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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Tool Design & MCP Integration | 18% | - Tool integration
|
| Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Context Management & Reliability | 15% | - Context handling
|
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NEW QUESTION # 78
After the web search agent and document analysis agent complete their tasks, the coordinator invokes the synthesis agent. However, the synthesis agent responds that it cannot complete the task because no research findings were provided. What is the most likely cause of this issue?
Answer: D
Explanation:
The synthesis agent relies on the coordinator to provide relevant findings from prior subagents. If the coordinator fails to include these outputs in the synthesis prompt, the agent receives no actionable context and cannot produce a meaningful summary or analysis.
NEW QUESTION # 79
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 extraction system implements automatic retries when validation fails. On each retry, the specific validation error is appended to the prompt. This retry-with-error-feedback approach resolves most failures within 2-3 attempts.
For which failure pattern would additional retries be LEAST effective?
Answer: D
Explanation:
Retry-with-error-feedback is effective when the required information is available and the defect concerns representation. Options A, C, and D are correctable formatting failures: the model can flatten an object into an array, remove thousands separators and emit an integer, or convert a datetime into the required date-only format. The validation message supplies enough information to revise the output.
Option B is fundamentally different. The complete co-author list is absent from the model's input and exists only in an external document. No number of retries can recover evidence that was never supplied. Repeated attempts may instead increase the probability of fabrication. Anthropic's hallucination guidance recommends restricting responses to available documents, allowing the model to acknowledge missing information, and withholding or retracting claims that cannot be grounded in the source. ( https://docs.anthropic.com/en/docs
/test-and-evaluate/strengthen-guardrails/reduce-hallucinations )
The correct response is therefore to return an explicit missing-data state, retrieve the external document through a tool, or route the record for enrichment. Retrying without changing the information boundary cannot solve the problem.
Current Anthropic Structured Outputs can eliminate many shape-related failures by guaranteeing JSON Schema conformance, but they still cannot create unavailable source facts. ( https://platform.claude.com/docs
/en/build-with-claude/structured-outputs ) This distinction-format failure versus evidence failure-is central to reliable extraction architecture.
Official references/topics: Retry Boundaries; Missing Context; Grounded Extraction; Structured Outputs; External Data Retrieval.
NEW QUESTION # 80
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: B
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 # 81
The coordinator agent has AgentDefinitions configured for all four specialized subagents, each with appropriate descriptions, prompts, and tool restrictions. During testing, you notice the coordinator correctly reasons about when to delegate - it generates messages like "I'll ask the web search agent to find sources on this topic" - but no subagent execution ever occurs. The coordinator then proceeds as if the delegation happened and continues with incomplete information. Logs show no errors. What is the most likely cause?
Answer: C
Explanation:
Defining specialized subagents is not enough; the coordinator must also be allowed to use the Task tool that actually invokes them. If Task is missing from the coordinator's allowed tools, the model may describe delegation in text but cannot execute the subagent call, so the workflow continues without real delegated results.
NEW QUESTION # 82
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.
An engineer asks your agent to identify untested code paths in a legacy payment processing module spanning
45 files. After reading the first 8 source files, the agent's responses are becoming noticeably less accurate-it' s forgetting previously discussed code patterns and hasn't yet located all test files or traced critical payment flows.
What's the most effective approach to complete this investigation?
Answer: D
Explanation:
The investigation contains several bounded research questions that can be delegated independently: locating the complete test suite, tracing payment and refund flows, identifying conditional branches, and mapping external dependencies. Each subagent can read the relevant files in its own context and return a focused summary to the coordinating agent.
Anthropic recommends subagents for codebase exploration because extensive file reading rapidly consumes the main context window. Subagents isolate that volume and return only their conclusions, preserving the main conversation for synthesis and implementation. ( https://docs.anthropic.com/en/docs/claude-code
/common-workflows ) Anthropic also describes parallel research as appropriate when separate investigation paths can proceed independently and the main agent can synthesize the results afterward. ( https://docs.
anthropic.com/en/docs/claude-code/sub-agents )
Option B sacrifices the current conversational state and requires reconstruction after /clear . Option C may reduce token usage, but isolated text matches cannot reliably reveal full execution paths, indirect calls, or test coverage relationships. Option D converts the current analysis into a single lossy summary and risks omitting details needed later.
Option A directly addresses the demonstrated context degradation while retaining a high-level coordinating thread. The subagent prompts should be narrowly scoped and require concrete outputs such as file paths, uncovered branches, call-chain evidence, and existing tests associated with each flow.
Official references/topics: Subagent Context Isolation; Parallel Research; Context Preservation; Coordinated Codebase Analysis.
NEW QUESTION # 83
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