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| Section | Weight | Objectives |
|---|---|---|
| Agentic Architecture & Orchestration | 27% | - Designing agentic systems and workflows - Selecting appropriate Claude architectures - Agent coordination and orchestration patterns |
| Prompt Engineering & Structured Output | 20% | - Structured output generation and validation - Improving Claude response quality and consistency - Prompt design strategies |
| Claude Code Configuration & Workflows | 20% | - Integrating Claude Code into development processes - Claude Code usage and configuration - Developer productivity workflows |
| Tool Design & MCP Integration | 18% | - Designing effective tools for Claude applications - Model Context Protocol (MCP) concepts and integration - Tool safety, reliability, and usability |
| Context Management & Reliability | 15% | - Evaluation and reliability strategies - Managing context windows and information flow - Production deployment considerations |
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NEW QUESTION # 38
A team wants to reduce hallucinations without significantly changing application logic. Which improvement should be implemented FIRST?
Answer: D
Explanation:
Grounding Claude with trusted retrieved information is one of the most effective methods for reducing hallucinations. It provides current, authoritative context while requiring relatively small architectural changes compared with retraining or redesigning the application.
NEW QUESTION # 39
You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
The agent verifies customer identity through a multi-step process before resetting passwords. During testing, you notice that after the customer answers the third verification question, the agent asks them to provide their name again, as if the earlier exchange never happened.
What's the most likely cause of this behavior?
Answer: A
Explanation:
The Claude Messages API is stateless. Each API request must include the conversation history that Claude requires for the current response. If the application sends only the customer's third verification answer, Claude does not automatically retrieve the earlier turns containing the customer's name and previous answers.
From the model's perspective, that information is absent.
Anthropic's Messages API documentation states that applications must send the full conversational history to build a multi-turn interaction. The correct implementation appends each user message and assistant response to the messages array and resends the accumulated sequence on every subsequent request. ( https://platform.
claude.com/docs/en/build-with-claude/working-with-messages )
Option A cannot restore information that was omitted from the request. Instructions to "remember" do not create server-side conversational state. Option C describes application-specific behavior for which the scenario provides no evidence. Option D is factually incorrect: Claude does not have a default two-turn retention limit. Its effective conversational awareness depends on the messages provided and the model's context window.
For a verification workflow, the application should also persist structured verification state separately from prose history. That state may include completed checks, pending checks, attempt counts, and a verified customer identifier. This improves reliability while the conversational transcript preserves the natural interaction.
Official references/topics: Stateless Messages API, multi-turn history construction, structured workflow state, conversational continuity.
NEW QUESTION # 40
Your automated review calls the Claude API for each pull request, using tool_use with a report_findings tool that returns a JSON array of finding objects. Each object contains file_path, line_number, severity, category, and description. During testing on a large pull request touching more than 30 files, the response reaches the max_tokens limit and is truncated in the middle of the JSON, causing your pipeline's parser to fail. What is the most effective way to handle this?
Answer: B
Explanation:
Option A reduces the maximum output required from any single response while preserving the structured schema and complete severity range. The pipeline can partition files into coherent groups, execute bounded reviews, validate each returned array, and merge and deduplicate findings using stable fields such as file path, line number, category, and description.
Anthropic's stop-reason documentation confirms that max_tokens means generation reached the configured output limit and the response must be treated as incomplete. Structured output constraints can guarantee schema-valid generation when completion succeeds, but they cannot create unlimited output capacity. A large findings array can still exceed the available token budget.
NEW QUESTION # 41
You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
An engineer asks your agent to identify untested code paths in a legacy payment processing module spanning 45 files. After reading the first 8 source files, the agent's responses are becoming noticeably less accurate - it's forgetting previously discussed code patterns and hasn't yet located all test files or traced critical payment flows. What's the most effective approach to complete this investigation?
Answer: C
Explanation:
Subagents isolate detailed exploration in separate context windows and return concise findings to the main agent. This prevents further context degradation while preserving high-level coordination across test discovery and payment-flow tracing.
NEW QUESTION # 42
You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
The coordinator agent has AgentDefinition objects configured for all four specialized subagents, each with appropriate descriptions, prompts, and tool restrictions. During testing, you notice that the coordinator correctly reasons about when to delegate-it generates messages such as, "I'll ask the web-search agent to find sources on this topic"-but no subagent execution ever occurs. The coordinator then proceeds as if the delegation happened and continues with incomplete information. Logs show no errors.
What is the most likely cause?
Answer: C
Explanation:
Option C matches the distinction between reasoning about delegation and executing it. Defining subagents makes their descriptions available for selection, but the coordinator must still invoke the SDK's subagent- spawning tool. Current Claude Agent SDK documentation calls this the Agent tool; Task was its earlier name and remains relevant to older SDK configurations. Anthropic's Subagents in the SDK documentation instructs developers to include Agent in allowedTools so subagent invocations are approved automatically. Without that permission, an invocation can fall through to a permission callback or be denied under a non-interactive permission mode. Option A is unlikely because the configured subagent descriptions already tell Claude when each agent should be selected, although explicit prompting can improve invocation reliability. Option B misstates context isolation: context must be included in the spawning prompt, but that issue occurs after an invocation is attempted and does not explain the absence of all subagent executions. Option D would normally produce truncation evidence or incomplete output rather than consistent verbal promises with no tool call. The configuration should therefore permit Agent, explicitly request delegation where necessary, and log subagent invocation events.
NEW QUESTION # 43
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