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| Section | Weight | Objectives |
|---|---|---|
| Applications and Integration | 33.1% | - Streaming and Batch API - Vision capabilities - SDK and third-party integration - Claude Messages API |
| Tools and Model Context Protocol (MCP) | 10.6% | - Tool integration and usage - MCP server development |
| Agents and Workflows | 14.7% | - Workflow vs autonomous agents - Claude Agent SDK usage - Agent architecture principles - Memory and context management |
| Model Selection and Optimization | 16.8% | - Claude model family characteristics - Cost and token optimization - Latency and performance trade-offs |
| Evaluation, Testing, and Debugging | 2.6% | - Error handling and debugging - Output evaluation and validation |
| Prompt and Context Engineering | 11% | - Context window management - Structured output handling - Prompt design and structuring |
| Security and Safety | 8.1% | - AI application security - Guardrails and safety controls |
| Claude Code | 3.1% | - Claude Code configuration and usage |
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NEW QUESTION # 49
Your Claude application's prompt was written months ago and has not been updated. The team has discovered through evals that the prompt produces good results on common cases but underperforms on a specific category of inputs that has grown in volume.
How would you respond?
Answer: A
Explanation:
The correct response is iterative prompt improvement backed by evaluation , making A the appropriate choice. The supplied Claude Certified Developer Foundations material explicitly selects A. When production input distribution changes, a prompt that previously met requirements can become inadequate. The correct engineering response is not to preserve the prompt merely because it once worked; prompts are application components that should evolve with observed workload behavior.
Anthropic's official evaluation guidance describes prompt development as a cycle involving test cases, an initial prompt, iterative testing and refinement, final validation, and deployment. Success criteria should be specific and measurable, and evaluations should include representative cases that expose known failure modes.
The underperforming category should therefore be incorporated into the evaluation set. The team can modify instructions, examples, context organization, or other prompt components, then compare the revised prompt against both the newly important category and existing common cases. This prevents improvement in one segment from silently producing regressions elsewhere.
B avoids the defect instead of correcting it. C risks degrading previously successful behavior. D creates unnecessary architectural fragmentation.
Relevant Claude Developer topics: prompt iteration, evaluation-driven development, regression testing, representative test sets, prompt optimization, and production feedback loops .
NEW QUESTION # 50
You are building an agent that needs to call several internal APIs and a database in a structured, repeatable way. Your team has decided to use the Claude Agent SDK rather than build a custom loop. You are setting up the agent's tool definitions and execution loop.
How would you set up the tools and execution loop?
Answer: B
Explanation:
The supplied Claude Developer examination source selects A . The purpose of choosing an agent SDK rather than implementing a custom Messages API loop is to consume the SDK's higher-level abstractions. Re- implementing dispatch, iteration, and state handling would discard much of the value provided by the SDK.
Anthropic's current documentation distinguishes low-level tool-use loops from higher-level SDK-managed abstractions. With a manually implemented Messages API workflow, application code must inspect stop_reason, execute requested client tools, append tool_result blocks, preserve conversation history, and repeat until Claude completes the turn. Anthropic's higher-level tooling can instead encapsulate this repetitive control flow. Current migration guidance also confirms that Agent SDK @tool functions are automatically dispatched by the SDK and that agents, tools, and sessions are first-class SDK concepts.
B is incorrect because structured tool use should not be replaced with informal plain-text calls. C introduces an unnecessary custom state dependency when no external persistence requirement exists. D duplicates control-loop functionality despite the explicit decision to use the SDK.
Relevant Claude Developer topics: Agent SDK, tool definitions, dispatch, agentic loops, conversation state, tool execution, and abstraction selection .
NEW QUESTION # 51
A teammate has asked how the Claude SDK handles transient API errors, such as a temporary network issue or a brief rate-limit response. They want to know whether the application code needs to handle every transient error or whether the SDK provides any default behavior.
How would you describe the SDK's default behavior?
Answer: C
Explanation:
Option D precisely matches Anthropic's SDK behavior. Official SDK documentation states that transient conditions are automatically retried two times by default with exponential backoff. The default retry set includes connection failures, HTTP 408 Request Timeout, 409 Conflict, 429 Rate Limit, and 5xx server errors. Developers can alter or disable this behavior through the SDK's retry configuration.
This directly eliminates A because the retry count is configurable. B is incorrect because rate-limit responses are among the conditions automatically retried by the supported SDKs; the API also provides retry-after information where applicable. C is incorrect because the SDK does not merely log these failures and continue without attempting recovery.
Application-level handling is still necessary after the configured retry policy is exhausted. Production code should distinguish permanent errors from transient failures, preserve request IDs for diagnostics, and decide whether higher-level fallback, queuing, or user-visible failure handling is appropriate.
Therefore, D correctly characterizes the SDK abstraction: useful retry defaults combined with developer control. Relevant Study Guide topics: Claude SDKs, transient errors, HTTP 429, retries, exponential backoff, configuration, and resilient API integration.
NEW QUESTION # 52
Your Claude agent has too many tools, and many of them have overlapping functionality. The agent often picks an inappropriate tool when several could plausibly handle a request.
How would you address the tool selection problem?
Answer: B
Explanation:
Option D addresses the architectural cause of the failure: an ambiguous tool surface. Claude chooses tools partly from their names, descriptions, schemas, and the relationship between the request and the capability described. If multiple tools appear to perform substantially the same task, selection becomes unnecessarily difficult.
Anthropic's tool-definition guidance explicitly recommends consolidating related operations into fewer tools and making descriptions clear about both what a tool does and when it should be used. The documentation notes that fewer, more capable tools reduce selection ambiguity and make the available tool surface easier for Claude to navigate.
Option A makes the ambiguity worse by expanding an already overlapping tool set. B destroys required application capabilities. C could improve selection somewhat, because examples can clarify complex inputs, but it leaves the underlying duplication intact. Examples complement good tool design; they are not a substitute for distinct tool responsibilities.
Therefore, D is the strongest solution: remove unused tools, merge functions that represent the same conceptual operation, and write discriminative descriptions defining appropriate and inappropriate usage.
Relevant Study Guide topics: tool design, tool selection, descriptions, tool consolidation, agent construction, and reducing ambiguity.
NEW QUESTION # 53
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: B
Explanation:
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.
NEW QUESTION # 54
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