CCAR-F Exam Claude Certified Architect - Foundations (CCAR-F) https://www.passquestion.com/ccar-f.html 35% OFF on All, Including CCAR-F Questions and Answers Pass CCAR-F Exam with PassQuestion CCAR-F questions and answers in the first attempt. https://www.passquestion.com/ 1. 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. The automated review consistently flags patterns your team uses intentionally—force-unwrapping optionals in test files, using large coordinator classes that follow your established architecture, and importing internally maintained modules marked as deprecated in the public SDK. Developers dismiss approximately 30% of all findings as project-specific false positives. Which approach prevents the model from generating these findings in the first place by supplying the project’ s conventions as persistent context during every review? A. Document the team’s accepted patterns and intentional conventions in the project’s CLAUDE.md file so the model receives this context during every review. B. Configure the review to analyze only the changed lines in the diff without the surrounding file context, reducing the amount of code the model evaluates. C. Build post-processing keyword filters that suppress findings containing terms such as “force unwrap,” “large class,” or “deprecated import” before results reach developers. D. Have developers add inline suppression comments at flagged lines and preprocess diffs to exclude suppressed lines before sending code to the model. Answer: A Explanation: Option A supplies the missing project context before Claude evaluates the code. Anthropic’s CLAUDE.md documentation defines project-level files as persistent, version-controlled instructions for coding standards, architecture, workflows, and conventions. The described exceptions are stable repository facts: force-unwrapping is permitted in tests, coordinator classes follow an intentional pattern, and an internally maintained dependency remains approved despite its public deprecation status. Recording these facts concisely allows every review session to interpret the patterns correctly. Option B removes surrounding evidence and would make architectural and cross-file judgments less reliable. Option C hides findings after generation and can suppress genuine bugs containing the same keywords. Option D adds noise to source files and distributes policy across individual call sites instead of documenting it centrally. The CLAUDE.md entries should be specific and scoped—for example, limiting the force-unwrapping exception to test paths rather than approving it globally. Anthropic notes that shorter, precise instructions are followed more consistently, so the file should state the accepted convention, its scope, and any conditions that would still make the pattern reportable. PassQuestion CCAR-F Practice Questions 2 / 16 2. 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. PassQuestion CCAR-F Practice Questions 3 / 16 3. 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 system must extract event details from calendar invitations and output JSON that strictly conforms to a schema with fields for title, date, time, location, and attendees. Downstream systems reject any malformed or non-conformant JSON. What approach provides the most reliable schema compliance? A. Pre-fill Claude’s response with an opening brace to force JSON output, then complete and parse the response. B. Append instructions like “Output only valid JSON matching the schema exactly” and implement retry logic to re-prompt when JSON parsing fails. C. Define a tool with your target schema as input parameters and have Claude call it with the extracted data. D. Include detailed JSON formatting instructions and the target schema in your prompt, then parse Claude’ s text response as JSON. Answer: C Explanation: A tool definition converts the desired extraction structure into an explicit machine-readable contract. Claude returns the event information inside a tool_use block, with the tool arguments corresponding to the properties defined by the tool’s input_schema. Anthropic specifies that custom tool parameters are described using JSON Schema, allowing the application to extract the structured arguments directly rather than attempting to recover JSON from ordinary prose. For current implementations, adding strict: true to the tool definition provides guaranteed conformance of tool-call inputs to the declared schema. (https://docs.anthropic.com/en /docs/agents-and-tools/tool-use/implement-tool-use) Options A, B, and D remain prompt-based formatting techniques. They may improve the probability of valid JSON, but none creates the same schema-enforced interface. Prefilling an opening brace constrains the beginning of the response without guaranteeing valid field names, required properties, or data types. Retry logic detects failures only after generation and adds latency. Detailed formatting instructions can still produce malformed or structurally incorrect output. Anthropic now also provides Structured Outputs for direct, schema-validated JSON responses. Within the options presented, however, a schema-defined tool is the only approach that establishes an explicit structured-output boundary rather than relying primarily on text-generation compliance. (https://docs.anthropic.com/en /docs/test-and-evaluate/strengthen-guardrails/increase-consistency) Official references/topics: Tool Definitions, JSON Schema Input Contracts, Strict Tool Use, Structured Outputs. PassQuestion CCAR-F Practice Questions 4 / 16 4. You have configured the system so that all four subagents have access to the complete set of 18 tools. During testing, agents frequently call tools outside their specialization—the synthesis agent attempts web searches, and the report generator tries to analyze documents. What is the primary cause of this