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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Topic 2: Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Topic 3: Context Management & Reliability | 15% | - Context handling
|
| Topic 4: Tool Design & MCP Integration | 18% | - Tool integration
|
| Topic 5: Prompt Engineering & Structured Output | 20% | - Prompt design
|
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NEW QUESTION # 170
Your test-generation process produces unit tests for new code, but reviews show that 55% are low-value: trivial assertions that verify only that functions do not throw exceptions, tests that duplicate existing coverage, or tests that ignore your team's fixture conventions. How should you reduce the rate of low-value tests being generated in the first place?
Answer: D
Explanation:
The failures reflect missing project-specific knowledge: Claude does not know which fixtures are preferred, what the existing suite already covers, or what the team considers meaningful behaviour. Option C provides this information as persistent project context before test generation begins. This changes generation quality at the source instead of filtering weak tests after spending tokens to produce them.
Anthropic's CLAUDE.md documentation recommends storing shared build and test commands, coding standards, architectural decisions, conventions, and common workflows in a project CLAUDE.md. Testing guidance can define required behavioural assertions, fixture selection rules, duplication checks, naming conventions, and representative examples of acceptable and unacceptable tests.
NEW QUESTION # 171
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.
After deploying automated code review, developers report that approximately 35% of findings are false positives following consistent patterns: style suggestions that contradict team conventions, security warnings for patterns that are safe in the deployment environment, and performance suggestions that would degrade this particular use case.
You want to reduce false positives while enabling the model to generalize its judgment to novel code patterns it has not seen before.
Which approach is most effective?
Answer: D
Explanation:
Option B demonstrates the decision boundary Claude must learn. Carefully selected examples can show structurally similar code producing different outcomes based on project context-for example, an approved authentication wrapper versus an unsafe direct call, or a deliberate performance trade-off versus an accidental quadratic operation. These contrasts help Claude apply the underlying judgment to new code rather than merely memorizing prohibited phrases.
Anthropic identifies relevant, diverse, and clearly structured examples as one of the most reliable methods for improving output accuracy and consistency. It recommends several examples that mirror the real use case and cover important edge conditions. Option A risks creating an oversized negative catalogue that consumes context, becomes difficult to maintain, and cannot anticipate every future variation. Option C filters text after generation and may suppress genuine findings that happen to use the selected keywords. Option D is dangerously vague: telling a reviewer to be conservative can suppress real but uncertain defects and reduce recall. The prompt should provide paired acceptable/problematic examples, explain why each classification differs, and require concrete code evidence for every reported finding. Anthropic prompting best practices
NEW QUESTION # 172
Your automated reviewer uses a single prompt covering security issues, API design, and business-logic correctness. Your evaluation suite shows strong recall for API-design findings at 82% but poor recall for business-logic edge cases in quiz scoring at 34%. When you add few-shot examples of logic bugs to the prompt, logic recall improves to 41%, but API-design recall drops to 68%. How should you address this trade- off to improve detection across both categories?
Answer: A
Explanation:
The evaluation results demonstrate prompt interference: improving attention to business-logic examples reduces performance on API design. Option C isolates the competing objectives so each model call can apply focused terminology, examples, evidence requirements, and evaluation criteria. The resulting structured findings can then be merged, deduplicated, and ranked before being posted.
Anthropic's Building Effective AI Agents describes parallelization through sectioning, where independent aspects of a task are handled by separate model calls and subsequently aggregated. This pattern is appropriate when one prompt must evaluate several distinct considerations and a single call does not handle all of them reliably.
Option A increases context volume without resolving competition between the review objectives; irrelevant repository content may further dilute attention. Option B may improve known quiz-scoring cases but can overfit the reviewer to an incomplete checklist and does not restore API-design recall. Option D assumes that additional model capability will eliminate prompt interference without supporting evaluation evidence.
Separate specialized review passes directly address the measured failure. Each pass should be evaluated independently, followed by an end-to-end evaluation confirming that consolidation preserves recall and does not introduce duplicated findings.
NEW QUESTION # 173
An organization wants predictable outputs for automated invoice classification. Which API parameter should generally be LOWER?
Answer: C
Explanation:
Lower temperature reduces randomness during token selection, producing more consistent and repeatable outputs. This is especially valuable in structured enterprise workflows where deterministic behavior is preferred over creative variation.
NEW QUESTION # 174
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.
After adding an MCP server with specialized code-refactoring tools-extract_function, rename_variable, and inline_function-you notice that the agent still uses basic text manipulation through Write and Bash sed commands for refactoring tasks. The MCP server is connected and healthy. Examining the configuration, you find that each MCP tool has a minimal description such as, "extract_function: Extracts a function from code." What is the most effective way to improve adoption of the MCP refactoring tools?
Answer: D
Explanation:
Option C corrects the weak selection signal presented to the model. Claude chooses among available tools using their names, descriptions, parameter schemas, and the current request. "Extracts a function from code" does not explain whether the tool understands syntax trees, preserves imports, updates call sites, validates scope, or offers advantages over Write and sed. Anthropic identifies prompt-engineering tool descriptions as one of the most effective ways to improve agent tool use. Descriptions should state what the operation performs, when it should be selected, what inputs are required, what output it returns, and any limitations.
Option A adds a separate probabilistic routing layer without improving the tool contract Claude ultimately sees. Option B ignores the server's intended value. Option D removes a broadly useful capability and may prevent unrelated edits without guaranteeing that the MCP tools are used correctly. Each refactoring tool should instead describe its semantic behavior and contrast it with plain text manipulation-for example, that rename_variable performs scope-aware symbol renaming and updates references. Clear schemas, concrete examples, and evaluation against real refactoring tasks should accompany the improved descriptions.
NEW QUESTION # 175
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