Question 1

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

Question 2

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 3

You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline.

The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You

need to design prompts that provide actionable feedback and minimize false positives.

Your pipeline includes a release-notes generation step that classifies and summarizes approximately 200

commits at the end of each weekly release cycle. Each commit is currently sent as a separate Messages API

request using a Sonnet-tier Claude model. The release notes are not needed until the following morning,

providing approximately 12 hours of acceptable latency.

Your team must reduce the per-token API cost while retaining the same model, prompts, and output quality.

Which approach satisfies all these constraints?

Options :

A : Issue the 200 Messages API requests concurrently because parallel execution reduces the per-token price.

B : Submit the 200 requests through the Message Batches API with unique custom_id values and retrieve the results after the batch finishes.

C : Concatenate all 200 commit messages into one Messages API request because reducing the number of requests always reduces token costs.

D : Replace the Sonnet-tier model with a Haiku-tier model to obtain a lower per-token price.

Answer: B

Question 4

You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives. Your automated reviewer uses a single prompt covering security issues, API design, and business-logic correctness. Your evaluation suite shows strong recall for API-design findings at 82% but poor recall for business-logic edge cases in quiz scoring at 34%. When you add few-shot examples of logic bugs to the prompt, logic recall improves to 41%, but API-design recall drops to 68%. How should you address this trade-off to improve detection across both categories?

Options :

A : Split the review into separate focused prompts—one for security and API design and another for business logic—each with dedicated examples, and then consolidate the findings before posting.

B : Replace the few-shot examples with a detailed checklist of specific logic edge cases to verify, such as division by zero in score calculations and boundary conditions in grading thresholds.

C : Upgrade to a more capable model tier because its stronger reasoning will handle both concern types in one prompt and eliminate the recall trade-off.

D : Provide the full repository as context instead of only the changed files and surrounding code, giving the model deeper visibility into business-logic patterns.

Answer: A

Question 5

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

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