poor tool-selection behavior? A. The agents’ role descriptions in their system prompts conflict with having access to tools outside those roles. B. The tool definitions consume too much context-window space, leaving insufficient room for task content. C. The coordinator cannot track which capabilities each subagent has, leading to misrouted tasks. D. Choosing from 18 tools instead of four or five relevant tools increases decision complexity beyond reliable selection thresholds. Answer: A Explanation: Option A identifies the direct configuration contradiction. Each system prompt defines a specialized responsibility, but the corresponding tool allowlist presents unrelated actions as available capabilities. The synthesis agent may therefore interpret web searching as an acceptable way to fill evidence gaps, while the report generator may perform document analysis instead of limiting itself to report production. Anthropic’s subagent documentation defines subagents as focused workers with their own prompts and specific tool access. Its tool-definition guidance also recommends reducing selection ambiguity by presenting clear, relevant tools. The appropriate correction is least-capability configuration: expose search tools to the search agent, document tools to the analyzer, synthesis utilities to the synthesizer, and formatting or output tools to the report generator. Option B may affect token efficiency but does not explain the role-specific misuse pattern. Option C concerns delegation, whereas the improper calls occur after successful delegation. Option D is tempting, but Anthropic does not define four or five tools as a universal reliability threshold; 18 tools alone does not prove threshold failure. The decisive defect is misalignment between declared roles and permitted capabilities. PassQuestion CCAR-F Practice Questions 5 / 16 5. You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems. After implementing tool use with strict schema definitions, JSON syntax errors are eliminated, but 5% of extractions still contain empty arrays or null values for required fields such as citations and methodology. Spot-checking reveals that the source documents contain this information, but in varied formats—inline citations versus bibliographies, and methodology sections versus details embedded in introductions. What is the most effective way to address these failures? A. Implement retry logic that resends requests when validation detects empty required fields. B. Add few-shot examples demonstrating extractions from documents with varied structures, showing how to identify citations in different formats and locate methodology details across section types. C. Build a regex-based post-processing layer that scans source documents for citation patterns and methodology keywords, populating empty fields when the model fails to extract them. D. Modify the schema to make citations and methodology optional, and flag incomplete records for manual review instead of failing validation. Answer: B Explanation: Option B targets the remaining failure mode: semantic recognition across heterogeneous document structures. Strict schemas eliminate malformed JSON and can guarantee that tool inputs conform to declared types, but they cannot force Claude to locate evidence that appears under unfamiliar headings or in atypical sections. Anthropic’s prompting guidance says that a few relevant, diverse, structured examples are among the most reliable ways to improve accuracy and consistency. Examples should therefore show inline citations, reference lists, numbered bibliographies, methodology sections, and methods embedded in introductions, each paired with the correct extracted structure. This teaches the intended evidence-location and granularity rules rather than merely repeating the same request. Option A retries an unchanged prompt and can reproduce the same omission. Option C introduces brittle regex rules that may miss nonstandard citations and mistake keyword mentions for methodology content. Option D suppresses validation failures by weakening the contract, but it does not improve extraction and would convert recoverable omissions into incomplete records. The examples should be drawn from real failure cases, evaluated on a held-out set, and expanded when monitoring reveals new layouts. Schema constraints and few-shot coverage solve different layers of reliability and should be used together. PassQuestion CCAR-F Practice Questions 6 / 16 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. You need to add a date validation check ensuring event dates are in the future. This requires adding a conditional statement to one existing function in a single file. What is the most appropriate approach? A. Use direct execution to make the change. B. Start with extended thinking mode enabled to ensure thorough reasoning about the validation logic. C. Enter plan mode first to create a detailed implementation strategy before making the change. D. Enter plan mode to analyze how the validation might impact other parts of the reservation flow. Answer: A Explanation: This change is narrow, localized, and already defined: add one conditional validation check to an existing function in a single file. A separate planning phase would introduce process overhead without resolving meaningful architectural uncertainty. Direct execution allows Claude to read the function, implement the condition, and run the relevant focused tests. Anthropic explicitly states that plan mode adds overhead and should generally be skipped when the scope is clear and the fix is small. Planning is most valuable when the approach is uncertain, multiple files are affected, or the code is unfamiliar. Anthropic’s practical rule is that when the required diff can be described in one sentence, direct implementation is appropriate. (https://code.claude.com/docs/en/best-practices) Option B allocates unnecessary reasoning effort to straightforward validation logic. Options C and D exaggerate the complexity of a single-function change. Broader impact analysis would be justified only if the requirement altered reservation semantics, time-zone rules, persistence behavior, or public interfaces—none of which is stated. The implementation should still include verification. Claude should add or update tests for a future date, the current date, and a past date, then run the narrowest relevant test command. Direct execution does not mean unverified execution. Official references/topics: Direct Execution; Plan-Mode Selection; Small Scoped Changes; Focused Verification. PassQuestion CCAR-F Practice Questions 7 / 16 7. 