A customer returns 4 hours after the initial session about the same billing dispute. The previous
32-turn session contains lookup_order results showing "Status: PENDING, Expected resolution:
24-48 hours." In testing, you observe that when resuming sessions with stale tool results, the agent often references the outdated data in responses (e.g., "I see your refund is still being processed") even after subsequent fresh tool calls return different information. What approach most reliably handles returning customers?

• A. Resume with full history and add a system prompt instruction telling the agent to always prefer the most recent tool results when multiple calls to the same tool exist in context.
• B. Resume with full history and configure the agent to automatically re-call all previously-used tools at session start to ensure data freshness.
• C. Resume with full history but filter out previous tool_result messages before resuming, keeping only the human/assistant turns so the agent must re-fetch needed data.
• D. Start a new session, inject a structured summary of the previous interaction (issue type, actions taken, resolution status), then make fresh tool calls before engaging.

You are building developer-productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools--Read, Write, Bash, Grep, and Glob--and integrates with Model Context Protocol (MCP) servers.
Engineers frequently ask the agent to cross-reference code changes with Jira tickets during reviews--checking ticket descriptions, acceptance criteria, and recent comments. This currently requires manually copying and pasting content into conversations. The team wants the agent to access this standard Jira ticket data directly.
What is the most effective approach?

• A. Export Jira tickets to Markdown files in the repository that the agent accesses using the Read tool.
• B. Use the Bash tool with curl to call Jira's REST API, including authentication headers and parsing JSON responses inline.
• C. Integrate an existing Jira MCP server that exposes tickets, comments, and metadata through discoverable tool interfaces.
• D. Build a custom MCP server wrapping Jira's API with tools designed specifically for this team's code-review workflow.

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.
Your extraction pipeline processes contracts that frequently include amendments. When a contract contains both original terms and later amendments (e.g., original clause specifies "30- day payment terms" while Amendment 1 changes this to "45 days"), the model inconsistently extracts one value or the other with no indication of which applies.
What's the most effective approach to improve extraction accuracy for documents with amendments?

• A. Redesign the schema so amended fields capture multiple values, each with source location and effective date.
• B. Add prompt instructions to always extract the most recent amendment value and ignore superseded original terms.
• C. Implement post-extraction validation using pattern matching to detect amendments and flag those extractions for manual review.
• D. Preprocess documents with a classifier that identifies and removes superseded sections before the main extraction step.

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 runs:
PROMPT='You are a code reviewer. Analyze the provided diff for bugs,
security issues, and style violations.'
claude -p \
--dangerously-skip-permissions \
--system-prompt "$PROMPT" \
< diff.txt
The reviews complete and return feedback, but Claude only comments on the piped diff text--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 inspect related repository files while still applying your custom review instructions?

• A. Remove --system-prompt entirely and place the review instructions in a CLAUDE.md file, because - -system-prompt is incompatible with tool use under -p.
• B. Stop piping the diff through standard input and embed it inside the prompt, so Claude Code treats the invocation as an agentic session.
• C. Keep --system-prompt and add --allowedTools "Read,Glob,Grep", because non-interactive -p mode otherwise disables filesystem tools.
• D. Replace --system-prompt with --append-system-prompt and explicitly instruct Claude to inspect related repository files whenever broader context is needed.