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CCA-F Practice Questions — Page 3

Question 21

Open question ↗

Scenario: Claude Code for Continuous Integration

Your CI pipeline includes two Claude-based code review modes: a pre-merge-commit hook that blocks PR merge until completion, and a “deep analysis” that runs overnight, polls for batch completion, and posts detailed suggestions to the PR. You want to reduce API cost using the Message Batches API, which offers 50% savings but requires polling and can take up to 24 hours. Which mode should use batch processing?

Which mode should use batch processing?

  • A.Only the pre-merge-commit hook.
  • B.Only the deep analysis.
  • C.Both modes.
  • D.Neither mode.

Question 22

Open question ↗

Scenario: Claude Code for Continuous Integration

Your automated review analyzes comments and docstrings. The current prompt instructs Claude to “check that comments are accurate and up to date.” Findings often flag acceptable patterns (TODO markers, simple descriptions) while missing comments describing behavior the code no longer implements. What change addresses the root cause of this inconsistent analysis?

What change addresses the root cause?

  • A.Include `git blame` data so Claude can identify comments that predate recent code changes.
  • B.Add few-shot examples of misleading comments to help the model recognize similar patterns in the codebase.
  • C.Filter TODO, FIXME, and descriptive comment patterns before analysis to reduce noise.
  • D.Specify explicit criteria: flag comments only when the behavior they claim contradicts the code’s actual behavior.

Question 23

Open question ↗

Scenario: Claude Code for Continuous Integration

Your automated code review system shows inconsistent severity ratings—similar issues like null pointer risks are rated “critical” in some PRs but only “medium” in others. Developer surveys show growing distrust—many start dismissing findings without reading because “half are wrong.” High-false-positive categories erode trust in accurate categories. Which approach best restores developer trust while improving the system?

Which approach best restores developer trust?

  • A.Temporarily disable high-false-positive categories (style, naming, documentation) and keep only high-precision categories while improving prompts.
  • B.Keep all categories enabled but display confidence scores with each finding so developers can decide what to investigate.
  • C.Keep all categories enabled and add few-shot examples to improve accuracy for each category over the next few weeks.
  • D.Apply a uniform strictness reduction across all categories to bring the overall false-positive rate down.

Question 24

Open question ↗

Scenario: Claude Code for Continuous Integration

Your automated review generates test-case suggestions for each PR. Reviewing a PR that adds course completion tracking, Claude suggests 10 test cases, but developer feedback shows that 6 duplicate scenarios already covered by the existing test suite. What change most effectively reduces duplicate suggestions?

What change is most effective?

  • A.Include the existing test file in context so Claude can determine what scenarios are already covered.
  • B.Reduce the requested number of suggestions from 10 to 5, assuming Claude prioritizes the most valuable cases first.
  • C.Add instructions directing Claude to focus exclusively on edge cases and error conditions rather than success paths.
  • D.Implement post-processing that filters suggestions whose descriptions match existing test names via keyword overlap.

Question 25

Open question ↗

Scenario: Claude Code for Continuous Integration

After an initial automated review identifies 12 findings, a developer pushes new commits to address issues. Re-running review produces 8 findings, but developers report that 5 duplicate previous comments on code that was already fixed in the new commits. What is the most effective way to eliminate this redundant feedback while maintaining thoroughness?

What is the most effective way to eliminate redundant feedback?

  • A.Run review only when the PR is created and in the final pre-merge state, skipping intermediate commits.
  • B.Add a post-processing filter that removes findings that match previous ones by file paths and issue descriptions before posting comments.
  • C.Restrict review scope to files changed in the most recent push, excluding files from earlier commits.
  • D.Include previous review findings in context and instruct Claude to report only new or still-unresolved issues.

Question 26

Open question ↗

Scenario: Claude Code for Continuous Integration

Your pipeline script runs `claude "Analyze this pull request for security issues"`, but the job hangs indefinitely. Logs show Claude Code is waiting for interactive input. What is the correct approach to run Claude Code in an automated pipeline?

What is the correct approach?

  • A.Add a `--batch` flag: `claude --batch "Analyze this pull request for security issues"`.
  • B.Add the `-p` flag: `claude -p "Analyze this pull request for security issues"`.
  • C.Redirect stdin from `/dev/null`: `claude "Analyze this pull request for security issues" < /dev/null`.
  • D.Set the environment variable `CLAUDE_HEADLESS=true` before running the command.

Question 27

Open question ↗

Scenario: Claude Code for Continuous Integration

A pull request changes 14 files in an inventory tracking module. A single-pass review that analyzes all files together produces inconsistent results: detailed feedback on some files but shallow comments on others, missed obvious bugs, and contradictory feedback (a pattern is flagged in one file but identical code is approved in another file in the same PR). How should you restructure the review?

How should you restructure the review?

  • A.Run three independent full-PR review passes and flag only issues that appear in at least two of the three runs.
  • B.Split into focused passes: review each file individually for local issues, then run a separate integration-oriented pass to examine cross-file data flows.
  • C.Require developers to split large PRs into smaller submissions of 3–4 files before running automated review.
  • D.Switch to a larger model with a bigger context window so it can pay sufficient attention to all 14 files in one pass.

Question 28

Open question ↗

Scenario: Claude Code for Continuous Integration

Your automated code review averages 15 findings per pull request, and developers report a 40% false-positive rate. The bottleneck is investigation time: developers must click into each finding to read Claude’s rationale before deciding whether to fix or dismiss it. Your CLAUDE.md already contains comprehensive rules for acceptable patterns, and stakeholders rejected any approach that filters findings before developers see them. What change best addresses investigation time?

What change best addresses investigation time?

  • A.Require Claude to include its rationale and confidence estimate directly in each finding.
  • B.Add a post-processor that analyzes finding patterns and automatically suppresses those that match historical false-positive signatures.
  • C.Categorize findings as “blocking issues” vs “suggestions,” with different review requirements by level.
  • D.Configure Claude to show only high-confidence findings, filtering uncertain flags before developers see them.

Question 29

Open question ↗

Scenario: Claude Code for Continuous Integration

Analysis of your automated code review shows large differences in false-positive rates by finding category: security/correctness findings have 8% false positives, performance findings 18%, style/naming findings 52%, and documentation findings 48%. Developer surveys show growing distrust—many start dismissing findings without reading because “half are wrong.” High-false-positive categories erode trust in accurate categories. Which approach best restores developer trust while improving the system?

Which approach best restores developer trust?

  • A.Temporarily disable high-false-positive categories (style, naming, documentation) and keep only high-precision categories while improving prompts.
  • B.Keep all categories enabled but display confidence scores with each finding so developers can decide what to investigate.
  • C.Keep all categories enabled and add few-shot examples to improve accuracy for each category over the next few weeks.
  • D.Apply a uniform strictness reduction across all categories to bring the overall false-positive rate down.

Question 30

Open question ↗

Scenario: Claude Code for Continuous Integration

Your team wants to reduce API costs for automated analysis. Currently, synchronous Claude calls support two workflows: (1) a blocking pre-merge check that must complete before developers can merge, and (2) a technical debt report generated overnight for review the next morning. Your manager proposes moving both to the Message Batches API to save 50%. How should you evaluate this proposal?

How should you evaluate this proposal?

  • A.Move both to batch processing with fallback to synchronous calls if batches take too long.
  • B.Move both workflows to batch processing with status polling to verify completion.
  • C.Use batch processing only for technical debt reports; keep synchronous calls for pre-merge checks.
  • D.Keep synchronous calls for both workflows to avoid issues with batch result ordering.