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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Context Management & Reliability | 15% | - Context handling
|
| Topic 2: Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Topic 3: Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Topic 4: Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Topic 5: Tool Design & MCP Integration | 18% | - Tool integration
|
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NEW QUESTION # 121
You are building a structured data-extraction system using Claude. The system extracts information from unstructured documents, validates output against JSON schemas, and integrates the results with downstream systems.
Monitoring reveals that specifications sometimes appear inconsistently within source documents. For example, a summary section might state "Battery: 4000 mAh," while the detailed specifications table states
"Battery: 4200 mAh." Your current schema contains a single battery_capacity field.
This inconsistency occurs in approximately 15% of documents, and historical analysis confirms that the detailed specifications table is accurate 90% of the time.
What is the most effective approach?
Answer: D
Explanation:
Option C converts an empirically validated source hierarchy into an explicit extraction rule. The downstream contract requires one battery_capacity value, and historical analysis establishes that the detailed specifications table is substantially more reliable than the summary section. Claude should therefore be instructed to inspect all occurrences, detect conflicts, and select the detailed-table value when the two locations disagree.
Anthropic's prompting guidance emphasizes clear, direct instructions, relevant context, and explicit decision rules when order or completeness matters. Providing the reason for the precedence rule also helps the model generalize it to comparable specification conflicts. Option A preserves every value but transfers a known and repeatable decision into downstream systems, unnecessarily changing the schema. Option B rejects approximately 15% of otherwise processable documents even though a reliable precedence policy is available.
Option D creates a costly manual-review queue for conflicts that can usually be resolved deterministically.
The extraction system should still retain source quotations or locations in internal provenance metadata when auditability matters, but the output field should follow the established detailed-table precedence. Anthropic prompting best practices
NEW QUESTION # 122
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, and Glob--and integrates with Model Context Protocol (MCP) servers.
Engineers frequently ask the agent to cross-reference code changes with Jira tickets during reviews--checking ticket descriptions, acceptance criteria, and recent comments. This currently requires manually copying and pasting content into conversations. The team wants the agent to access this standard Jira ticket data directly.
What is the most effective approach?
Answer: D
Explanation:
Option D uses the established integration mechanism without creating unnecessary infrastructure. Anthropic's Claude Code MCP documentation specifically recommends connecting an MCP server when users repeatedly copy information from an external system, such as an issue tracker, into conversations. Once connected, the server exposes Jira operations through named, schema-defined tools that Claude can discover and invoke directly. This allows the agent to retrieve ticket descriptions, acceptance criteria, comments, and metadata while preserving the server's authentication and access controls.
NEW QUESTION # 123
Your automated review CI jobs take 18 seconds to initialize before Claude begins analyzing code. Profiling reveals that the delay comes from automatically discovering hooks, MCP servers, plugins, skills, and multiple nested CLAUDE.md files throughout your monorepo. You need to reduce startup time while ensuring that reviews still enforce your team's coding standards, which are documented in the root-level CLAUDE.md file.
What is the most effective approach?
Answer: A
Explanation:
Claude Code's --bare option is specifically designed for faster scripted execution. It skips automatic discovery of CLAUDE.md files, hooks, skills, plugins, MCP servers, and auto-memory while retaining essential built-in capabilities such as Bash, file reading, and file editing. Because --bare also prevents automatic loading of the root CLAUDE.md, the required standards must be added explicitly.
Option C accomplishes both objectives. --append-system-prompt-file ./CLAUDE.md loads the project standards into the current invocation while preserving Claude Code's default coding-agent system prompt and tool-use guidance. According to the official Claude Code CLI reference , append flags add file contents to the default system prompt, whereas replacement flags discard that default guidance.
Option A could work functionally but duplicates the standards in every command and creates configuration drift. Option B replaces the complete default prompt, removing useful coding, safety, and tool-use instructions. Option D improves prompt-cache reuse across different machines, but it does not disable hooks, plugins, skills, MCP servers, or CLAUDE.md discovery and therefore does not directly address the measured initialization delay.
NEW QUESTION # 124
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.
After expanding the agent's MCP tools with delivery-specific capabilities ( check_delivery_status , contact_driver , issue_credit , apply_promo_code , update_delivery_address , reschedule delivery ), the total tool count has grown from 4 to 10. Your evaluation suite shows tool selection accuracy has dropped from 88% to 71%. Log analysis reveals the majority of errors involve the agent selecting between semantically overlapping tools - calling issue_credit when process_refund was correct, and calling check_delivery_status when lookup_order already returns the needed data. Which approach structurally eliminates the semantic overlap identified in the logs as the error source?
Answer: B
Explanation:
Merging issue_credit and process_refund into one compensation tool, and folding delivery-status lookup into lookup_order, removes competing tools with overlapping purposes. This directly eliminates the ambiguity causing incorrect tool selection, rather than merely hiding tools, routing between agents, or adding examples.
NEW QUESTION # 125
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: D
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 # 126
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