Pass the Anthropic Claude Certified Architect CCAR-F Questions and answers with Dumpstech

The primary risk is not implementation difficulty but incomplete requirements. Production caching introduces decisions involving invalidation, stale-data tolerance, cache keys, tenant boundaries, serialization, stampede prevention, failure behavior, observability, deployment topology, and consistency expectations. Implementing Redis with a five-minute TTL before resolving these questions can produce a technically functional but operationally unsafe design.

Anthropic recommends having Claude interview the user before beginning a larger feature when important requirements remain uncertain. The AskUserQuestion workflow is intended to surface technical implementation concerns, edge cases, trade-offs, and assumptions the user may not have considered. Anthropic further recommends converting the resulting answers into a self-contained specification with explicit scope and an end-to-end verification step. ( https://code.claude.com/docs/en/best-practices )

Option B provides useful codebase context but postpones requirement discovery. Option C creates avoidable rework by allowing architecture to emerge from production failures. Option D documents uncertainty but delegates unresolved design choices during implementation, when they may already constrain the code.

After the interview produces a caching specification, Claude can enter plan mode to inspect the endpoint and map those requirements onto the existing architecture before implementation.

Official references/topics: Requirements Interviewing; AskUserQuestion; Specification Development; Edge-Case and Trade-Off Discovery.

The failure is caused by missing evidence, not insufficient reasoning over the supplied evidence. A static prompt containing only changed files cannot reliably identify callers, configuration dependencies, generated interfaces, or indirect relationships located elsewhere in the repository. Asking Claude to reason more deeply cannot recover code that it was never given.

Option A converts the review into a bounded agentic loop. Claude can search for symbol references, read relevant callers, inspect type definitions, and follow newly discovered dependencies before validating a potential defect. Anthropic describes the Claude Code agentic loop as gathering context, taking action, verifying results, and repeating based on tool feedback. A turn limit preserves predictable cost and execution time.

Option C improves coverage but imposes an arbitrary two-hop boundary and depends on the accuracy of a precomputed graph, which may omit dynamic imports, reflection, generated code, configuration references, or language-specific call relationships. Option D creates duplicated context and aggregation complexity while still limiting each reviewer to predetermined dependents. Option B changes the reasoning instructions but supplies no mechanism for checking unchanged files. Tool-enabled, just-in-time retrieval is therefore the most adaptable and reliable architecture for cross-file review.

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Option A implements an iterative orchestrator-worker loop in which synthesis is treated as an evaluation checkpoint rather than an irreversible transition to report generation. When synthesis identifies missing evidence, the coordinator can formulate focused follow-up tasks, send them to the agents with the appropriate tools, and repeat synthesis after receiving the additional findings.

Anthropic’s multi-agent research architecture follows this pattern: the lead agent synthesizes subagent findings and determines whether further research is required. If gaps remain, it creates additional subagents or refines the research strategy. This preserves specialization and centralized control.

Option B makes the incompleteness visible but does not improve research coverage. Option C may increase initial cost without guaranteeing that the specific gaps discovered during synthesis will be addressed. Option D weakens separation of concerns by giving the synthesis agent search capabilities that belong to the research specialists. Targeted redelegation is more efficient because the second research round is informed by concrete deficiencies rather than speculative breadth. It also allows the coordinator to track completeness explicitly before authorizing final report generation.

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A mandatory compliance threshold must be enforced outside probabilistic model reasoning. A PreToolUse hook can inspect every attempted process_refund call before execution, compare its amount with the $500 threshold, and deny the call when the limit is exceeded. The host application can then create the human-escalation case using the validated customer and order context.

Anthropic describes hooks as deterministic controls that ensure required actions occur instead of relying on the model to choose them. Agent SDK documentation confirms that a single denying PreToolUse hook blocks the tool call, including MCP tools matched through their qualified tool names. ( https://docs.anthropic.com/en/docs/claude-code/hooks-guide )

Options A and B improve expected behavior but cannot guarantee compliance. The stated production failure rate demonstrates that prompting alone is insufficient. Option C prevents the refund from completing, but the subsequent escalation still depends on Claude correctly interpreting the returned error and invoking the human tool. It therefore enforces the financial block but not the complete escalation requirement.

Option D enforces both sides of the policy at the orchestration boundary: deny the unauthorized action and route the case to an approved human process. The escalation action should be idempotent and auditable.

