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| Section | Weight | Objectives |
|---|---|---|
| Claude Code Configuration & Workflows | 20% | - Developer productivity workflows - Integrating Claude Code into development processes - Claude Code usage and configuration |
| Context Management & Reliability | 15% | - Production deployment considerations - Evaluation and reliability strategies - Managing context windows and information flow |
| Agentic Architecture & Orchestration | 27% | - Agent coordination and orchestration patterns - Selecting appropriate Claude architectures - Designing agentic systems and workflows |
| Tool Design & MCP Integration | 18% | - Designing effective tools for Claude applications - Model Context Protocol (MCP) concepts and integration - Tool safety, reliability, and usability |
| Prompt Engineering & Structured Output | 20% | - Improving Claude response quality and consistency - Structured output generation and validation - Prompt design strategies |
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NEW QUESTION # 126
A customer returns 4 hours after the 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 In testing, you observe that when resuming sessions with stale tool results, the agent often references the outdated data in responses (e.g., "I see your refund is still being processed") even after subsequent fresh tool calls return different information.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: A
Explanation:
Providing a concise structured summary preserves essential context from the previous session while ensuring the agent retrieves fresh, up-to-date tool results. This prevents reliance on stale data and allows accurate, current responses to the returning customer.
NEW QUESTION # 127
Production reviews reveal inconsistent handling of uncertainty in final reports. Sometimes conflicting subagent findings are synthesized into a single confident statement, losing important nuance, while other reports over-hedge with excessive qualifications and become unhelpful. The web-search agent returns,
"Industry analysts estimate a $50 billion market size, although methodologies vary." The document-analysis agent returns, "A peer-reviewed study estimates $35 billion, with a ±$7 billion 95% confidence interval." The coordinator either selects one estimate arbitrarily or produces a vague $35-$50 billion range. What systematic approach best addresses this?
Answer: A
Explanation:
Option A preserves disagreement as meaningful evidence. The report should identify the two estimates separately, explain that they arise from different methodologies and source types, retain the peer-reviewed study's stated confidence interval, and classify the market size as contested rather than manufacturing false agreement. It may then explain what additional evidence would resolve the discrepancy.
Anthropic's research prompting guidance recommends developing competing hypotheses, tracking confidence, verifying across sources, and retaining structured research notes. This supports calibrated synthesis rather than arbitrary selection or vague hedging.
Option B discards potentially important minority evidence and incorrectly treats corroboration count as equivalent to reliability. Option C creates numerical precision unsupported by the original sources; confidence scores from different agents are not automatically comparable, and averaging incompatible estimates can be meaningless. Option D introduces survivorship bias by suppressing uncertainty before synthesis. A systematic reporting structure distinguishes consensus, disagreement, evidence quality, methodological limitations, and unresolved questions. That produces an actionable briefing without overstating certainty or weakening every conclusion with generic qualifications.
NEW QUESTION # 128
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, Glob) and integrates with Model Context Protocol (MCP) servers.
After integrating a local MCP server providing code analysis tools ( analyze_dependencies , find_dead_code , calculate_complexity ), you verify the server is healthy and tools appear in the tools/list response. However, you observe that the agent consistently uses Grep to search for import statements instead of calling analyze_dependencies -even when users explicitly ask about "code dependencies." Examining tool definitions reveals:
* MCP analyze_dependencies - "Analyzes dependency graph"
* Built-in Grep - "Search file contents for a pattern using regular expressions. Returns matching lines with line numbers and surrounding context." What's the most effective approach to improve the agent's selection of MCP tools?
Answer: B
Explanation:
Claude selects tools principally from their names, descriptions, parameter schemas, and the task context. The current description-"Analyzes dependency graph"-does not explain why the MCP tool is superior to a familiar text search. It omits the tool's scope, the circumstances in which it should be selected, and the structured information it returns.
Anthropic identifies detailed descriptions as the most important factor in tool-use performance. A strong description should state what the tool does, when it should and should not be used, what its parameters mean, and any limitations. Anthropic recommends several sentences for complex tools rather than a short generic label. ( https://docs.anthropic.com/en/docs/agents-and-tools/tool-use/implement-tool-use ) The MCP connector guidance likewise states that Claude selects among available tools using their names and descriptions and that clear, specific descriptions improve selection accuracy. ( https://docs.anthropic.com/en
/docs/agents-and-tools/mcp-connector )
Option B makes the functional distinction explicit: Grep locates textual import statements, whereas analyze_dependencies constructs a semantic graph containing direct and transitive dependencies, cycles, and potentially unresolved references. Option A is a brittle global override. Option C removes a generally useful tool. Option D increases the number of tools and selection ambiguity, contrary to Anthropic's recommendation to consolidate related operations where practical.
Official references/topics: MCP Tool Discovery; Tool Descriptions; Tool Selection Accuracy; Tool-Surface Design.
NEW QUESTION # 129
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.
You're implementing the escalation logic for when the agent should call escalate_to_human . Your team proposes four different approaches for triggering escalation.
Which approach will most reliably identify cases that genuinely require human intervention?
Answer: B
Explanation:
Option B identifies escalation through direct operational criteria rather than indirect proxies. A customer's explicit request for a person is unambiguous. A required policy exception defines an authorization boundary.
An inability to make meaningful progress identifies a case where continued autonomous execution is no longer productive.
Anthropic describes agents as systems that act, observe results, adjust, and continue until the task is completed or human input is required. Its trustworthy-agent guidance also emphasizes that agents must recognize when to pause rather than pushing through uncertainty or decisions that only a human can settle. (
https://www.anthropic.com/research/trustworthy-agents )
Option A is too rigid for high-ambiguity support cases. Maintaining exhaustive mappings for every issue, product, and customer segment recreates the brittleness that agentic reasoning is intended to avoid. Option C treats repeated tool use as a substitute for semantic progress; one definitive authorization failure may require immediate escalation, while several legitimate diagnostic calls may not. Option D confuses emotional tone with operational necessity. A calm customer may require a policy exception, while a frustrated customer may still have a straightforward automated resolution.
The escalation criteria should be encoded in the system instructions and supported by evaluations covering explicit requests, authorization limits, unresolved ambiguity, repeated non-progress, and successful self- service cases.
Official references/topics: Human-control checkpoints, escalation criteria, authorization boundaries, progress-aware agents.
NEW QUESTION # 130
Your automated code review is missing genuine bugs in pull requests. Investigation reveals that the review prompt includes this 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 findings are genuine logic errors that the model investigated but chose not to report. The team requires the review output to remain structured, with every 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: A
Explanation:
The prompt explicitly instructs Claude to suppress uncertain and lower-severity findings.
Increasing reasoning depth cannot override that reporting policy: Claude may identify a real defect during analysis and still omit it from the final response. Option B separates comprehensive detection from acceptance filtering, removing the source of the false negatives while retaining metadata needed for automated decisions.
Anthropic's current code-review prompting guidance recommends reporting every issue, including uncertain or lower-severity candidates, and attaching confidence and estimated severity so a separate verification or filtering stage can rank them. A structured schema can additionally require fields such as file path, line number, category, confidence, severity, evidence, and recommended action.
Option B preserves recall and machine-readable output while allowing deterministic, adjustable downstream thresholds. This is more reliable than embedding an overly restrictive acceptance decision inside the model's initial detection task.
NEW QUESTION # 131
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