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| Section | Weight | Objectives |
|---|---|---|
| Security and Safety | 8.1% | - Guardrails and safety controls - AI application security |
| Claude Code | 3.1% | - Claude Code configuration and usage |
| Model Selection and Optimization | 16.8% | - Latency and performance trade-offs - Claude model family characteristics - Cost and token optimization |
| Evaluation, Testing, and Debugging | 2.6% | - Error handling and debugging - Output evaluation and validation |
| Tools and Model Context Protocol (MCP) | 10.6% | - MCP server development - Tool integration and usage |
| Prompt and Context Engineering | 11% | - Prompt design and structuring - Structured output handling - Context window management |
| Applications and Integration | 33.1% | - Streaming and Batch API - SDK and third-party integration - Vision capabilities - Claude Messages API |
| Agents and Workflows | 14.7% | - Claude Agent SDK usage - Workflow vs autonomous agents - Agent architecture principles - Memory and context management |
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NEW QUESTION # 88
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: C
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 # 89
You are building a Claude application that processes 10,000 customer emails overnight to extract structured data. The work is non-interactive, runs once daily, and has a flexible completion window of several hours.
Which Claude API would you use?
Answer: A
Explanation:
Option A is correct because the Message Batches API is designed for asynchronous, high-volume processing where results do not need to be returned interactively. Anthropic's API reference states that a Message Batch can contain many independent Messages requests and may take up to 24 hours to complete. That makes it appropriate for 10,000 overnight email-extraction jobs with a several-hour completion window.
Streaming in B solves a different requirement: it exposes partial response events while a single request is being generated, which is valuable for interactive user experiences or long-running synchronous requests, but it does not provide the workload-management advantages of a batch job. C processes items sequentially and unnecessarily sacrifices throughput. D can increase throughput with concurrent real-time calls, but it adds concurrency management and rate-limit pressure when the workload explicitly tolerates asynchronous completion.
The batch design also lets each request carry a custom identifier so results can be matched back to source emails even if completion order differs. Therefore, A is the intended Claude API choice. Relevant Study Guide topics: Message Batches API, asynchronous processing, high-volume workloads, request correlation, throughput, and non-interactive application design.
NEW QUESTION # 90
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: B
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 # 91
Your Claude application has been running for several conversation turns, and you notice the model occasionally references information that was discussed many turns ago but is no longer relevant. You suspect context drift is causing the model to weight stale content too heavily.
How would you address the drift?
Answer: C
Explanation:
Option C applies the appropriate context-management technique. The issue is not simply whether the older conversation can physically fit into the context window; it is that stale details remain prominent enough to interfere with the model's current task. Effective context engineering optimizes signal quality, not merely maximum token retention.
Anthropic's context-editing documentation describes compaction as summarizing accumulated history and replacing the full history with a structured summary when context becomes large. This preserves important task state while substantially reducing low-value detail that can distract later reasoning.
A retains precisely the stale material producing the problem and therefore does not address context drift. B destroys all useful continuity between turns, including valid goals, decisions, and intermediate state. D is similarly excessive because limiting Claude to only the latest turn discards information that may still be required.
Compaction provides the correct middle ground: retain durable conclusions, current objectives, unresolved issues, and other high-signal state while compressing obsolete conversational detail. Relevant Study Guide topics: context engineering, compaction, conversation history, long-running agents, stale context, context drift, and high-signal state preservation.
NEW QUESTION # 92
Your Claude agent's hooks are currently triggered for every action, which slows down the agent significantly even when actions pose no risk. The team wants to scope hooks more carefully.
How would you scope the hooks?
Answer: D
Explanation:
Option A correctly applies selective enforcement. Claude Code hooks can execute automatically at lifecycle events such as PreToolUse, and matchers or conditions can narrow exactly which operations trigger a hook.
Anthropic's hook reference demonstrates this pattern by applying a PreToolUse hook specifically to destructive shell operations rather than indiscriminately processing every command. The documentation notes that if the matcher or conditional expression does not match, the handler is skipped, avoiding unnecessary process-spawn overhead.
That architecture is particularly appropriate for costly or security-sensitive checks. High-risk events- destructive file operations, privileged commands, production changes, or access to sensitive resources-can receive deterministic pre-execution enforcement while routine low-risk actions proceed without additional hook latency.
B creates an avoidable period in which safeguards disappear entirely. C reduces application availability without addressing the actual source of overhead. D is technically weaker because system-prompt instructions influence model behavior but are not equivalent to deterministic lifecycle interception capable of blocking execution.
Therefore, hooks should be scoped using event types, matchers, and conditions according to risk. Relevant Claude Developer topics are Claude Code hooks, agent construction, tool governance, deterministic controls, permission boundaries, safety/performance tradeoffs, and lifecycle interception.
NEW QUESTION # 93
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