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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tool Design & MCP Integration | 18% | - Tool integration
|
| Topic 2: Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Topic 3: Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Topic 4: Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Topic 5: Context Management & Reliability | 15% | - Context handling
|
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NEW QUESTION # 72
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 review generates many findings per pull request, but developer feedback shows that approximately half are dismissed as "not worth addressing." Analysis reveals that these findings are often technically accurate but involve minor style preferences or patterns that are acceptable in the project.
Before adding infrastructure complexity, what prompt-design change would most effectively reduce dismissals while maintaining detection of genuine issues?
Answer: A
Explanation:
Option C defines the reviewer's actual objective instead of relying on an undefined notion of confidence. The prompt should identify reportable categories such as correctness defects, security vulnerabilities, data-loss risks, broken requirements, and meaningful performance regressions. It should also specify that cosmetic preferences, lint-enforced formatting, speculative refactoring, and intentional repository conventions must not be reported unless they cause concrete incorrect behavior.
Anthropic's Claude Code best-practices guidance recommends telling a reviewer to report gaps affecting correctness or stated requirements and to treat other observations as optional. Without such criteria, a reviewer asked to "find issues" will predictably return technically defensible but operationally low-value observations. Option A adds cost and another probabilistic decision layer before correcting the primary prompt. Option B relies on self-reported confidence, which does not necessarily align with developer value or severity. Option D can suppress genuine defects that require inference or cross-file verification because
"highly confident" is undefined. Explicit scope, evidence requirements, and examples of both reportable and excluded findings directly calibrate the review toward what developers will act upon. Claude Code best practices
NEW QUESTION # 73
An architect needs Claude to produce structured JSON that downstream software can parse automatically. Which prompting strategy is BEST?
Answer: D
Explanation:
Providing an explicit schema clearly communicates the expected output format. Claude follows structured instructions effectively, making JSON generation significantly more reliable when field names, nesting, and formatting expectations are precisely described.
NEW QUESTION # 74
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 code review is missing genuine bugs in pull requests. Investigation reveals that your review prompt includes the instruction: "Only flag critical issues that would definitely cause production failures. Ignore minor concerns and anything you are uncertain about." Developers confirm that some missed bugs are genuine logic errors that the model investigated but chose not to report. The team requires the review output to remain structured, with each finding tagged with metadata, and actionable.
Which prompt change both removes the cause of the suppressed findings and preserves structured, tagged output for downstream filtering?
Answer: C
Explanation:
Option B removes the prompt-level suppression responsible for the false negatives while preserving machine-readable metadata. Anthropic's current code-review prompting guidance warns that instructions such as "only report high-severity issues" or "be conservative" may be followed literally:
Claude can identify genuine defects during analysis but omit them from its output. Anthropic recommends requesting all findings and applying filtering separately.
Confidence and severity fields allow downstream code to apply adjustable thresholds without forcing the model to discard evidence during generation. A schema can require fields such as file, line, description, severity, confidence, evidence, and recommended action; Anthropic's Structured Outputs documentation supports enforcing such a response contract.
NEW QUESTION # 75
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?
Answer: C
Explanation:
The current prompt asks for testing analysis at too high a level. Claude recognizes the obvious absence of an entire test but has not been instructed to construct a path-level inventory. Option B turns the desired behavior into an explicit verification procedure: enumerate each condition, alternative branch, early return, exception handler, and failure path, then locate a test assertion that exercises its behavior.
Anthropic's prompting best practices emphasize clear, specific instructions and explicit sequential steps when a task requires a defined analysis process. This change keeps the existing single review call while making the missing evaluation criterion unambiguous.
NEW QUESTION # 76
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, Glob) and integrates with Model Context Protocol (MCP) servers.
Your code review assistant needs to analyze pull requests and provide feedback on three aspects: code style compliance, potential security issues, and documentation completeness.
Each aspect requires reading files, running analysis tools, and generating a report section. The review process follows the same three-step workflow for every PR. Which task decomposition pattern is most appropriate for this workflow?
Answer: A
Explanation:
The review always follows the same predefined stages-style, security, and documentation-so each can be analyzed separately and then combined into a final report. Orchestrator-workers is better when subtasks must be determined dynamically.
NEW QUESTION # 77
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