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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering & Structured Output | 20% | - Explicit criteria definition and few-shot prompting - JSON schema design and structured output enforcement - System prompt design and persona alignment - Validation, parsing and retry loop strategies |
| Topic 2: Agentic Architecture & Orchestration | 27% | - Task decomposition and dynamic subagent selection - Agentic loop design and stop_reason handling - Error recovery, guardrails and safety patterns - Session state management and workflow enforcement - Multi-agent patterns: coordinator-subagent and hub-and-spoke |
| Topic 3: Claude Code Configuration & Workflows | 20% | - Hooks vs advisory instructions - CI/CD integration and non-interactive mode parameters - Custom slash commands and plan mode vs direct execution - CLAUDE.md hierarchy, precedence and @import rules - Path-specific rules and .claude/rules/ configuration |
| Topic 4: Context Management & Reliability | 15% | - Context window optimization and prioritization - Idempotency, consistency and failure resilience - Token budget management and cost control - Context pruning and summarization strategies |
| Topic 5: Tool Design & MCP Integration | 18% | - Tool schema design and interface boundaries - Tool distribution and permission controls - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - MCP tool, resource and prompt implementation - Error handling and tool response formatting |
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NEW QUESTION # 97
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.
You have configured the system so that all four subagents have access to the complete set of 18 tools. During testing, agents frequently call tools outside their specialization-the synthesis agent attempts web searches, and the report generator tries to analyze documents.
What is the primary cause of this poor tool-selection behavior?
Answer: C
Explanation:
Option C identifies the architectural contradiction. A specialized subagent's role prompt says what work it should perform, but granting it every tool simultaneously signals that unrelated actions are available and potentially legitimate. The synthesis agent can therefore interpret web search as a permissible way to fill gaps, while the report generator may invoke document-analysis tools even though those responsibilities belong elsewhere. Anthropic's Agent SDK documentation describes subagents as focused workers with tailored instructions and explicitly supports restricting each subagent to specific tools; it notes that tool restrictions reduce unintended actions. There is no documented universal threshold at which choosing among 18 tools becomes unreliable, so option B invents a "four or five tools" limit. Context consumption in option A may affect efficiency, but it does not directly explain the role-specific cross-calling pattern. Option D is also incorrect because the observed behavior occurs after delegation, inside agents that already possess the tools.
The remedy is least-capability configuration: give the web agent search tools, the document agent document tools, the synthesis agent only the inputs and utilities required to combine findings, and the report agent formatting or file-output tools. Aligning role, description, and tool allowlist removes the conflicting affordances.
NEW QUESTION # 98
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.
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: A
Explanation:
In the standard Claude tool-use loop, the application executes lookup_order and sends its output back as a tool_result . That result becomes part of the conversation state available to Claude. Claude then evaluates the purchase date, refund policy, customer request, authorization constraints, and available tools before selecting the next action.
Anthropic describes client-tool orchestration as a repeated loop: Claude emits a tool_use request, the application executes it, returns a tool_result , and Claude continues reasoning from the updated conversation.
Claude, rather than the tool implementation, selects when and how to invoke the next available tool unless the application has explicitly implemented a fixed workflow. ( https://platform.claude.com/docs/en/agents-and- tools/tool-use/how-tool-use-works ) Options B and C describe possible custom orchestration architectures, but neither is stated in the scenario.
Option D is inconsistent with adaptive agent behavior because later actions depend on information that did not exist before lookup_order completed. A rigid sequence would not respond appropriately to different purchase dates, eligibility states, or order conditions.
The tool result should return high-signal fields such as purchase date, return-window status, refund eligibility, existing refund status, and stable order identifiers so Claude can make the subsequent decision accurately.
Official references/topics: Tool-result continuation, adaptive agent loops, model-directed tool selection, sequential dependency handling.
NEW QUESTION # 99
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: D
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.
Option B may postpone the failure but provides no durable guarantee for still-larger pull requests, and aggressively shortening descriptions may eliminate necessary evidence. Option C abandons machine- validated structure without reducing the amount of generated content. Option D deliberately suppresses medium- or low-severity findings and repeats an oversized request rather than addressing its scope.
Partitioning establishes predictable output bounds, supports targeted retries, retains every required finding category, and prevents a single truncated response from invalidating the complete review.
NEW QUESTION # 100
A chatbot frequently receives greetings, policy questions, and technical support requests. Which architecture improves maintainability?
Answer: C
Explanation:
Prompt routing directs requests to specialized workflows optimized for different tasks. Separate prompts and tools improve accuracy, simplify maintenance, and reduce conflicts between unrelated instructions.
NEW QUESTION # 101
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your schema includes a skills: string[] field. Production monitoring reveals three consistency issues:
(1) compound phrases like "Python and SQL" are sometimes kept as one
entry, sometimes split;
(2) implied but unstated skills occasionally appear in extractions;
(3) similar documents produce wildly different array lengths (5-10 vs
40+ entries).
Your prompt currently says "Extract all skills mentioned."
What's the most effective improvement?
Answer: A
Explanation:
The failure is caused by an underspecified extraction policy rather than an inadequate JSON structure. "Extract all skills mentioned" does not define whether coordinated phrases should be split, whether inferred capabilities qualify, or what level of specificity constitutes one skill. Few- shot examples can demonstrate all three decisions using concrete inputs and expected outputs.
Anthropic identifies examples as one of the most effective ways to improve output accuracy and consistency because they communicate behavior more precisely than abstract instructions alone.
Representative examples can show that "Python and SQL" becomes two entries, that an unstated skill must not be inferred from a job title, and that broad descriptions should not be decomposed into dozens of speculative micro-skills.
NEW QUESTION # 102
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