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 the research phase takes longer than expected. Analysis reveals that the coordinator invokes the web-search subagent, waits for its response, and then invokes the document-analysis subagent. These tasks are independent; neither requires the other’s output. How should you modify the system to run these subagents concurrently? A. Switch both subagents to a Haiku-tier model instead of Sonnet to reduce their individual execution times. B. Structure the coordinator to emit both Agent tool calls—formerly Task tool calls—in a single response rather than across separate conversation turns. C. Add instructions explaining the performance benefits of parallel execution and requesting that the coordinator invoke both subagents simultaneously. D. Create an asynchronous orchestration layer that starts separate coordinator-subagent pairs in parallel and aggregates their results. Answer: B Explanation: Option B expresses both independent delegations in the same orchestration turn, allowing the runtime to execute them concurrently and return their results together. The current Claude Agent SDK calls the subagent-spawning capability the Agent tool; Task is its former name. Anthropic’s SDK subagent documentation explicitly identifies parallel analysis as a primary subagent use case. Claude tool responses can also contain multiple tool_use blocks, enabling independent calls to be handled as one parallel group rather than as serial model turns. Option A shortens each execution but does not remove the unnecessary wait between them and may reduce analysis quality. Option C provides useful behavioral guidance, but instructions alone do not establish the required response structure; the coordinator must actually emit both calls together. Option D duplicates coordinator execution, complicates state management, and creates unnecessary aggregation work. The correct flow is parallel fan-out from one coordinator, followed by a synchronization point that validates both results before synthesis begins. Failures should be tracked independently so that only the unsuccessful branch requires retrying. PassQuestion CCAR-F Practice Questions 8 / 16 8. 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. Standardize all subagent outputs as JSON containing fields for claim, evidence, source, and confidence. C. Update the synthesis agent to render each content type appropriately—financial data as tables, news as prose, and technology areas as structured lists. D. Add a format-conversion layer that transforms every subagent result into a common intermediate representation before synthesis. Answer: C Explanation: Option C preserves the semantic strengths of each information type instead of forcing heterogeneous evidence into one presentation structure. Financial values share comparable fields and therefore belong in a table. News developments require connected prose to preserve chronology and causality. Patent technology areas are naturally represented as grouped or prioritized lists. Anthropic’s prompting best practices recommend giving explicit output-format instructions and matching the requested format to the intended communication goal. The synthesis prompt should therefore include rendering rules for each recognized content type while maintaining common provenance fields behind the presentation. Option A damages numerical comparability and makes trends harder to scan. Option B can be useful as an internal exchange schema, but presenting the entire briefing as uniform JSON does not produce an effective executive document. Option D normalizes transport but does not correct the synthesis agent’s bullet-only rendering policy; a common representation can still be displayed badly. The correct design separates structured internal interchange from audience-facing presentation. The synthesis agent should preserve citations and metadata while selecting the most legible form for each class of evidence. PassQuestion CCAR-F Practice Questions 9 / 16 9. 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. PassQuestion CCAR-F Practice Questions 10 / 16 10. 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. Briefly explain what the agent can help with and offer to resolve the issue quickly, escalating only if the customer repeats the request. B. First call get_customer and lookup_order to gather account context, and then escalate to a human agent. C. Immediately call escalate_to_human with the conversation history. D. Acknowledge the frustration and ask one targeted question to understand the specific issue before escalating. Answer: C 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. PassQuestion CCAR-F Practice Questions 11 / 16 11. 