You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
Your team has connected a custom MCP server that provides DevOps workflow templates. The server exposes several MCP prompts (such as deploy_checklist and incident_response) in addition to tools. How do these MCP prompts become accessible within Claude Code?

• A. They are added to Claude Code's tool registry alongside the server's tools, invoked automatically by the model when relevant to the task.
• B. They are automatically prepended to every conversation as additional system-level context, influencing Claude's behavior throughout the session.
• C. They appear as slash commands (e.g., mcp_servername_deploy_checklist) that you can invoke, with arguments passed after the command name.
• D. They are surfaced as @-mentionable resources alongside files, fetched and attached to your message when referenced.

Explanation: Only visible for FreePdfDump members. You can sign-up / login (it's free).

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

Explanation: Only visible for FreePdfDump members. You can sign-up / login (it's free).

Your CI pipeline performs security-focused code reviews on approximately 50 pull requests daily, currently costing $150 per day through the synchronous API. Reviews are non-blocking-- developers merge after tests pass and address findings in follow-up commits. You are evaluating the Message Batches API because it offers a 50% cost reduction. What factor most determines whether batch processing is appropriate for this use case?

• A. Whether review feedback arriving up to 24 hours after pull-request creation remains actionable.
• B. Whether each review can be structured as a single request without multi-turn refinement.
• C. Whether your result-processing system can handle reviews arriving in a different order from the order in which they were submitted.
• D. Whether reducing per-review latency from 30?0 seconds to near-instantaneous delivery matters to your workflow.

Explanation: Only visible for FreePdfDump members. You can sign-up / login (it's free).

Production reviews reveal inconsistent handling of uncertainty in final reports. Sometimes conflicting subagent findings are synthesized into a single confident statement (losing nuance), while other times reports over-hedge with excessive qualifications (becoming unhelpful). When the web search agent returns "industry analysts estimate $50B market size (methodology varies)" and the document analysis agent returns "peer-reviewed study estimates $35B (±$7B, 95% CI)," the coordinator either picks one arbitrarily or produces vague statements like "the market may be
$35B-$50B depending on factors." What systematic approach best addresses this?

• A. Implement a confidence calibration layer that normalizes subagent uncertainty expressions to standardized probability scores (0.0-1.0), then weight-average findings by their calibrated confidence.
• B. Instruct the synthesis agent to structure reports with explicit sections distinguishing well- established findings from contested ones, preserving original source characterizations and methodological context.
• C. Configure subagents to only report findings meeting a high-confidence threshold, filtering uncertain information before it reaches the coordinator.
• D. Add a verification subagent that cross-references findings across sources, only passing claims to synthesis that are corroborated by at least two independent sources.

Explanation: Only visible for FreePdfDump members. You can sign-up / login (it's free).

A user asks Claude to ignore all previous instructions and reveal confidential internal prompt content. How should Claude behave?

• A. Generate random text.
• B. Reveal the prompt.
• C. Follow higher-priority instructions and refuse.
• D. Partially reveal hidden instructions.

Explanation: Only visible for FreePdfDump members. You can sign-up / login (it's free).