active context, local handling is the faster and simpler architecture. CLAUDE CERTIFIED ARCHITECT Question 14 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 agent has analyzed a complex service module-reading 23 source files, tracing request flows, and identifying error handling patterns. A developer wants to compare two testing strategies before committing to one: end-to-end tests with mocked external services vs. snapshot tests capturing expected outputs. They need to independently develop both approaches to evaluate trade-offs. How should you manage the sessions? A. Resume the analysis session with fork_session enabled, creating a separate branch for each testing strategy. https://www.passcert.com/CCAR-F.html Page 14 Question 14 continued B. Start two fresh sessions, having each re-read the relevant source files before beginning. C. Continue in the original session, developing end-to-end tests first, then snapshot tests sequentially. D. Export the analysis session's key findings to a file, then create two new sessions that reference this file. Answer: A Explanation Forking the existing analysis session creates independent continuations that inherit the accumulated conversation context. Each branch begins with the same understanding of the service module, request flow, source files, and error-handling patterns, but subsequent work on one testing strategy does not alter the other branch or the original session. Anthropic's Agent SDK documentation states that sessions can be resumed with their full context and forked to explore different approaches. In the SDK, enabling fork_session while resuming causes the continuation to receive a new session identifier rather than modifying the original session. (https://docs.anthropic.com/en /docs/claude-code/sdk?utm_source=chatgpt.com) Option B wastes time, tokens, and tool calls by requiring both new sessions to rebuild the same 23-file analysis. Option C mixes two experimental implementations into one conversation, increasing the risk that assumptions, edits, or conclusions from the first strategy influence the second. Option D preserves only a manually selected summary, which may omit details contained in the full session history. The appropriate design is to create one fork for the end-to-end strategy and another fork for the snapshot strategy. The original analysis remains a stable parent, while each child session develops and evaluates its approach independently. Official references/top