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CCA-F Practice Questions — Page 5

Question 41

Open question ↗

Scenario: Code Generation with Claude Code

You want to create a custom slash command `/review` that runs your team’s standard code review checklist. It should be available to every developer when they clone or update the repository.

Where should you create the command file?

  • A.In `~/.claude/commands/` in each developer’s home directory.
  • B.In the project repository under `.claude/commands/`.
  • C.In `.claude/config.json` as an array of commands.
  • D.In the root project CLAUDE.md.

Question 42

Open question ↗

Scenario: Code Generation with Claude Code

Your team’s CLAUDE.md grew beyond 500 lines mixing TypeScript conventions, testing guidance, API patterns, and deployment procedures. Developers find it hard to locate and update the right sections.

What approach does Claude Code support to organize project-level instructions into focused topical modules?

  • A.Define a `.claude/config.yaml` mapping file patterns to specific sections inside CLAUDE.md.
  • B.Create separate Markdown files in `.claude/rules/`, each covering one topic (e.g., `testing.md`, `api-conventions.md`).
  • C.Split instructions into README.md files in relevant subdirectories that Claude automatically loads as instructions.
  • D.Create multiple files named CLAUDE.md at different levels of the directory tree, each overriding parent instructions.

Question 43

Open question ↗

Scenario: Code Generation with Claude Code

You create a custom skill `/explore-alternatives` that your team uses to brainstorm and evaluate implementation approaches before choosing one. Developers report that after running the skill, subsequent Claude responses are influenced by the alternatives discussion—sometimes referencing rejected approaches or retaining exploration context that interferes with actual implementation.

How should you most effectively configure this skill?

  • A.Use the `!` prefix in the skill to run exploration logic as a bash subprocess.
  • B.Add `context: fork` in the skill frontmatter.
  • C.Split into two skills—`/explore-start` and `/explore-end`—to mark boundaries when exploration context should be discarded.
  • D.Create the skill in `~/.claude/skills/` instead of `.claude/skills/`.

Question 44

Open question ↗

Scenario: Code Generation with Claude Code

Your team wants to add a GitHub MCP server for searching PRs and checking CI status via Claude Code. Each of six developers has their own personal GitHub access token. You want consistent tooling across the team without committing credentials to version control.

Which configuration approach is most effective?

  • A.Have each developer add the server in user scope via `claude mcp add --scope user`.
  • B.Create an MCP server wrapper that reads tokens from a `.env` file and proxies GitHub API calls, then add the wrapper to the project `.mcp.json`.
  • C.Add the server to the project `.mcp.json` using environment variable substitution (`${GITHUB_TOKEN}`) for auth and document the required environment variable in the project README.
  • D.Configure the server in project scope with a placeholder token, then tell developers to override it in their local config.

Question 45

Open question ↗

Scenario: Code Generation with Claude Code

You’re adding error-handling wrappers around external API calls across a 120-file codebase. The work has three phases: (1) discover all call sites and patterns, (2) collaboratively design the error-handling approach, and (3) implement wrappers consistently. In Phase 1, Claude generates large output listing hundreds of call sites with context, quickly filling the context window before discovery finishes.

Which approach is most effective to complete the task while maintaining implementation consistency?

  • A.Use an Explore subagent for Phase 1 to isolate verbose discovery output and return a summary, then continue Phases 2–3 in the main conversation.
  • B.Do all phases in the main conversation, periodically using `/compact` to reduce context usage while moving through files.
  • C.Switch to headless mode with `--continue`, passing explicit context summaries between batch calls to maintain continuity.
  • D.Define the error-handling pattern in CLAUDE.md, then process files in batches across multiple sessions relying on the shared memory file for consistency.

Question 46

Open question ↗

Scenario: Customer Support Agent

While testing, you notice the agent often calls `get_customer` when users ask about order status, even though `lookup_order` would be more appropriate. What should you check first to address this problem?

What should you check first?

  • A.Implement a preprocessing classifier to detect order-related requests and route them directly to `lookup_order`.
  • B.Reduce the number of tools available to the agent to simplify choice.
  • C.Add few-shot examples to the system prompt covering all possible order request patterns to improve tool selection.
  • D.Check the tool descriptions to ensure they clearly differentiate each tool’s purpose.

Question 47

Open question ↗

Scenario: Customer Support Agent

Your agent handles single-issue requests with 94% accuracy (e.g., “I need a refund for order #1234”). But when customers include multiple issues in one message (e.g., “I need a refund for order #1234 and also want to update the shipping address for order #5678”), tool selection accuracy drops to 58%. The agent usually solves only one issue or mixes parameters across requests. What approach most effectively improves reliability for multi-issue requests?

What approach is most effective?

  • A.Implement a preprocessing layer that uses a separate model call to decompose multi-issue messages into separate requests, handle each independently, and merge results.
  • B.Combine related tools into fewer universal tools.
  • C.Add few-shot examples to the prompt demonstrating correct reasoning and tool sequencing for multi-issue requests.
  • D.Implement response validation that detects incomplete answers and automatically reprompts the agent to resolve missed issues.

Question 48

Open question ↗

Scenario: Customer Support Agent

Production logs show that for simple requests like “refund for order #1234,” your agent resolves the issue in 3–4 tool calls with 91% success. But for complex requests like “I was billed twice, my discount didn’t apply, and I want to cancel,” the agent averages 12+ tool calls with only 54% success—often investigating issues sequentially and fetching redundant customer data for each. What change most effectively improves handling of complex requests?

What change is most effective?

  • A.Add explicit verification checkpoints between stages, requiring the agent to record progress after resolving each issue before moving to the next.
  • B.Reduce the number of tools by combining `get_customer`, `lookup_order`, and billing-related tools into a single `investigate_issue` tool.
  • C.Decompose the request into separate issues, then investigate each in parallel using shared customer context before synthesizing a final resolution.
  • D.Add few-shot examples to the system prompt demonstrating ideal tool-call sequences for various multi-faceted billing scenarios.

Question 49

Open question ↗

Scenario: Customer Support Agent

Your agent achieves 55% first-contact resolution, well below the 80% target. Logs show it escalates simple cases (standard replacements for damaged goods with photo proof) while trying to handle complex situations requiring policy exceptions autonomously. What is the most effective way to improve escalation calibration?

What is the most effective way to improve escalation calibration?

  • A.Require the agent to self-rate confidence on a 1–10 scale before each response and automatically route to humans when confidence drops below a threshold.
  • B.Deploy a separate classifier model trained on historical tickets to predict which requests need escalation before the main agent starts processing.
  • C.Add explicit escalation criteria to the system prompt with few-shot examples showing when to escalate versus resolve autonomously.
  • D.Implement sentiment analysis to determine customer frustration level and automatically escalate past a negative sentiment threshold.

Question 50

Open question ↗

Scenario: Customer Support Agent

After calling `get_customer` and `lookup_order`, the agent has all available system data but still faces uncertainty. Which situation is the most justified trigger for calling `escalate_to_human`?

Which situation is most justified for escalation?

  • A.A customer wants to cancel an order shipped yesterday and arriving tomorrow. The agent should escalate because the customer might change their mind after receiving the package.
  • B.A customer claims they didn’t receive an order, but tracking shows it was delivered and signed for at their address three days ago. The agent should escalate because presenting contradictory evidence could harm the customer relationship.
  • C.A customer requests competitor price matching. Your policies allow price adjustments for price drops on your own site within 14 days, but say nothing about competitor prices. The agent should escalate for policy interpretation.
  • D.A customer message contains both a billing question and a product return. The agent should escalate so a human can coordinate both issues in one interaction.