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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Claude Code Configuration & Workflows | 20% | - CLAUDE.md hierarchy, precedence and @import rules - Path-specific rules and .claude/rules/ configuration - Custom slash commands and plan mode vs direct execution - Hooks vs advisory instructions - CI/CD integration and non-interactive mode parameters |
| Topic 2: 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 |
| Topic 3: Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - MCP tool, resource and prompt implementation - Tool schema design and interface boundaries - Error handling and tool response formatting - Tool distribution and permission controls |
| Topic 4: Prompt Engineering & Structured Output | 20% | - Explicit criteria definition and few-shot prompting - JSON schema design and structured output enforcement - Validation, parsing and retry loop strategies - System prompt design and persona alignment |
| Topic 5: Agentic Architecture & Orchestration | 27% | - Session state management and workflow enforcement - Task decomposition and dynamic subagent selection - Agentic loop design and stop_reason handling - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Error recovery, guardrails and safety patterns |
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NEW QUESTION # 11
Your product search tool queries an external catalog API and returns matching items. In production, you observe the agent frequently retries searches immediately after receiving zero results, treating "no matches found" as a failure requiring retry. The external API returns HTTP 200 with an empty results array - a valid response. How should you restructure the tool's result to help the agent correctly interpret empty result sets?
Answer: A
Explanation:
A zero-result search is a successful tool execution with an empty result set, not an error.
Returning a structured success indicator with the results array helps the agent distinguish valid
"no matches found" outcomes from actual tool or API failures, preventing unnecessary retries.
NEW QUESTION # 12
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 process_refund tool returns two types of errors: technical errors ("503 Service Unavailable",
"Connection timeout") that are transient (~5% of calls), and business errors ("Order exceeds 30-day return window", "Item already refunded") that are permanent (~12% of calls). Monitoring shows the agent wastes 3-
4 turns retrying business errors that can never succeed. Currently, both error types return only a plain text message to Claude.
What's the most effective way to reduce wasted retries while improving customer-facing response quality?
Answer: B
Explanation:
The error response must explicitly communicate both control semantics and user-facing meaning. Setting
"retriable": false tells the agent that repeating the same operation cannot change the outcome. Providing a customer-friendly explanation allows Claude to respond accurately without exposing internal implementation details or inventing its own interpretation of the business rule.
Anthropic recommends returning failed tool operations with an error indicator and sufficiently informative content so Claude can decide whether to retry, request another input, or explain the limitation. Tool interfaces should provide detailed descriptions and structured parameters rather than forcing Claude to infer operational behavior from ambiguous text. ( https://platform.claude.com/docs/en/agents-and-tools/tool-use/build-a-tool- using-agent?utm_source=chatgpt.com ) Option A appropriately limits automatic retries to technical failures, but it does not solve the stated customer- response problem unless business failures also carry clear semantics. Option B relies on fragile parsing of human-readable messages. Option C adds latency and another tool dependency, and eligibility may still change or fail for reasons not covered by the preliminary check.
A complete schema should identify the error code, category, retryability, customer-safe explanation, and permitted next actions. Technical errors can similarly return "retriable": true with retry guidance and a maximum-attempt policy.
Official references/topics: Structured tool errors, retryability classification, customer-safe explanations, resilient MCP contracts.
NEW QUESTION # 13
When implementing your lookup_order MCP tool, the backend sometimes returns errors (e.g.,
"Order not found" or temporary database failures). What is the correct pattern for communicating these errors back to the agent?
Answer: D
Explanation:
Returning structured tool results with isError set to true allows the agent to receive explicit error signals while preserving the error message. This enables the agent to reason about the failure, choose appropriate recovery actions, and provide meaningful feedback to the user.
NEW QUESTION # 14
You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline.
The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
Your automated code review is missing genuine bugs in pull requests. Investigation reveals that your review prompt includes the instruction: "Only flag critical issues that would definitely cause production failures.
Ignore minor concerns and anything you are uncertain about." Developers confirm that some missed bugs are genuine logic errors that the model investigated but chose not to report. The team requires the review output to remain structured, with each finding tagged with metadata, and actionable.
Which prompt change both removes the cause of the suppressed findings and preserves structured, tagged output for downstream filtering?
Answer: D
Explanation:
Option B removes the prompt-level suppression responsible for the false negatives while preserving machine- readable metadata. Anthropic's current code-review prompting guidance warns that instructions such as "only report high-severity issues" or "be conservative" may be followed literally: Claude can identify genuine defects during analysis but omit them from its output. Anthropic recommends requesting all findings and applying filtering separately.
Confidence and severity fields allow downstream code to apply adjustable thresholds without forcing the model to discard evidence during generation. A schema can require fields such as file, line, description, severity, confidence, evidence, and recommended action; Anthropic's Structured Outputs documentation supports enforcing such a response contract. Option A repeats the same suppressive instruction and is likely to reproduce the same omissions. Option C removes the explicit reporting structure and leaves filtering behavior undefined. Option D may improve analysis depth, but extended reasoning does not override a direct instruction to suppress uncertain findings. Separating detection from deterministic filtering preserves recall, structure, and operational control.
NEW QUESTION # 15
When researching "renewable-energy adoption," the web-search agent returns recent statistics showing 35% adoption in 2024, while the document-analysis agent extracts an 18% adoption figure from an internal 2021 report. The synthesis agent incorrectly treats the figures as contradictory instead of recognizing that they may show growth over time. What change would best enable the synthesis agent to interpret such temporal differences correctly?
Answer: B
Explanation:
The two statistics cannot be compared correctly without temporal metadata. Option A preserves when each value was measured, not merely when its document was retrieved. The structured record should ideally distinguish publication date, observation period, geographic scope, population, methodology, unit, and metric definition. The synthesis agent can then determine whether the figures represent a trend, conflicting measurements of the same period, or incomparable populations.
Anthropic's multi-agent research guidance emphasizes specifying output formats and source requirements so downstream agents receive the context required for accurate synthesis. Its structured-output capability enables these provenance fields to be required and schema validated.
Option B removes valuable historical evidence and still does not guarantee that returned sources describe the same measurement period. Option C assumes newer automatically means more reliable and could discard an authoritative historical baseline. Option D preserves older material but imposes an interpretation without examining whether methodology, geography, or definitions changed. Explicit temporal and methodological metadata allows the synthesis agent to state a defensible conclusion such as apparent growth from 18% in
2021 to 35% in 2024 while preserving any comparability caveats.
NEW QUESTION # 16
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