Your team's Claude application has been in production for a year, and the team has decided to formalize its testing strategy. Currently, the team writes ad-hoc tests for individual features but has no overall testing approach.What testing approach would you formalize?
Answer(s): B
Option B establishes a layered testing strategy rather than relying on a single testing granularity. Unit tests validate deterministic functions and isolated components quickly. Integration tests verify boundaries between application code and Claude-related components such as API clients, tool execution, parsing, persistence, and error handling. End-to-end tests then validate the most important user workflows across the complete application stack.This separation is particularly useful for Claude applications because deterministic software failures and probabilistic model-quality failures should not be treated identically. Anthropic's evaluation guidance recommends defining specific, measurable success criteria and constructing representative test cases to determine whether model behavior meets those criteria. These evaluations complement conventional software tests rather than replacing them.A focuses exclusively on test-driven development; TDD can be valuable but does not define all required test levels. C preserves the existing ad-hoc methodology rather than establishing a repeatable quality strategy. D overuses expensive and slower end-to-end tests while omitting the faster diagnostic value of unit and integration tests.The supplied exam source marks B as correct. Relevant topics: SW Eng Foundations, test strategy, unit testing, integration testing, end-to-end testing, Claude evaluations, regression coverage, and production reliability.
A teammate is reviewing the team's threat model for a Claude application and has asked you to identify the categories of AI-specific threats that the model should cover. The teammate has already listed traditional web application threats and wants to know what additional categories apply to a Claude application.Which AI-specific threat categories would you add?
Answer(s): C
Option C correctly identifies threat categories introduced or significantly amplified by LLM-based application architecture. Prompt injection attempts to manipulate Claude into following adversarial instructions. Jailbreaks seek to circumvent behavioral or application safeguards. Data leakage can expose confidential information contained in system prompts, retrieved context, tool results, or conversation state. Unsafe model output becomes particularly serious when output is consumed by downstream systems or translated into tool actions.Anthropic's official guardrail documentation explicitly distinguishes jailbreaks and direct prompt injection from indirect prompt injection. It recommends input screening, hardened system prompts, structured handling of untrusted tool content, least-privilege access, output screening, and continuous monitoring. Anthropic also warns that sensitive prompt/context data can leak and recommends post-processing and output filtering where required.Traditional threats such as SQL injection or XSS remain relevant to the surrounding application, but they are not the additional AI-specific categories requested. B similarly describes conventional infrastructure threats. D is far too narrow because the SDK is only one component of the attack surface.The supplied exam source explicitly marks C. Relevant topics: Claude App Design, threat modeling, prompt injection, jailbreaks, data leakage, output safety, least privilege, and defense in depth.
Your Claude application uses structured output that is consumed by downstream code. The team wants to handle malformed or unexpected output gracefully so it does not crash downstream systems.The best choice for handling this issue would be to...
Answer(s): D
Option D applies a fundamental production engineering principle: treat externally generated data as potentially malformed and parse it defensively before use. Downstream application logic should not assume that every field exists, every type is correct, or every unexpected property can safely be ignored. Instead, the parser should validate expected structures, handle optional or missing values deliberately, reject incompatible types, and convert failures into controlled application errors rather than process crashes.Anthropic's Structured Outputs documentation identifies exactly these failure classes for unconstrained model output: malformed JSON, missing required fields, inconsistent types, and schema violations can break downstream applications. Current Structured Outputs and strict tool-use capabilities can eliminate many schema-level failures through constrained decoding, but defensive handling remains an important software boundary when unexpected data can still arise from external services, legacy paths, or semantic validation requirements.A creates unnecessary outages. B hides failures and discards potentially recoverable data without observability. C deliberately postpones a reliability requirement until after deployment.The supplied question identifies D as correct. Relevant topics: Claude App Design, defensive programming, structured output, schema validation, parsing, error handling, downstream reliability, and type safety.
You are designing a Claude application that will process customer support tickets in two stages: a triage stage that classifies tickets and a response stage that drafts replies. The team is debating whether to use a single Claude call that handles both stages or separate Claude calls for each stage.How would you structure the application?
Option C follows a prompt-chaining/workflow decomposition pattern. Ticket classification and reply generation are materially different tasks. Triage may require a constrained category, priority, or routing decision, while response generation requires richer context, tone, policy compliance, and customer-facing language. Splitting the stages allows each Claude call to use a prompt, context set, model configuration, output format, and evaluation criterion appropriate to that specific responsibility.Anthropic's guidance on effective agentic systems distinguishes predictable workflows from open-ended agents and recommends using the simplest architecture that meets requirements. Predefined workflows are particularly appropriate where work can be divided into clear processing stages.This decomposition also creates useful engineering boundaries: the triage output can be validated before becoming input to the drafting stage; triage accuracy and response quality can be evaluated independently; and either stage can later be optimized without rewriting the entire process.A unnecessarily restricts the second stage to rules. B introduces expensive parallel generation and a selection step without a stated requirement. D optimizes solely for call count while sacrificing task separation and observability.The supplied exam source marks C. Relevant topics: Agent Patterns, prompt chaining, workflow decomposition, focused prompts, stage-level evaluation, and modular Claude application design.
You are reviewing an architectural diagram for a Claude-powered travel-booking system. The diagram shows a top-level component that interprets user requests and three subordinate components that handle flights, hotels, and ground transportation. The top-level component is responsible for routing each request, sequencing the subordinate components, and reconciling their outputs into a final itinerary. The diagram also shows that each subordinate component has its own tool list and own short conversation history that is not shared with the others.Which architectural pattern does this diagram most closely describe?
Answer(s): A
The architecture is a manager/supervisor---or orchestrator/subagent---pattern with isolated subagent context. The defining characteristics are a coordinating top-level agent, specialized subordinate agents, delegated responsibilities, and distinct tool/context boundaries. The supervisor determines which specialist should act, sequences work where necessary, and integrates the specialists' outputs into the final result.Anthropic's published multi-agent research architecture uses this same structural principle. Its Research system employs an orchestrator-worker pattern in which a lead agent analyzes the task, creates specialized subagents, delegates separate research responsibilities, and consolidates their findings. Anthropic emphasizes that good delegation requires each subagent to receive a specific objective, task boundaries, expected output, and appropriate tools.The question additionally specifies that each travel specialist has its own conversation history and tool list. That eliminates B because context is explicitly not shared globally. C would involve fixed sequential processing rather than supervisor-directed specialization. D describes retrieval augmentation, not autonomous subordinate agents.Therefore, A precisely matches both the hierarchy and context-isolation characteristics described.The supplied examination set also marks A as the intended answer. Relevant topics: Agent Patterns, manager/supervisor architecture, orchestrator-worker systems, subagents, delegation, specialized tools, and context isolation.
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