Anthropic CCAR-F - Claude Certified Architect – Foundations

The schema already supports the correct representation of missing evidence: null . The remaining defect is behavioral. Claude must be explicitly instructed that absence of information is a valid outcome and that values may be populated only when directly supported by the supplied article.

Anthropic’s hallucination-reduction guidance recommends explicitly permitting Claude to express uncertainty, grounding outputs in the source, and retracting claims that lack supporting evidence. It also recommends restricting the model to the provided documents rather than allowing unsupported external knowledge. ( https://docs.anthropic.com/en/docs/test-and-evaluate/strengthen-guardrails/reduce-hallucinations ) Option C translates those controls directly into the extraction contract: when no source evidence exists, the model returns null .

Option A may improve baseline performance but does not remove the architectural ambiguity that encourages completion of missing fields. Option B is counterproductive because non-nullable required fields force the model to provide values even when the article contains none. Structured validation would confirm that the response is syntactically valid while accepting fabricated content. Option D adds cost, latency, and another probabilistic model decision; a second model can repeat or endorse the original hallucination.

A robust implementation should supplement the instruction with source spans or quotations for populated fields and automated checks where feasible. Nevertheless, among the options, explicit null behavior is the direct and most effective correction.

Official references/topics: Reduce Hallucinations; External-Knowledge Restriction; Nullable JSON Schema Fields; Grounded Extraction.

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.

================

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

================

In the standard Claude tool-use loop, the application executes lookup_order and sends its output back as a tool_result . That result becomes part of the conversation state available to Claude. Claude then evaluates the purchase date, refund policy, customer request, authorization constraints, and available tools before selecting the next action.

Anthropic describes client-tool orchestration as a repeated loop: Claude emits a tool_use request, the application executes it, returns a tool_result , and Claude continues reasoning from the updated conversation. Claude, rather than the tool implementation, selects when and how to invoke the next available tool unless the application has explicitly implemented a fixed workflow. ( https://platform.claude.com/docs/en/agents-and-tools/tool-use/how-tool-use-works )

Options B and C describe possible custom orchestration architectures, but neither is stated in the scenario. Option D is inconsistent with adaptive agent behavior because later actions depend on information that did not exist before lookup_order completed. A rigid sequence would not respond appropriately to different purchase dates, eligibility states, or order conditions.

The tool result should return high-signal fields such as purchase date, return-window status, refund eligibility, existing refund status, and stable order identifiers so Claude can make the subsequent decision accurately.

Official references/topics: Tool-result continuation, adaptive agent loops, model-directed tool selection, sequential dependency handling.

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/topics: Agent SDK Sessions, Session Forking, Context Preservation, Alternative-Approach Evaluation.

Option B uses the tool designed to search file contents. Anthropic’s Claude Code tools reference distinguishes Grep from Glob: Grep searches lines inside files, whereas Glob matches filenames and paths. Grep is built on ripgrep and accepts regular-expression patterns, so the opening parenthesis should be escaped as eval\( when the intention is to match the literal function call. The search can return matching files, line numbers, and surrounding context without loading every file into the model’s context window. Option A searches filenames resembling eval , not source files whose contents invoke the function. Option C follows only code reachable from a selected entry point and can miss test utilities, dynamically loaded modules, scripts, and dormant vulnerable code. Option D combines a recursive filename listing with text filtering; it still searches names rather than file contents. After Grep identifies matches, the agent should use Read on the relevant ranges to distinguish actual executable calls from comments, strings, or safe wrappers. Grep therefore provides the most complete and context-efficient initial security scan.

================

Option D fixes the actual failure while avoiding needless reprocessing. Anthropic’s Message Batches API treats every request independently, records the result against its unique custom_id, and explicitly states that one failed request does not affect the others. The 9,700 successful documents therefore require no retry. The failed requests exceeded the available context; increasing max_tokens changes the permitted output budget, not the size of the input context, so option C does not correct the cause. Resubmitting all 10,000 requests would repeat paid work, and prompt caching would only reduce some repeated-input cost without repairing oversized requests. Chunking each failed document reduces the input presented to Claude so every replacement request fits within the model’s context window . The application can then merge the extracted partial structures deterministically, retaining the original document identifier and chunk ordering. Anthropic recommends retry logic for failed batch requests and using custom_id to match results because batch results can arrive out of order. Consequently, selective retry plus chunking is both the technically correct recovery path and the lowest-cost option.

================

Option A removes the semantic ambiguity created by one generic tool performing three substantially different operations. Each replacement tool can have a distinct name, focused description, appropriate required parameters, and an output schema designed for its task. For example, extract_data_points can return typed records, while verify_claim_against_source can return a verdict, supporting excerpt, and source location.

Anthropic’s tool-design guidance recommends thoughtful tools with clear, distinct purposes and natural task boundaries. Ambiguous or overlapping capabilities make it harder for agents to select the correct operation and interpret its results consistently.

Option B improves the existing interface, but the implementation and result contract remain overloaded, increasing the possibility that the model or tool will mix behavior across modes. Option C adds another probabilistic classification stage without repairing the underlying interface. Option D may reduce some errors, but free-text interpretation remains the principal control mechanism. Purpose-specific tools encode the intended operation directly into the callable interface, permit strict schemas tailored to each result type, and make evaluation failures easier to diagnose. This produces a stronger contract between the agent and deterministic document-processing system.

================

Option C matches the distinction between reasoning about delegation and executing it. Defining subagents makes their descriptions available for selection, but the coordinator must still invoke the SDK’s subagent-spawning tool. Current Claude Agent SDK documentation calls this the Agent tool; Task was its earlier name and remains relevant to older SDK configurations. Anthropic’s Subagents in the SDK documentation instructs developers to include Agent in allowedTools so subagent invocations are approved automatically. Without that permission, an invocation can fall through to a permission callback or be denied under a non-interactive permission mode. Option A is unlikely because the configured subagent descriptions already tell Claude when each agent should be selected, although explicit prompting can improve invocation reliability. Option B misstates context isolation: context must be included in the spawning prompt, but that issue occurs after an invocation is attempted and does not explain the absence of all subagent executions. Option D would normally produce truncation evidence or incomplete output rather than consistent verbal promises with no tool call. The configuration should therefore permit Agent, explicitly request delegation where necessary, and log subagent invocation events.

================

The root cause is an incomplete tool contract. Claude sees that process_refund requires an order_id , but the tool description does not explain the parameter’s trusted source or the prerequisite lookup sequence. Option A makes the dependency explicit and prohibits fabricated values.

Anthropic states that tool descriptions should explain what a tool does, when it should be used, how it behaves, and any important limitations. Its prompting guidance also states that dependent tool calls must be executed sequentially and that Claude must never use placeholders or guess missing tool parameters. ( https://platform.claude.com/docs/en/agents-and-tools/tool-use/define-tools )

Option B merely forces some tool use; it does not force the correct tool or prevent a fabricated identifier. Option C is an essential defense-in-depth control, but it detects the invalid ID after the model has already made the faulty call. It does not correct the selection logic that caused the fabrication. Option D works only when the customer actually provides an identifier and introduces an unnecessary preprocessing dependency.

The process_refund schema should describe order_id as a verified identifier returned by lookup_order , and the tool description should state that the tool is unavailable until the corresponding order has been retrieved and eligibility established.

Official references/topics: Tool descriptions, prerequisite tool calls, parameter provenance, sequential tool orchestration.