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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Context Management & Reliability | 15% | - Context handling
|
| Topic 2: Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Topic 3: Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Topic 4: Tool Design & MCP Integration | 18% | - Tool integration
|
| Topic 5: Prompt Engineering & Structured Output | 20% | - Prompt design
|
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NEW QUESTION # 121
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
After implementing tool use with strict schema definitions, JSON syntax errors are eliminated, but
5% of extractions still contain empty arrays or null values for required fields such as citations and methodology. Spot-checking reveals that the source documents contain this information, but in varied formats--inline citations versus bibliographies, and methodology sections versus details embedded in introductions.
What is the most effective way to address these failures?
Answer: B
Explanation:
Option B targets the remaining failure mode: semantic recognition across heterogeneous document structures. Strict schemas eliminate malformed JSON and can guarantee that tool inputs conform to declared types, but they cannot force Claude to locate evidence that appears under unfamiliar headings or in atypical sections. Anthropic's prompting guidance says that a few relevant, diverse, structured examples are among the most reliable ways to improve accuracy and consistency. Examples should therefore show inline citations, reference lists, numbered bibliographies, methodology sections, and methods embedded in introductions, each paired with the correct extracted structure. This teaches the intended evidence-location and granularity rules rather than merely repeating the same request.
NEW QUESTION # 122
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.
When analyzing complex legal cases that cite multiple precedents, the document analysis subagent processes each sequentially. A landmark case citing 12 precedents takes over 3 minutes to analyze completely. What's the most effective way to reduce this latency while preserving the coordinator's ability to monitor and debug the system?
Answer: B
Explanation:
Parallelizing independent precedent analyses reduces latency, while keeping delegation at the coordinator level preserves centralized visibility, tracing, and debugging. Recursive delegation would make monitoring more difficult.
NEW QUESTION # 123
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 monitoring shows that the research phase takes longer than expected. Analysis reveals that the coordinator invokes the web-search subagent, waits for its response, and then invokes the document-analysis subagent. These tasks are independent; neither requires the other's output.
How should you modify the system to run these subagents concurrently?
Answer: C
Explanation:
Option B expresses both independent delegations in the same orchestration turn, allowing the runtime to execute them concurrently and return their results together. The current Claude Agent SDK calls the subagent- spawning capability the Agent tool; Task is its former name. Anthropic's SDK subagent documentation explicitly identifies parallel analysis as a primary subagent use case. Claude tool responses can also contain multiple tool_use blocks , enabling independent calls to be handled as one parallel group rather than as serial model turns. Option A shortens each execution but does not remove the unnecessary wait between them and may reduce analysis quality. Option C provides useful behavioral guidance, but instructions alone do not establish the required response structure; the coordinator must actually emit both calls together. Option D duplicates coordinator execution, complicates state management, and creates unnecessary aggregation work.
The correct flow is parallel fan-out from one coordinator, followed by a synchronization point that validates both results before synthesis begins. Failures should be tracked independently so that only the unsuccessful branch requires retrying.
NEW QUESTION # 124
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 pipeline validates outputs against JSON schemas, but you need to implement human review given limited reviewer capacity (they can handle approximately 5% of total extraction volume).
What's the most effective basis for selecting which extractions to route for human review?
Answer: D
Explanation:
Limited review capacity should be concentrated on records with the highest probability of semantic error. Schema validation confirms that the response has the correct structure and data types; it does not establish that the extracted values are accurate. Ambiguous wording, contradictory passages, missing evidence, and model-reported uncertainty are direct indicators that an extraction requires human judgment.
Anthropic's reliability guidance recommends permitting Claude to express uncertainty, grounding factual outputs in source material, and validating critical information because hallucination- reduction methods do not eliminate errors completely. These principles support routing uncertain or evidentially conflicted records to reviewers rather than treating syntactically valid output as automatically trustworthy.
NEW QUESTION # 125
In production, final reports frequently contain claims without proper source attribution. Investigation shows that the web-search and document-analysis agents correctly attach citations to their outputs, but the synthesis agent loses track of which sources support which conclusions when combining findings. What is the most effective architectural change?
Answer: A
Explanation:
Option C preserves provenance as part of the data contract instead of attempting to reconstruct it after synthesis. Each finding should carry a stable claim identifier and one or more source records containing the URL or document identifier, relevant location, supporting excerpt, retrieval date, and applicable qualification.
The synthesis agent can combine or rewrite claims while retaining their source relationships.
Anthropic's structured-output documentation supports schema-constrained, parseable results for downstream workflows. Its citation documentation explains that reliable citations depend on valid pointers to provided source material. Anthropic's skill guidance similarly recommends cross-referencing each major claim and verifying its citation before completion.
Option A reconstructs attribution probabilistically and may associate a claim with a similar but incorrect passage. Option B encodes metadata inside prose, making transformations and parsing fragile. Option D retains excessive context and adds another model stage to recover information that should never have been discarded. Structured claim-source mappings permit deterministic merging, citation validation, deduplication, and audit trails. The report generator can therefore cite the exact evidence supporting each conclusion without replaying complete research transcripts.
NEW QUESTION # 126
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