Official references/topics: PreToolUse hooks, deterministic policy enforcement, MCP tool interception, human-approval controls.

Option C is the strongest choice among the listed approaches because it moves structured data generation from unconstrained response text into a schema-governed interface. Prompt-only JSON instructions, regular-expression extraction, and corrective retries are probabilistic recovery mechanisms: they may reduce failures, but none guarantees that the first response satisfies the contract. Anthropic now provides two schema-constrained mechanisms: JSON Structured Outputs through output_config.format and strict tool use through a tool definition with strict: true. Strict tool use uses grammar-constrained sampling so the tool input conforms to the declared JSON Schema, including required properties and data types. Therefore, the production implementation represented by option C should define the extraction tool’s input_schema and enable strict mode. The application reads the tool_use block as the extracted record; it does not need to scrape prose or search for braces. Option A remains vulnerable to preambles or malformed output. Option B can select a JSON-looking substring but cannot establish schema validity. Option D adds latency and cost and can still fail repeatedly. The schema-constrained tool contract is consequently the most reliable architecture available in these options.

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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.

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Option C gives the reviewer access to the evidence currently missing from its prompt. An agentic review can use Grep, Glob, Read, language-server tools, and test commands to locate callers, follow imports, inspect type definitions, and verify whether a suspected compatibility issue actually exists. A turn limit controls cost while still allowing targeted exploration.

Anthropic’s context-engineering guidance recommends just-in-time retrieval: agents should retain lightweight references and dynamically load the information needed for the current task instead of preloading a large fixed context. Anthropic has also reported that code-review performance improves when the necessary repositories are available for gathering complete context. Option A may load many irrelevant files while still missing reflection, generated interfaces, dependency injection, or indirect call paths beyond the arbitrary two-hop boundary. Option B cannot reason reliably about code that the model cannot inspect. Option D improves coverage but fragments repository-wide relationships and may duplicate substantial work. An agentic loop can begin with the diff, identify uncertain cross-file effects, search only relevant paths, and report findings only after locating supporting code. Anthropic context-engineering guidance , Claude Code quality report

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Claude selects tools principally from their names, descriptions, parameter schemas, and the task context. The current description—“Analyzes dependency graph”—does not explain why the MCP tool is superior to a familiar text search. It omits the tool’s scope, the circumstances in which it should be selected, and the structured information it returns.

Anthropic identifies detailed descriptions as the most important factor in tool-use performance. A strong description should state what the tool does, when it should and should not be used, what its parameters mean, and any limitations. Anthropic recommends several sentences for complex tools rather than a short generic label. ( https://docs.anthropic.com/en/docs/agents-and-tools/tool-use/implement-tool-use ) The MCP connector guidance likewise states that Claude selects among available tools using their names and descriptions and that clear, specific descriptions improve selection accuracy. ( https://docs.anthropic.com/en/docs/agents-and-tools/mcp-connector )

Option B makes the functional distinction explicit: Grep locates textual import statements, whereas analyze_dependencies constructs a semantic graph containing direct and transitive dependencies, cycles, and potentially unresolved references. Option A is a brittle global override. Option C removes a generally useful tool. Option D increases the number of tools and selection ambiguity, contrary to Anthropic’s recommendation to consolidate related operations where practical.

Official references/topics: MCP Tool Discovery; Tool Descriptions; Tool Selection Accuracy; Tool-Surface Design.

Option A defines what successful research must achieve without hard-coding a brittle search procedure. The subagent receives measurable objectives—breadth, diversity, recency, and relevance—but retains authority to reformulate queries, follow promising leads, and adjust source selection when the topic does not match familiar patterns. Anthropic’s account of its multi-agent research system states that subagents need an objective, expected output format, guidance about tools and sources, and clear task boundaries. This is more informative than option B’s vague instruction, yet more adaptive than prescribing every query. Option C improves only one known failure path and still constrains the agent to a predefined procedure that may be unsuitable for emerging subjects. Option D adds orchestration complexity and a potentially incorrect classification step without solving over-prescription within either category. The coordinator should describe mandatory constraints separately from strategic guidance: authoritative-source requirements and time boundaries can remain fixed, while query formulation and exploration paths remain agent-controlled. Evaluation should then measure source coverage, evidence quality, duplication, and discovery of relevant secondary leads rather than whether the subagent followed a particular series of searches.

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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

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