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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Topic 2: Context Management & Reliability | 15% | - Context handling
|
| Topic 3: Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Topic 4: Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Topic 5: Tool Design & MCP Integration | 18% | - Tool integration
|
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NEW QUESTION # 69
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.
A customer raises three separate issues during one session: a refund inquiry (turns 1-15), a subscription question (turns 16-30), and a payment method update (turns 31-45). At turn 48, the customer asks "What happened with my refund?" The conversation is approaching context limits.
What strategy best maintains the agent's ability to address all issues throughout the session?
Answer: A
Explanation:
The agent must preserve durable state for several concurrently open issues without keeping every conversational turn in the active context window. Option D creates a structured issue ledger containing identifiers, issue type, requested action, current status, completed steps, unresolved questions, and relevant tool outputs.
Anthropic recommends structured note-taking for long-horizon agents. In this pattern, critical state is written outside the context window and retrieved later, allowing the agent to preserve dependencies and progress across dozens of tool calls. Anthropic also describes compaction as retaining critical details while discarding redundant raw messages and historical tool output. ( https://www.anthropic.com/engineering/effective-context- engineering-for-ai-agents ) Option A is better than retaining the entire transcript, but a narrative summary can merge separate issues or obscure exact identifiers and statuses. Option B will discard the refund conversation because it occurred outside the most recent 30 turns. Option C may recover current backend facts, but it cannot reconstruct conversational commitments, prior explanations, or why a particular action remains pending.
A structured context layer should maintain one record per issue and be updated after every material action.
The active prompt can include compact summaries of all open issues and expanded details only for the issue currently being discussed.
Official references/topics: Structured note-taking, external agent memory, context compaction, multi-issue state management.
NEW QUESTION # 70
The automated review consistently flags patterns your team uses intentionally-force-unwrapping optionals in test files, using large coordinator classes that follow your established architecture, and importing internally maintained modules marked as deprecated in the public SDK. Developers are dismissing approximately 30% of all findings as project-specific false positives. Which approach prevents the model from generating these findings in the first place by supplying the project's conventions as persistent context during every review?
Answer: B
Explanation:
Option D supplies the missing repository-specific knowledge before Claude evaluates the code. The project's CLAUDE.md should state that force-unwrapping is permitted within test paths, explain the coordinator-class architecture, and identify the internally maintained deprecated module as an approved dependency. These instructions should be concise and scoped carefully so exceptions do not become global permissions.
Anthropic's CLAUDE.md documentation defines project CLAUDE.md files as persistent context for coding standards, architecture, workflows, and team conventions. They are loaded at the beginning of applicable sessions, preventing the organization from repeatedly supplying the same corrections. Anthropic also recommends recording information that Claude should know whenever it works in the repository.
Option A filters generated findings after tokens have already been consumed and can hide genuine issues containing similar terminology. Option B removes valuable surrounding context and would likely increase false positives and missed cross-file defects. Option C distributes project knowledge across individual source lines, creates maintenance overhead, and requires custom preprocessing. Centralized, narrowly scoped CLAUDE.md instructions address the root cause and improve the initial review judgment instead of repairing its output afterward.
NEW QUESTION # 71
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.
A customer returns 4 hours after their initial session about the same billing dispute. The previous 32-turn session contains lookup_order results showing "Status: PENDING, Expected resolution: 24-48 hours." In testing, you observe that when resuming sessions with stale tool results, the agent often references the outdated data in responses (e.g., "I see your refund is still being processed") even after subsequent fresh tool calls return different information.
What approach most reliably handles returning customers?
Answer: D
Explanation:
Option D separates durable case history from volatile operational data. The new session receives a compact, structured summary describing the billing dispute, the customer's objective, actions previously taken, and the unresolved status. It does not inherit outdated backend observations as though they were still authoritative.
Fresh tool calls then retrieve the current refund or order state before the agent responds.
Agent SDK sessions preserve conversation history, including earlier tool calls and tool results. Resuming the complete transcript therefore reintroduces stale system data into the active context, even though the external backend may have changed substantially during the four-hour gap. Conversation persistence must not be confused with persistence of external-system truth.
Option A performs unnecessary calls to every previously used tool, including tools unrelated to the returning customer's current question. Option B relies on prompt compliance while retaining contradictory historical evidence in context. Option C manually removes tool results from an existing transcript and may damage the logical relationship between prior tool_use and tool_result blocks while still retaining a long, unstructured conversation.
A structured summary should preserve stable identifiers, previous actions, customer commitments, and unresolved issues. Time-sensitive fields such as refund status, delivery state, account balance, or expected resolution should always be refreshed through authoritative tools.
Official references/topics: Session persistence, stale tool-result management, context compaction, fresh-data retrieval.
NEW QUESTION # 72
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.
After adding an MCP server with specialized code-refactoring tools-extract_function, rename_variable, and inline_function-you notice that the agent still uses basic text manipulation through Write and Bash sed commands for refactoring tasks. The MCP server is connected and healthy. Examining the configuration, you find that each MCP tool has a minimal description such as, "extract_function: Extracts a function from code." What is the most effective way to improve adoption of the MCP refactoring tools?
Answer: D
Explanation:
Option C corrects the weak selection signal presented to the model. Claude chooses among available tools using their names, descriptions, parameter schemas, and the current request. "Extracts a function from code" does not explain whether the tool understands syntax trees, preserves imports, updates call sites, validates scope, or offers advantages over Write and sed. Anthropic identifies prompt-engineering tool descriptions as one of the most effective ways to improve agent tool use. Descriptions should state what the operation performs, when it should be selected, what inputs are required, what output it returns, and any limitations.
Option A adds a separate probabilistic routing layer without improving the tool contract Claude ultimately sees. Option B ignores the server's intended value. Option D removes a broadly useful capability and may prevent unrelated edits without guaranteeing that the MCP tools are used correctly. Each refactoring tool should instead describe its semantic behavior and contrast it with plain text manipulation-for example, that rename_variable performs scope-aware symbol renaming and updates references. Clear schemas, concrete examples, and evaluation against real refactoring tasks should accompany the improved descriptions.
NEW QUESTION # 73
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.
Your agent needs to insert a new helper function into the middle of a 150-line utility module, between two existing functions. The Edit tool fails because its old_string parameter cannot find unique text to match-the file has repetitive docstrings, variable names, and structural patterns.
What is the most reliable way to complete this insertion?
Answer: D
Explanation:
Option D is the closest match to Claude Code's documented editing procedure. The Claude Code tools reference explains that Edit performs exact string replacement and requires old_string to appear exactly once.
When the text occurs multiple times, the prescribed response is to include sufficient surrounding context to identify one occurrence uniquely. For this insertion, the match should span a distinctive boundary between the preceding function and the following function. It does not literally need 30 lines; it needs the smallest exact block that is demonstrably unique, but option D is the only answer expressing that method.
Option A would modify every occurrence of a repeated pattern and could insert the helper function multiple times. Option B places the function at the end rather than at the required architectural location. Option C can technically work, but Write replaces the complete file and increases the change surface. Anthropic explicitly states that Write creates or overwrites full files, while partial modifications should use Edit. A targeted, uniquely anchored Edit preserves all unrelated content and produces a smaller, safer diff.
NEW QUESTION # 74
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