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. In production, you observe that simple fact-checking queries, such as “In what year was the Paris Climate Agreement signed?”, traverse all four subagents sequentially, consuming more than 40 seconds and significant tokens per query. Complex comparative research benefits from the complete pipeline. Your query distribution is diverse and continues to evolve as users discover new applications. What is the most effective approach to optimize for varying query complexity? A. Create a fast path for factual questions that bypasses subagents entirely, routing every other query through the complete pipeline. B. Train a query-complexity classifier using labeled historical data to predict the optimal subagent combination, retraining it periodically. C. Implement pattern-based routing that classifies queries as single-fact, comparative, or analytical and maps each category to a predefined subagent combination. D. Have the coordinator analyze each query and dynamically determine which subagents are required. Answer: D Explanation: Option D allows orchestration effort to scale with the actual request. A simple factual query may require only the web-search agent and a direct coordinator response, whereas a comparative investigation may require web research, document analysis, synthesis, and report generation. Anthropic’s Building Effective AI Agents describes the orchestrator-workers pattern as a central model dynamically identifying subtasks, delegating them, and combining the results. It is specifically appropriate when the required subtasks cannot be predicted reliably in advance. Anthropic’s multi-agent research architecture likewise emphasizes varying the number of agents and tool calls according to task complexity. Option A introduces an inflexible binary decision and still sends every non-factual request through the full pipeline. Option B requires labeled data, ongoing retraining, and reliable definitions of the “optimal” agent combination. Option C is easier to implement but will become brittle as new query types appear. A capable coordinator can examine the requested output, necessary evidence, source requirements, and analytical depth at runtime, then invoke only the specialists that materially contribute to the answer. PassQuestion CCAR-F Practice Questions 12 / 16 12. 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 add comprehensive tests to a legacy codebase with 200 files and minimal existing test coverage. The engineer hasn’t specified which modules to prioritize. How should the agent decompose this open-ended task? A. Create a fixed testing schedule upfront based on directory structure, allocating equal effort to each top-level directory regardless of code complexity or business importance. B. Use Glob and Grep to map codebase structure, identify heavily-coupled modules, create a prioritized plan for high-impact areas, and revise as dependencies are discovered. C. Systematically read all 200 files to create a complete function inventory before writing any tests, ensuring the testing plan accounts for every function before beginning. D. Start writing tests for the first module alphabetically, using test failures and imports to discover related files organically. Answer: B Explanation: The task is open-ended because neither the critical modules nor the required testing sequence is known in advance. The agent should first use lightweight discovery tools to map the repository, locate existing tests, identify central modules, and determine which components have high fan-in, business significance, complex branching, or extensive external dependencies. It can then produce an initial risk-based testing plan and refine it as new dependency information appears. Anthropic distinguishes predefined workflows from agents that dynamically control their processes and tool usage. Agents are appropriate when the required steps cannot be reliably hardcoded and must adapt to environmental evidence. During execution, they should obtain ground truth through tool results and use that feedback to determine subsequent actions. (https://www.anthropic.com/research/building-effective-agents) Anthropic also identifies orchestrator-worker designs as suitable for complex coding and search tasks where the necessary subtasks depend on what the investigation reveals. (https://www.anthropic.com/research /building-effective-agents) Option A assigns effort using directory boundaries rather than risk. Option C exhausts context before delivering value. Option D uses alphabetical order, which has no relationship to impact or coverage priority. Option B establishes an evidence-driven decomposition: discover, prioritize, test high-impact paths, measure results, and revise the plan as dependencies and uncovered risks emerge. Official references/topics: Dynamic Task Decomposition; Adaptive Agent Loops; Orchestrator-Workers; Risk-Based Test Planning. PassQuestion CCAR-F Practice Questions 13 / 16 13. 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. Your agent is handling a billing dispute. After calling get_customer and lookup_order, it identifies that the dispute involves a promotional pricing error requiring manager approval—beyond the agent’s authorization level. How should the workflow handle this mid-process escalation? A. Call escalate_to_human, passing only the customer’s original message. B. Compile a structured handoff with customer details, order info, and the identified issue before calling escalate_to_human. C. Attempt the refund with process_refund anyway, escalating only if the system rejects the transaction. D. Persist the complete conversation and tool response history to a database, then call escalate_to_human with a reference ID. Answer: B Explanation: A mid-process escalation should transfer the decision-ready state accumulated by the agent. The human reviewer needs the verified customer identity, relevant order information, the promotional-pricing discrepancy, the reason approval is required, and any actions already attempted. Option B preserves this information in a concise, structured handoff while avoiding unnecessary repetition of the complete raw transcript. Anthropic’s