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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: Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Topic 4: Context Management & Reliability | 15% | - Context handling
|
| Topic 5: Claude Code Configuration & Workflows | 20% | - Claude Code
|
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NEW QUESTION # 187
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 synthesis agent receives summarized findings from the web-search and document-analysis agents, then passes a consolidated summary to the report generator. During testing, you discover that the generated reports make factual claims without proper citations. The report generator cannot attribute statements to their original sources because that metadata was lost during the summarization steps.
What is the most effective approach to ensure proper source attribution in the final reports?
Answer: A
Explanation:
Option A preserves provenance as first-class data throughout the pipeline. Each finding should contain the summarized claim, supporting excerpt, source identifier, URL or document title, and location information such as a page or content-block index. The synthesis agent can combine findings without severing the association between evidence and source, while the report generator can render citations directly from the structured records. Anthropic's Citations documentation explains that reliable citations identify the supporting passage and its precise source location, including PDF page ranges or content-block indices. Passing all raw output, option B, preserves evidence but introduces excessive context and makes source selection harder.
Inline references in prose, option C, are vulnerable to being altered, omitted, or detached from claims during subsequent summarization. Option D repeats research after report generation and may locate a different source rather than the evidence originally used. A structured provenance contract should therefore be required in every subagent's output and validated at each handoff. The report generator should reject or flag factual claims that lack an associated evidence record instead of inventing or retrospectively reconstructing citations.
NEW QUESTION # 188
Your pipeline runs:
PROMPT= " You are a code reviewer. "
PROMPT= " $PROMPT Analyze the provided diff "
PROMPT= " $PROMPT for bugs, security issues, "
PROMPT= " $PROMPT and style violations. "
claude -p \
--dangerously-skip-permissions \
--system-prompt " $PROMPT " < diff.txt
The reviews complete and return feedback, but Claude comments only on the piped diff-it never reads surrounding files in the checked-out repository to understand broader context, even when the diff modifies a function called by many other modules. Which change to the invocation will cause Claude to read related repository files while still applying your custom review instructions?
Answer: D
Explanation:
The --system-prompt flag replaces Claude Code's complete default system prompt. Although this does not technically remove the available tools, it discards the default coding-agent guidance that tells Claude how to gather repository context, navigate code, use tools, and verify findings. The replacement prompt says only to analyze the supplied diff, so the observed diff-only behaviour is consistent with the configured instructions.
Option B preserves the standard Claude Code identity and tool guidance while adding the custom review criteria. The Claude Code CLI reference states that --append-system-prompt appends instructions to the default prompt, whereas --system-prompt replaces it. Anthropic specifically recommends appending when Claude should remain a coding assistant that follows additional per-invocation rules.
Option A is incorrect because print mode does not inherently disable Read, Glob, or Grep; --allowedTools restricts or pre-authorizes tools but does not restore overwritten behavioural guidance. Option C is incorrect because replacement system prompts remain compatible with tools, although the replacement must supply suitable tool instructions. Option D changes where the diff appears but does not restore Claude Code's default code-navigation behaviour. Appending the review instructions directly addresses the configuration error.
NEW QUESTION # 189
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: D
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 # 190
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 code review is missing genuine bugs in pull requests. Investigation reveals that your review prompt includes the 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 bugs are genuine logic errors that the model investigated but chose not to report. The team requires the review output to remain structured, with each 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: D
Explanation:
Option B removes the prompt-level suppression responsible for the false negatives while preserving machine-readable metadata. Anthropic's current code-review prompting guidance warns that instructions such as "only report high-severity issues" or "be conservative" may be followed literally:
Claude can identify genuine defects during analysis but omit them from its output. Anthropic recommends requesting all findings and applying filtering separately.
Confidence and severity fields allow downstream code to apply adjustable thresholds without forcing the model to discard evidence during generation. A schema can require fields such as file, line, description, severity, confidence, evidence, and recommended action; Anthropic's Structured Outputs documentation supports enforcing such a response contract.
NEW QUESTION # 191
Production monitoring shows that follow-up queries such as "summarize what we learned about market trends" consistently take more than 40 seconds. Investigation reveals that the coordinator spawns the synthesis subagent for every summarization request, passing more than 80,000 tokens of accumulated findings. The coordinator already has these findings in its context from orchestrating the research. What is the most effective way to improve response time for these follow-up summaries?
Answer: C
Explanation:
Option C avoids an unnecessary agent boundary. The coordinator already possesses the accumulated findings and can perform a straightforward summary without serializing, transferring, and reprocessing more than
80,000 tokens in another context window. Subagents should be reserved for work that requires isolated context, specialized instructions, separate tools, or an independent analytical process.
Anthropic's current prompting guidance advises using subagents for independent workstreams and parallel or context-isolated tasks, while handling simpler tasks directly. Anthropic also notes that excessive subagent use creates unnecessary cost and latency.
Option A generates multiple summaries speculatively, consuming resources even if they are never requested and creating cache-invalidation complexity whenever findings change. Option B may reduce repeated input- token cost, but it does not eliminate subagent startup, message processing, or the unnecessary orchestration round trip. Option D introduces an iterative request protocol that will likely increase latency further. Direct coordinator summarization uses information already available in active context and therefore provides the smallest architectural change, lowest token-transfer overhead, and fastest response while preserving subagent synthesis for genuinely complex comparative or cross-source analysis.
NEW QUESTION # 192
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