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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Claude Code Configuration & Workflows | 20% | - Path-specific rules and .claude/rules/ configuration - Hooks vs advisory instructions - CLAUDE.md hierarchy, precedence and @import rules - Custom slash commands and plan mode vs direct execution - CI/CD integration and non-interactive mode parameters |
| Topic 2: Context Management & Reliability | 15% | - Token budget management and cost control - Context pruning and summarization strategies - Context window optimization and prioritization - Idempotency, consistency and failure resilience |
| Topic 3: Prompt Engineering & Structured Output | 20% | - JSON schema design and structured output enforcement - Explicit criteria definition and few-shot prompting - Validation, parsing and retry loop strategies - System prompt design and persona alignment |
| Topic 4: Tool Design & MCP Integration | 18% | - Tool schema design and interface boundaries - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool distribution and permission controls - MCP tool, resource and prompt implementation - Error handling and tool response formatting |
| Topic 5: Agentic Architecture & Orchestration | 27% | - Session state management and workflow enforcement - Task decomposition and dynamic subagent selection - Error recovery, guardrails and safety patterns - Agentic loop design and stop_reason handling - Multi-agent patterns: coordinator-subagent and hub-and-spoke |
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NEW QUESTION # 159
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.
After deploying automated code review, developers report that approximately 35% of findings are false positives following consistent patterns: style suggestions that contradict team conventions, security warnings for patterns that are safe in the deployment environment, and performance suggestions that would degrade this particular use case.
You want to reduce false positives while enabling the model to generalize its judgment to novel code patterns it has not seen before.
Which approach is most effective?
Answer: A
Explanation:
Option B demonstrates the decision boundary Claude must learn. Carefully selected examples can show structurally similar code producing different outcomes based on project context-for example, an approved authentication wrapper versus an unsafe direct call, or a deliberate performance trade-off versus an accidental quadratic operation. These contrasts help Claude apply the underlying judgment to new code rather than merely memorizing prohibited phrases.
Anthropic identifies relevant, diverse, and clearly structured examples as one of the most reliable methods for improving output accuracy and consistency. It recommends several examples that mirror the real use case and cover important edge conditions. Option A risks creating an oversized negative catalogue that consumes context, becomes difficult to maintain, and cannot anticipate every future variation. Option C filters text after generation and may suppress genuine findings that happen to use the selected keywords. Option D is dangerously vague: telling a reviewer to be conservative can suppress real but uncertain defects and reduce recall. The prompt should provide paired acceptable/problematic examples, explain why each classification differs, and require concrete code evidence for every reported finding. Anthropic prompting best practices
NEW QUESTION # 160
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: C
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 # 161
You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
The document-analysis agent has a single analyze_document tool that takes a document and a free-text instruction parameter. During evaluation, requests such as "extract the key financial metrics" often return narrative summaries, while "summarize the methodology" sometimes returns raw data tables. The synthesis agent reports that 35% of analysis results require new requests with clarified instructions.
What is the most effective way to improve reliability?
Answer: C
Explanation:
Option B removes ambiguity at the tool-interface level. Extraction, summarization, and claim verification are different operations with different success criteria and output structures. Separate tools allow each operation to have a precise name, purpose, parameter schema, response contract, and error behavior. Anthropic's guidance on writing effective tools for agents recommends a small set of thoughtful tools targeted at specific, high-impact workflows rather than generic wrappers that force the model to infer operational meaning from loosely structured instructions. Tool descriptions and examples, option A, could improve behavior but leave the overloaded free-text contract intact. An analysis_type enum, option C, identifies the requested mode but still requires one tool to return substantially different result structures, increasing validation and downstream branching. Coordinator pre-classification, option D, adds another probabilistic decision without correcting the interface used by the analysis agent. Purpose-specific tools let the synthesis layer know exactly what output it will receive. Their schemas should require relevant fields-for example, data-point names and source locations for extraction, or claim, verdict, and evidence for verification-and should be tested against the observed failure cases.
NEW QUESTION # 162
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 review calls the Claude API for each pull request, using tool_use with a report_findings tool that returns a JSON array of finding objects. Each object contains file_path, line_number, severity, category, and description. During testing on a large pull request touching more than 30 files, the response reaches the max_tokens limit and is truncated in the middle of the JSON, causing your pipeline's parser to fail.
What is the most effective way to handle this?
Answer: D
Explanation:
Option A is correct because the failure results from excessive output volume, not from JSON or tool use. Anthropic documents that max_tokens is a hard output ceiling and that a response stopped at this limit reports stop_reason: "max_tokens". Its tool-streaming guidance also warns that generation can stop midway through a tool parameter, leaving partial JSON that must not be treated as complete. Splitting the pull request into bounded file groups limits the number of findings produced by each call, preserves the report_findings schema, and allows the pipeline to validate every response independently before merging and deduplicating the arrays. Shared dependency context can still be included when cross-file analysis is required.
NEW QUESTION # 163
Users report that final reports sometimes lack depth on specific subtopics. Investigation shows that the document-analysis agent frequently identifies evidence gaps--for example, noting that
"the retrieved sources discuss API authentication but lack details about token-refresh patterns." Under the current strict pipeline, this insight is not actionable because searching has already finished. What is the most effective architectural change?
Answer: C
Explanation:
Option C converts the linear pipeline into a controlled iterative research loop. The document- analysis agent is best positioned to identify precisely what the retrieved evidence fails to answer.
It should return structured gaps containing the missing question, evidence already examined, preferred source characteristics, and the coverage criterion that remains unsatisfied. The coordinator can then issue focused searches and resubmit the new material for analysis.
Anthropic's multi-agent research architecture uses a lead agent to decompose work, coordinate research agents, assess returned information, and continue investigation when further evidence is required. The guidance also stresses clear objectives, output formats, source requirements, and task boundaries to prevent gaps and duplicated work.
NEW QUESTION # 164
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