tool-design guidance recommends returning high-signal information and stable identifiers containing only what Claude or the next workflow participant needs to determine the next action. Anthropic’s context-engineering guidance similarly advocates structured notes that preserve critical state and dependencies without retaining every redundant tool result. A structured escalation payload applies both principles and reduces handling time for the manager. (https://platform.claude.com/docs/en/agents-and-tools /tool-use/define-tools) Option A discards the investigation already completed. Option C violates the agent’s authorization boundary and risks an impermissible financial action. Option D provides auditability, but a reference ID alone forces the human to reconstruct the case from an excessively broad transcript. Human control must remain meaningful when an agent encounters a decision outside its authority; the agent should pause and hand the decision back with sufficient supporting context. (https://www.anthropic.com/research/trustworthy-agents) Official references/topics: Structured agent handoffs, high-signal tool results, human-control boundaries, persistent structured state. PassQuestion CCAR-F Practice Questions 14 / 16 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. An engineer used Claude Code yesterday to investigate authentication flows in a legacy monolith, building up significant context over a 2-hour session. Today she wants to continue that specific investigation. She’s worked on three other codebases since then and knows the session was named “auth-deep-dive”. How should she resume? A. Use --session-id with the UUID from yesterday’s session transcript file B. Use --continue to pick up where the most recent conversation left off C. Start fresh and re-read the same files D. Use --resume auth-deep-dive to load that specific session by name Answer: D Explanation: Claude Code supports resuming a specific saved session by either its identifier or its assigned name. Because the engineer knows the target session is named auth-deep-dive, the appropriate command is: claude --resume auth-deep-dive Anthropic’s CLI reference explicitly states that --resume can resume a session by ID or name and gives named-session usage in the same form. Resuming restores the relevant conversation history and accumulated context, including the prior analysis and files discussed during the investigation. (https://docs.anthropic.com /en/docs/claude-code/cli-reference) Option B is incorrect because --continue resumes the most recent session in the current directory. The engineer has conducted three subsequent sessions, so it may open an unrelated investigation. Option A uses a flag that is not the documented Claude Code mechanism for selecting a prior session; --resume itself accepts the session identifier when an ID is used. Option C discards two hours of established context and unnecessarily repeats codebase exploration. Named sessions are particularly useful when engineers alternate among multiple projects or parallel investigations. Assigning descriptive names allows the correct thread to be retrieved deterministically rather than depending on chronological recency. Official references/topics: Claude Code Session Persistence, Named Sessions, --resume, --continue. PassQuestion CCAR-F Practice Questions 15 / 16 15. 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. Anthropic’s tool use documentation states: “Write instructive error messages. Instead of generic errors like ‘failed’, include what went wrong and what Claude should try next.” A billing dispute agent uses lookup_order, which catches all exceptions and returns a tool_result with is_error: true and the message “Tool execution failed”. Monitoring shows two failure modes: the agent retries the identical call until hitting the turn limit, or it immediately calls escalate_to_human without trying alternative tools. Which change follows the documented recommendation and gives Claude the information it needs to select the correct recovery action for each error type? A. Implement retry logic with exponential backoff inside each tool implementation so transient errors are resolved transparently within the tool before any failure result is surfaced to Claude in the agentic loop. B. Return error-type-specific messages with is_error: true, e.g., “Order not found—try get_customer to search by phone” for data errors and “Database timeout (transient)—retry should succeed” for infrastructure errors. C. Remove is_error: true and return the error details as normal tool content, so Claude reasons about the response as data rather than treating it as a flagged failure condition that biases retry behavior. D. Add an error classification step in the agentic loop that intercepts tool errors before Claude sees them, then routes to hardcoded retry or escalation logic. Answer: B Explanation: Option B preserves the formal failure indicator while making the returned content operationally useful. Anthropic’s tool-use guidance states that when a tool fails, the application should return the error through tool_result content and mark it with is_error: true. Claude can then use that information to retry with corrected input, seek clarification, or explain the limitation. (https://docs.anthropic.com/en/docs/agents-and-tools/tool-use/bash-tool) The two example messages identify both the cause and the appropriate recovery path. “Order not found” indicates that repeating the same identifier will not help and proposes an alternative lookup method. “Database timeout” identifies a transient infrastructure condition for which a retry is reasonable. This eliminates blind repetition without removing Claude’s ability to adapt. Option A can be useful for tightly bounded low-level retries, but it does not solve permanent errors or tell the agent what happen