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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tool Design & MCP Integration | 18% | - Tool integration
|
| Topic 2: Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Topic 3: Context Management & Reliability | 15% | - Context handling
|
| Topic 4: Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Topic 5: Claude Code Configuration & Workflows | 20% | - Claude Code
|
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NEW QUESTION # 41
After the web search and document analysis subagents complete their tasks, the coordinator needs to spawn the synthesis subagent to synthesize the findings. What is the correct approach for providing the synthesis subagent with the information it needs?
Answer: D
Explanation:
The synthesis subagent requires the actual content from prior subagents to perform its task.
Including the complete findings in its prompt ensures it has the necessary context to accurately synthesize information without relying on indirect references or external state.
NEW QUESTION # 42
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.
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 use 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: C
Explanation:
Option D preserves the evidence instead of manufacturing certainty. The two estimates are not directly interchangeable: one is an industry estimate with unspecified methodology, while the other is a peer-reviewed estimate with an explicit confidence interval. Converting both into model- generated confidence scores and calculating a weighted average would create a new figure that neither source reported and that may have no statistical validity. Anthropic's hallucination- reduction guidance recommends making claims auditable through quotations, citations, and supporting evidence rather than presenting unsupported synthesis as fact. Its Citations documentation similarly emphasizes retaining the exact source passages supporting individual claims. Filtering uncertain findings, option B, would remove decision-relevant information.
Requiring two-source corroboration, option C, could also discard credible evidence concerning emerging or specialized subjects. The synthesis agent should report the estimates separately, explain their methodological differences, identify which findings are strongly supported or disputed, and state what evidence would resolve the disagreement. This produces calibrated, useful reporting without arbitrary selection, excessive hedging, or false precision.
NEW QUESTION # 43
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.
A developer asks the agent to investigate why a specific API endpoint intermittently returns 500 errors. The codebase has 200+ files and the developer doesn't know which components are involved. The agent must trace the error through routing, middleware, business logic, and database layers.
What task decomposition approach would be most effective?
Answer: B
Explanation:
The investigation path cannot be reliably predetermined because the responsible files, components, and execution sequence are unknown. The agent should begin with available evidence-such as route definitions, stack traces, logs, or endpoint references-and use each discovery to decide the next search, file read, or diagnostic action.
Anthropic distinguishes predefined workflows from agents that dynamically direct their own processes and tool usage. Agents are appropriate for open-ended problems where the required number and nature of the steps cannot be predicted or encoded as a fixed path. During execution, the agent should obtain ground truth from tool results and adapt its plan based on that environmental feedback. ( https://www.anthropic.com
/engineering/building-effective-agents )
Option A requires a comprehensive plan before the agent has inspected the code, so the plan would rest on unsupported assumptions. Option B forces every investigation through the same sequence even when an early discovery makes later steps irrelevant or identifies a different dependency path. Option C assumes the four layers can be investigated independently; tracing an intermittent request failure usually involves dependencies revealed sequentially across layers.
Option D implements an adaptive agent loop: inspect, form a hypothesis, use tools, evaluate the evidence, and generate the next subtask. The workflow should still include stopping conditions, testable hypotheses, and escalation when evidence remains inconclusive.
Official references/topics: Adaptive Agent Loops, Dynamic Task Decomposition, Tool Feedback, Open- Ended Coding Investigations.
NEW QUESTION # 44
Your automated reviewer uses a single prompt covering security issues, API design, and business-logic correctness. Your evaluation suite shows strong recall for API-design findings at 82% but poor recall for business-logic edge cases in quiz scoring at 34%. When you add few-shot examples of logic bugs to the prompt, logic recall improves to 41%, but API-design recall drops to 68%. How should you address this trade- off to improve detection across both categories?
Answer: D
Explanation:
The evaluation results demonstrate prompt interference: improving attention to business-logic examples reduces performance on API design. Option C isolates the competing objectives so each model call can apply focused terminology, examples, evidence requirements, and evaluation criteria. The resulting structured findings can then be merged, deduplicated, and ranked before being posted.
Anthropic's Building Effective AI Agents describes parallelization through sectioning, where independent aspects of a task are handled by separate model calls and subsequently aggregated. This pattern is appropriate when one prompt must evaluate several distinct considerations and a single call does not handle all of them reliably.
Option A increases context volume without resolving competition between the review objectives; irrelevant repository content may further dilute attention. Option B may improve known quiz-scoring cases but can overfit the reviewer to an incomplete checklist and does not restore API-design recall. Option D assumes that additional model capability will eliminate prompt interference without supporting evaluation evidence.
Separate specialized review passes directly address the measured failure. Each pass should be evaluated independently, followed by an end-to-end evaluation confirming that consolidation preserves recall and does not introduce duplicated findings.
NEW QUESTION # 45
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.
During testing, you observe that in extended exploration sessions (30+ minutes), the agent starts giving inconsistent answers about code structure it discussed earlier. Engineers report having to repeat context about modules they've already explored. What's the most effective approach to address this?
Answer: B
Explanation:
Long exploration sessions fill the context window and degrade recall. A persistent scratchpad externalizes important architectural findings so the agent can re-read them instead of relying solely on increasingly crowded conversation context.
NEW QUESTION # 46
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