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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering & Structured Output | 20% | - System prompt design and persona alignment - Explicit criteria definition and few-shot prompting - Validation, parsing and retry loop strategies - JSON schema design and structured output enforcement |
| Topic 2: Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool distribution and permission controls - Tool schema design and interface boundaries - Error handling and tool response formatting - MCP tool, resource and prompt implementation |
| Topic 3: Claude Code Configuration & Workflows | 20% | - Path-specific rules and .claude/rules/ configuration - CI/CD integration and non-interactive mode parameters - CLAUDE.md hierarchy, precedence and @import rules - Custom slash commands and plan mode vs direct execution - Hooks vs advisory instructions |
| Topic 4: Agentic Architecture & Orchestration | 27% | - Agentic loop design and stop_reason handling - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Task decomposition and dynamic subagent selection - Session state management and workflow enforcement - Error recovery, guardrails and safety patterns |
| Topic 5: Context Management & Reliability | 15% | - Context pruning and summarization strategies - Context window optimization and prioritization - Idempotency, consistency and failure resilience - Token budget management and cost control |
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NEW QUESTION # 49
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.
In production, you observe that simple fact-checking queries, such as "In what year was the Paris Climate Agreement signed?", traverse all four subagents sequentially, consuming more than 40 seconds and significant tokens per query. Complex comparative research benefits from the complete pipeline. Your query distribution is diverse and continues to evolve as users discover new applications.
What is the most effective approach to optimize for varying query complexity?
Answer: C
Explanation:
Option D allows orchestration effort to scale with the actual request. A simple factual query may require only the web-search agent and a direct coordinator response, whereas a comparative investigation may require web research, document analysis, synthesis, and report generation.
Anthropic's Building Effective AI Agents describes the orchestrator-workers pattern as a central model dynamically identifying subtasks, delegating them, and combining the results. It is specifically appropriate when the required subtasks cannot be predicted reliably in advance.
Anthropic's multi- agent research architecture likewise emphasizes varying the number of agents and tool calls according to task complexity.
NEW QUESTION # 50
When the agent calls lookup_order and receives order details showing the item was purchased
45 days ago, how does the agentic loop determine whether to call process_refund or escalate_to_human next?
Answer: D
Explanation:
In an agentic loop, tool results are returned to the model as context. The model then reasons over the updated information, such as the purchase age, and decides the next appropriate action based on the available tools, policies, and task objective.
NEW QUESTION # 51
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
After deployment, you find that 12% of extractions contain semantic errors that pass JSON Schema validation--for example, a duration such as "30 minutes" is incorrectly placed in an ingredient-quantity field. Human reviewers have the capacity to check only 20% of extractions.
Which approach most effectively allocates reviewer attention?
Answer: C
Explanation:
Option A uses the limited review capacity as a risk-ranking mechanism rather than spending it uniformly. JSON Schema validation can prove that an output has the expected types and structure, but it cannot detect a semantically misplaced value such as a duration stored as an ingredient quantity. Field-level confidence can identify the exact parts of a record that warrant inspection, provided those scores are calibrated against labeled examples rather than trusted at face value. The labeled validation set must mirror the production distribution and include edge cases; Anthropic's evaluation guidance explicitly recommends task-specific evaluations, representative distributions, measurable criteria, and automated grading where possible.
Calibration then converts raw confidence into an empirically tested estimate of error risk, allowing reviewers to inspect the lowest- confidence records or fields first.
NEW QUESTION # 52
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.
Your agent is handling a billing dispute. After calling get_customer and lookup_order , it identifies that the dispute involves a promotional pricing error requiring manager approval-beyond the agent's authorization level.
How should the workflow handle this mid-process escalation?
Answer: B
Explanation:
A mid-process escalation should transfer the decision-ready state accumulated by the agent. The human reviewer needs the verified customer identity, relevant order information, the promotional-pricing discrepancy, the reason approval is required, and any actions already attempted. Option B preserves this information in a concise, structured handoff while avoiding unnecessary repetition of the complete raw transcript.
Anthropic's tool-design guidance recommends returning high-signal information and stable identifiers containing only what Claude or the next workflow participant needs to determine the next action. Anthropic's context-engineering guidance similarly advocates structured notes that preserve critical state and dependencies without retaining every redundant tool result. A structured escalation payload applies both principles and reduces handling time for the manager. ( https://platform.claude.com/docs/en/agents-and-tools
/tool-use/define-tools )
Option A discards the investigation already completed. Option C violates the agent's authorization boundary and risks an impermissible financial action. Option D provides auditability, but a reference ID alone forces the human to reconstruct the case from an excessively broad transcript. Human control must remain meaningful when an agent encounters a decision outside its authority; the agent should pause and hand the decision back with sufficient supporting context. ( https://www.anthropic.com/research/trustworthy-agents ) Official references/topics: Structured agent handoffs, high-signal tool results, human-control boundaries, persistent structured state.
NEW QUESTION # 53
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 synthesis agent completes its initial pass but flags that three key research questions remain unanswered because the web-search and document-analysis agents did not find relevant information on those specific subtopics. The coordinator currently proceeds directly to report generation, producing reports with incomplete coverage.
What change would most effectively improve research completeness?
Answer: A
Explanation:
Option B introduces an evaluator-and-refinement loop at the correct orchestration layer. The coordinator already owns the research plan and delegation decisions, so it should inspect the synthesis result against the required questions, identify coverage gaps, and issue focused follow-up assignments. Anthropic's description of its multi-agent research system follows this pattern: the lead agent synthesizes returned findings, determines whether additional research is required, and creates new subagents or refines its strategy before producing the final result. Increasing the initial query breadth, option A, may generate additional irrelevant material and cannot guarantee that unforeseen gaps will be covered. Option C merely documents the incompleteness instead of correcting it. Option D weakens role separation by giving the synthesis agent search capabilities, increasing tool complexity and bypassing the coordinator's centralized tracking. Targeted re- delegation preserves specialized responsibilities and creates an observable sequence of research, evaluation, refinement, and resynthesis. The coordinator should also maintain explicit coverage criteria and limit the number of refinement rounds so the system improves completeness without entering an uncontrolled research loop.
NEW QUESTION # 54
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