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| Section | Weight | Objectives |
|---|---|---|
| Security and Safety | 8.1% | - AI application security - Guardrails and safety controls |
| Prompt and Context Engineering | 11% | - Context window management - Prompt design and structuring - Structured output handling |
| Applications and Integration | 33.1% | - Vision capabilities - Claude Messages API - SDK and third-party integration - Streaming and Batch API |
| Claude Code | 3.1% | - Claude Code configuration and usage |
| Agents and Workflows | 14.7% | - Workflow vs autonomous agents - Claude Agent SDK usage - Memory and context management - Agent architecture principles |
| Tools and Model Context Protocol (MCP) | 10.6% | - MCP server development - Tool integration and usage |
| Evaluation, Testing, and Debugging | 2.6% | - Output evaluation and validation - Error handling and debugging |
| Model Selection and Optimization | 16.8% | - Latency and performance trade-offs - Claude model family characteristics - Cost and token optimization |
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NEW QUESTION # 58
Your Claude agent has access to a tool that retrieves customer records. A teammate has noticed that the agent occasionally calls the tool with arguments the schema does not declare, and the tool's downstream service returns an error each time. The teammate proposes loosening the schema so the tool accepts whatever arguments the model produces.
How would you respond?
Answer: A
Explanation:
The supplied question identifies B as the intended answer. Tool schemas are contracts between Claude and executable application code. When the downstream service accepts only a defined set of arguments, relaxing that schema merely shifts invalid data farther into the system and increases runtime failures.
Anthropic's current tooling provides an even stronger implementation of this principle through strict tool use
. Setting strict: true constrains tool inputs to the declared JSON Schema, preventing undeclared properties, missing required values, and incompatible parameter types where the supported schema subset is used.
Anthropic explicitly recommends strict tool use for validated parameters, type-safe function calls, and reliable agentic workflows.
In a non-strict or legacy implementation, the application should still validate arguments before dispatch and convert invalid calls into structured tool errors that Claude can interpret and potentially correct. A prompt instruction can reinforce behavior, but it should not replace deterministic validation. C and D weaken the system boundary and knowingly send invalid calls downstream.
Therefore, maintain the contract rather than adapting the contract to malformed model output.
Relevant Claude Developer topics: Agent Construction, tool schemas, strict tool use, JSON Schema, parameter validation, structured errors, retry behavior, and defensive execution boundaries .
NEW QUESTION # 59
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 # 60
Your Claude application runs long agentic workflows where the agent makes many tool calls, and the conversation history grows quickly. After about 20 tool calls, you notice the agent's responses become less focused and sometimes ignore earlier task constraints.
How would you address this?
Answer: D
Explanation:
Option B addresses the actual architectural failure: low-value historical material is crowding out the high- signal information required for current reasoning. A larger context window does not guarantee better attention to important constraints. Effective agent architecture actively manages what remains in context as the workflow progresses.
Anthropic's Context Editing documentation explicitly provides tool-result clearing for agentic workflows with heavy tool usage. Once Claude has processed an older result, verbose file contents, search output, or API responses may no longer need to remain in full. Older results can therefore be removed while recent and important state remains available. Anthropic also documents compaction, where accumulated history is summarized and replaced with a smaller representation that preserves important task state.
A removes an important agent capability rather than solving context growth. C retains all accumulated noise and merely gives it more space. D repeatedly destroys valuable task state and creates artificial workflow boundaries.
Therefore, B preserves goals, decisions, unresolved constraints, and necessary results while reducing irrelevant historical content. Relevant Study Guide topics: context engineering, tool-result clearing, compaction, long-running agents, context quality, and task-state preservation.
NEW QUESTION # 61
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: D
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 # 62
The product team has described a new Claude feature in business terms: "agents should help our analysts produce client memos faster." You need to convert this into actionable technical requirements for the engineering team.
Your first step would be to...
Answer: C
Explanation:
The supplied examination source identifies D as correct. The statement "produce client memos faster" is a business objective , not an implementable engineering requirement. The first engineering activity is therefore requirements decomposition: determine what functionality is required and what technical characteristics must support it.
Functional requirements might specify how analysts provide source information, what stages the agent performs, whether it researches, outlines, drafts, revises, or cites material, which systems it accesses, what output it returns, and where human approval occurs. Infrastructure and non-functional requirements then define latency, access control, confidentiality, context size, availability, auditability, model selection, integration interfaces, token cost, observability, and operational constraints.
Anthropic's developer lifecycle similarly separates defining measurable success criteria from choosing and implementing model capabilities. Its evaluation guidance emphasizes establishing specific, measurable success criteria before optimizing a model or prompt. Claude's agent configuration also separates implementation components such as the model, system prompt, tools, MCP servers, and Skills-choices that logically follow requirement definition.
A is useful discovery but prematurely assumes a prompt-driven drafting implementation. B is precedent- driven rather than requirement-driven. C selects technology before defining what the system must accomplish.
Relevant topics: Understanding Reqs, functional requirements, infrastructure requirements, non- functional requirements, acceptance criteria, and solution decomposition .
NEW QUESTION # 63
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