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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tool Design & MCP Integration | 18% | - Tool integration
|
| Topic 2: Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Topic 3: Context Management & Reliability | 15% | - Context handling
|
| Topic 4: Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Topic 5: Claude Code Configuration & Workflows | 20% | - Claude Code
|
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NEW QUESTION # 33
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: D
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. (
https://docs.anthropic.com/en/docs/about-claude/use-case-guides/ticket-routing ) Option A adds provenance and confidence metadata but does not define the correct extraction granularity.
Option C addresses explicitness and splitting, but the arbitrary 10-20 limit can truncate documents containing more than 20 legitimate skills or force padding when fewer than 10 appear. Option D is useful for canonicalization and deduplication, but post-processing cannot reliably remove plausible implied skills or determine how the original phrase should have been interpreted.
The strongest prompt should combine explicit rules with diverse few-shot examples covering normal cases, compound phrases, implied information, duplicates, and unusually long or short skill lists.
Official references/topics: Few-Shot Prompting, Output Consistency, Explicit Extraction Criteria, Representative Examples.
NEW QUESTION # 34
A customer contacts the agent about a warranty claim on a power drill. Resolving this requires multiple sequential tool calls: get_customer to look up their account, lookup_order to find the purchase details, and then either process_refund or escalate_to_human depending on warranty eligibility. You're implementing the agentic loop that orchestrates these steps using the Claude API. What is the primary mechanism your application uses to determine whether to continue the loop or stop?
Answer: C
Explanation:
Continue the agentic loop when stop_reason is tool_use, execute the requested tool, and return its result to Claude. Exit when Claude returns a terminal reason such as end_turn; other reasons like max_tokens require separate handling.
NEW QUESTION # 35
Your automated review generates many findings per pull request, but developer feedback shows that roughly half are dismissed as "not worth addressing." Analysis reveals that dismissed findings are often technically accurate but involve minor style preferences or patterns that are acceptable in your codebase. Before adding infrastructure complexity, what prompt-design change could most effectively reduce dismissals while maintaining the detection of genuine issues?
Answer: B
Explanation:
The problem is an undefined relevance threshold. Claude is finding technically valid observations, but the prompt does not clearly distinguish actionable defects from acceptable conventions and low-value style preferences. Option A establishes explicit positive and negative reporting criteria, allowing the model to apply the team's actual definition of a useful finding during generation.
Anthropic's prompt-engineering guidance emphasizes clear, specific instructions and well-defined success criteria. The managed Code Review documentation similarly recommends defining skipped categories, generated paths, severity rules, and evidence requirements. Reporting "bugs affecting correctness or security" while excluding "formatting handled by CI and documented local conventions" is substantially more precise than asking the model to be generally conservative.
Option B adds cost, latency, another probabilistic decision, and a new evaluation surface before improving the original prompt. Option C confuses confidence with importance: Claude may be highly confident about a trivial style observation. Option D is dangerously broad because it can suppress legitimate but uncertain bugs, reducing recall. Explicit relevance criteria address the demonstrated failure directly while preserving the model's ability to investigate and report genuine correctness or security problems.
NEW QUESTION # 36
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.
Production logs reveal inconsistent error handling: when lookup_order fails, the agent sometimes retries 5+ times (wasteful when the order ID doesn't exist), sometimes escalates immediately (premature for temporary network issues), and sometimes asks users for clarification (inappropriate when the issue is a backend permission error). Investigation shows your MCP tool returns uniform error responses: {"isError": true,
"content": [{"type": "text", "text": "Operation failed"}]} . The agent cannot distinguish between error types.
What's the most effective improvement?
Answer: D
Explanation:
The agent is behaving inconsistently because every failure is represented identically. "Operation failed" contains no information about permanence, user correctability, authorization, or whether another attempt is likely to succeed. Adding structured fields converts the error into an actionable interface contract.
Anthropic instructs tool implementations to mark failures using an error flag and return information Claude can use to retry with corrected input, ask for clarification, or explain a limitation. Tool design should expose sufficient detail for the model to choose the correct next action rather than forcing it to infer the cause from a generic message. ( https://platform.claude.com/docs/en/agents-and-tools/tool-use/build-a-tool-using-agent?
utm_source=chatgpt.com )
With error_category , isRetryable , and a causal description, the agent can retry transient network failures, request corrected identifiers for validation errors, and escalate or report permission failures without pointless repetitions. Option B retries permanent validation and permission failures unnecessarily. Option C adds another tool call and failure point merely to classify information the original tool already possesses. Option D relies on parsing variable text and examples rather than providing explicit machine-readable semantics.
The tool should preserve isError: true , return a stable structured error object, include a safe customer-facing message where appropriate, and avoid exposing internal secrets or stack traces.
Official references/topics: MCP error contracts, actionable tool results, retryability metadata, agent- computer interface design.
NEW QUESTION # 37
A prompt asks Claude to produce Markdown tables. Which addition MOST increases consistency?
Answer: A
Explanation:
Explicit structural requirements reduce ambiguity. Providing exact column names, ordering, and formatting expectations helps Claude consistently generate Markdown tables that downstream users or systems can reliably consume.
NEW QUESTION # 38
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