Question 1

The web-search agent has gathered several relevant sources for a research topic. The document-analysis agent now needs to examine those sources. How does information typically flow between these two specialized subagents?

Options :

A : The coordinator receives the web-search agent’s output and includes the relevant sources and findings in the prompt used to invoke the document-analysis agent.

B : The web-search agent directly invokes the document-analysis agent and passes the discovered sources as parameters.

C : The agents communicate through an event-driven message queue, with the document-analysis agent subscribing to web-search completion events.

D : Both agents automatically access a shared memory store in which the web-search agent writes its findings.

Answer: A

Question 2

In production, you observe that simple fact-checking queries—for example, “What year was the Paris Climate Agreement signed?”—traverse all four subagents sequentially, consuming more than 40 seconds and significant tokens per query. Complex comparative research benefits from the full pipeline. Your query distribution is diverse and evolving as users discover new applications. What is the most effective approach to optimize for varying query complexity?

Options :

A : Create a fast path for factual questions that bypasses subagents entirely, routing all other queries through the complete pipeline to ensure research thoroughness.

B : Train a query-complexity classifier on labeled historical data to predict optimal subagent combinations, retraining it periodically as query patterns evolve.

C : Have the coordinator analyze each query and dynamically decide which subagents to invoke based on its assessment of the query requirements.

D : Implement pattern-based routing that categorizes queries by structure—single-fact, comparative, or analytical—and maps each category to a predefined subagent combination.

Answer: C

Question 3

You built an LLM-powered code-review tool that analyzes pull requests and returns structured findings. Each finding is a JSON object containing file_path, line_number, issue_category—such as security or style—and description. Developers can dismiss findings they consider unhelpful, and currently 35% of findings are dismissed. You want to analyze these dismissals to understand what the system is getting wrong and improve the prompts accordingly. What change to the output structure would best support this analysis?

Options :

A : Add a model_confidence field from 0.0 to 1.0 and filter findings below a threshold calibrated against historical dismissal rates.

B : Add a detected_pattern field recording the specific code construct that triggered the finding, such as single-letter loop variable.

C : Expand the description field with more detailed explanations of why each issue matters and how it should be fixed.

D : Remove the issue_category field and track dismissal rates only at the individual-finding level.

Answer: B

Question 4

You are building a structured data extraction system using Claude. The system extracts information fromunstructured documents, validates the output using JSON schemas, and maintains high accuracy. It musthandle edge cases gracefully and integrate with downstream systems.Your extraction system uses tool use with a JSON schema containing 12 fields and detailed descriptions,totaling approximately 2,500 tokens for the complete tool definition. Processing documents under 150,000tokens yields 98% accuracy. For documents between 175,000 and 190,000 tokens, accuracy drops to 71%,with information from the final third consistently missed. The model’s context window is 200,000 tokens.What is the most likely cause?

Options :

A : Schemas exceeding eight to ten fields increase decision complexity during parameter generation, reducing extraction accuracy independently of document length.

B : The model distributes attention proportionally across the input length, causing fields mentioned only once near the document’s end to receive insufficient processing focus.

C : Very long documents exceed the model’s effective attention span regardless of context limits, causing accuracy degradation for content farther from the prompt instructions.

D : Tool definitions consume input-context tokens. Combined with system prompts and document content, the total approaches the context limit, degrading end-of-document processing.

Answer: D

Question 5

You are building a structured data extraction system using Claude. The system extracts information fromunstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, andmaintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.The system needs to extract candidate information (name, contact details, skills, work experience, education)from uploaded resumes. The extracted data must strictly conform to a predefined JSON schema, as missingrequired fields or incorrect data types will cause downstream validation failures.What is the most reliable approach to ensure Claude’s output consistently matches the schema?

Options :

A : Parse Claude’s text response with regex patterns to extract JSON objects, using retry logic for malformed responses.

B : Include detailed JSON formatting instructions and a template example in the system prompt, asking Claude to output only valid JSON.

C : Make two separate API calls—first extracting information as text, then asking Claude to format that text as JSON.

D : Define a tool with an input schema matching your required JSON structure and extract the data from Claude’s tool_use response.

Answer: D