Question 1

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.txt

The reviews complete and return feedback, but Claude comments only on the piped diff—it never reads

surrounding files in the checked-out repository to understand broader context, even when the diff modifies a

function called by many other modules. Which change to the invocation will cause Claude to read related

repository 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

Question 2

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.

Monitoring shows 12% of extractions fail Pydantic validation with specific errors like “expected float for

quantity, got ‘2 to 3’”. Retrying these requests without modification produces identical failures.

What’s the most effective approach to recover from these validation failures?

Options :

A : Send a follow-up request including the validation error, asking the model to correct its output.

B : Set temperature to 0 to eliminate output variability and ensure consistent formatting.

C : Pre-process source documents to standardize problematic formats before sending them for extraction.

D : Implement a secondary pipeline using a larger model tier to reprocess documents that fail validation.

Answer: A

Question 3

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.

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 missing

required 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

Question 4

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 5

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

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

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

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

Answer: B

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