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

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 3

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 4

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 5

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

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