Question 1

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?

Options :

A : Pass reference identifiers and configure the subagent with read access to a shared memory store where the other subagents deposited their results.

B : Include the complete findings from both subagents directly in the synthesis subagent’s prompt.

C : Provide the subagent with tool definitions that allow it to request outputs from the other subagents through callbacks.

D : Spawn the subagent with only a brief task description, relying on automatic context inheritance from the coordinator.

Answer: B

Question 2

You are building a customer support resolution agent using the Claude Agent SDK. The agent handles highambiguity requests like returns, billing disputes, and account issues. It has access to your backend systemsthrough custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund ,escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.Your process_refund tool returns two types of errors: technical errors (“503 Service Unavailable”,“Connection timeout”) that are transient (~5% of calls), and business errors (“Order exceeds 30-day returnwindow”, “Item already refunded”) that are permanent (~12% of calls). Monitoring shows the agent wastes 3–4 turns retrying business errors that can never succeed. Currently, both error types return only a plain textmessage to Claude.What’s the most effective way to reduce wasted retries while improving customer-facing response quality?

Options :

A : Implement automatic retry logic at the tool layer for technical errors only, passing business errors to Claude without retries.

B : Add few-shot examples showing how to distinguish retriable from non-retriable errors by parsing error message text.

C : Add a check_refund_eligibility tool that must be called before process_refund to prevent business rule violations.

D : Return structured error responses with " retriable " : false for business errors and a customer-friendly explanation for Claude to use.

Answer: D

Question 3

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 4

Your code-review prompts include both implementation changes and the corresponding test file, but the review comments fail to identify untested code paths. The model correctly flags functions that have no tests at all, but it fails to recognize when conditional branches or error-handling paths within tested functions lack coverage. What is the most effective way to improve branch-level gap detection without overcomplicating the pipeline?

Options :

A : Interleave the implementation and tests in the prompt, presenting each function immediately before its test cases.

B : Add explicit instructions requiring Claude to enumerate every conditional branch and exception path, then verify that each path has a corresponding test assertion.

C : Implement a two-pass pipeline in which one model call extracts all conditional branches and another cross-references them against test assertions.

D : Include few-shot examples showing code with an uncovered branch and the corresponding review comment identifying the missing test case.

Answer: B

Question 5

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.txtThe reviews complete and return feedback, but Claude comments only on the piped diff—it never readssurrounding files in the checked-out repository to understand broader context, even when the diff modifies afunction called by many other modules. Which change to the invocation will cause Claude to read relatedrepository files while still applying your custom review instructions?

Options :

A : Keep --system-prompt and add --allowedTools " Read, Glob, Grep " because non-interactive -p mode otherwise disables filesystem tools.

B : Replace --system-prompt with --append-system-prompt so the review instructions are added to Claude Code’s default prompt instead of overwriting its built-in file-reading and code-navigation guidance.

C : Remove --system-prompt entirely and place the review instructions in a root-level CLAUDE.md because --system-prompt is incompatible with tool use under -p.

D : Stop piping the diff through standard input and embed it in the prompt string so Claude Code treats the invocation as an agentic session rather than a stream-processing operation.

Answer: B