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

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

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

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 5

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.

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 150,000

tokens 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

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