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| Section | Weight | Objectives |
|---|---|---|
| Model Selection and Optimization | 16.8% | - Model Selection
|
| Prompt and Context Engineering | 11% | - Context Engineering
|
| Tools and MCPs | 10.6% | - Tool Development and Integration
|
| Claude Code | 3.1% | - Claude Code Configuration and Usage
|
| Security and Safety | 8.1% | - Safety and Guardrails
|
| Eval, Testing, and Debugging | 2.6% | - Evaluation
|
| Applications and Integration | 33.1% | - Application Development and Integration
|
| Agents and Workflows | 14.7% | - Claude Agent SDK and Agent Loops
|
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NEW QUESTION # 26
The team is debating whether to integrate with the Claude API directly or through a third-party abstraction layer that supports multiple LLM providers. The team has identified that all current and projected use cases run on Claude, no internal customer has requested LLM portability, and the team's product roadmap does not mention multi-provider support over the next two years. The third-party abstraction would add roughly 15 percent overhead in code complexity and introduce one additional dependency.
Which integration approach would you recommend?
Answer: C
Explanation:
D follows the principle of choosing the simplest architecture that satisfies demonstrated requirements .
The supplied examination item explicitly marks D. In this scenario, portability is neither a present functional requirement nor a foreseeable roadmap requirement. Adding an abstraction therefore creates measurable complexity without delivering an identified product capability.
Anthropic provides official general-purpose SDKs for Claude in multiple languages. These expose the Messages API directly while adding idiomatic interfaces, type safety, streaming support, retries, and error handling. Anthropic separately describes compatibility layers and framework-specific libraries as alternative integration surfaces rather than requirements for ordinary Claude development.
A third-party abstraction becomes rational when there is a genuine requirement for multiple providers, vendor switching, standardized cross-model interfaces, or an existing architectural platform that mandates it. None exists here. Building that flexibility speculatively increases dependencies, debugging surface, version- compatibility work, and the possibility that provider-specific Claude capabilities are hidden behind a lowest- common-denominator API.
A optimizes for hypothetical future requirements. B doubles operational complexity. C creates an even larger maintenance burden.
Relevant Claude Developer topics: Claude App Design, SDK selection, abstraction boundaries, dependency management, YAGNI, integration architecture, and provider portability requirements .
NEW QUESTION # 27
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: C
Explanation:
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.
NEW QUESTION # 28
You are designing an agent that handles a complex claim-processing workflow. Each claim moves through fact extraction, eligibility evaluation, and a decision step. The three subtasks have distinct success criteria, and some claims require iteration between fact extraction and eligibility evaluation before a decision can be reached.
Which agent pattern would you apply?
Answer: D
Explanation:
A is correct because the workflow is state-dependent, non-linear, and iterative . The source explicitly identifies the graph-based pattern as the intended architecture for this scenario. Each stage-fact extraction, eligibility evaluation, and decision-making-has its own completion criteria, and the process may need to move backward from eligibility evaluation to fact extraction when information is incomplete. A graph representation naturally models these conditional transitions and loops.
Anthropic's current orchestration guidance supports workflows containing branching, loops, filtering, staged execution, and state-dependent control flow , rather than forcing every task through one fixed sequence.
Dynamic workflow orchestration can use explicit control logic so the next processing stage depends on current state and previous results.
B concerns progressive output delivery, not workflow-state transitions. C provides a generic agentic tool loop but does not explicitly model distinct states or transition criteria. D is unsuitable because it prohibits the required return path between extraction and eligibility evaluation.
Therefore, a graph-based architecture provides the necessary conditional routing, iteration, and stage-specific validation.
Relevant Claude Developer topics: Agent Patterns, graph workflows, state transitions, conditional branching, loops, stage-specific success criteria, and agent orchestration .
NEW QUESTION # 29
You are designing a Claude application that maintains user sessions across multi-turn conversations. The product team has asked how the application will handle session lifecycle: when sessions should expire, how state is reset, and how the application avoids carrying stale context into new conversations.
How would you design session lifecycle?
Answer: B
Explanation:
A is the correct lifecycle architecture. The supplied Claude Certified Developer Foundations item explicitly selects A . A session is a state boundary, so its lifecycle must define when accumulated conversation state remains valid and when that state must be discarded. Anthropic's Managed Agents documentation establishes that a session maintains conversation history across multiple interactions and provides explicit session operations including creation, update, archive, and deletion.
A therefore addresses all three required controls: expiration , reset , and fresh-session creation . Expiration prevents indefinitely retained conversational state. Reset triggers allow the application to clear state when events such as logout, workflow completion, tenant switching, or explicit user reset occur. Fresh-session rules prevent context belonging to an earlier task from influencing a logically unrelated conversation.
B relies exclusively on timeout and does not cover event-driven resets. C places lifecycle correctness on users instead of the application. D applies global reset behavior and omits independent per-session expiration semantics.
Relevant Claude Developer topics: Agent Architecture, session lifecycle, multi-turn state, context boundaries, state reset, stale-context prevention, and session management .
NEW QUESTION # 30
Your Claude application uses tool calling to fetch patient data and generate summary reports. The flow occasionally fails because the model returns a tool_use block that references arguments not present in the schema, and your application code does not handle this case gracefully.
How would you address this?
Answer: B
Explanation:
The question presents a trust-boundary failure: model-generated tool arguments are being accepted without sufficient validation before an external operation executes. A tool call should therefore be treated like input arriving at any typed application interface-the application must ensure that the tool name and arguments satisfy the declared contract before dispatch.
Anthropic describes tool use as a contract in which the application defines the tool's schema and Claude returns structured tool_use input. Current Claude functionality also supports strict: true, which guarantees schema-compliant tool inputs through constrained generation and specifically addresses invented parameters, incompatible types, and missing required fields. For applications not using strict tool use, explicit application- side validation and a controlled error path remain essential.
B knowingly executes invalid input. C relies on uncontrolled repetition rather than deterministic validation and bounded recovery. D eliminates a useful capability instead of correcting its execution boundary.
Therefore, A provides the correct defensive design. Relevant Study Guide topics: tool calling, tool_use, input_schema, strict tool use, schema validation, defensive execution, error handling, and secure tool dispatch.
NEW QUESTION # 31
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