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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Context Management & Reliability | 15% | - Context handling
|
| Topic 2: Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Topic 3: Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Topic 4: Tool Design & MCP Integration | 18% | - Tool integration
|
| Topic 5: Claude Code Configuration & Workflows | 20% | - Claude Code
|
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NEW QUESTION # 152
The automated review consistently flags patterns your team uses intentionally-force-unwrapping optionals in test files, using large coordinator classes that follow your established architecture, and importing internally maintained modules marked as deprecated in the public SDK. Developers are dismissing approximately 30% of all findings as project-specific false positives. Which approach prevents the model from generating these findings in the first place by supplying the project's conventions as persistent context during every review?
Answer: D
Explanation:
Option D supplies the missing repository-specific knowledge before Claude evaluates the code. The project's CLAUDE.md should state that force-unwrapping is permitted within test paths, explain the coordinator-class architecture, and identify the internally maintained deprecated module as an approved dependency. These instructions should be concise and scoped carefully so exceptions do not become global permissions.
Anthropic's CLAUDE.md documentation defines project CLAUDE.md files as persistent context for coding standards, architecture, workflows, and team conventions. They are loaded at the beginning of applicable sessions, preventing the organization from repeatedly supplying the same corrections. Anthropic also recommends recording information that Claude should know whenever it works in the repository.
Option A filters generated findings after tokens have already been consumed and can hide genuine issues containing similar terminology. Option B removes valuable surrounding context and would likely increase false positives and missed cross-file defects. Option C distributes project knowledge across individual source lines, creates maintenance overhead, and requires custom preprocessing. Centralized, narrowly scoped CLAUDE.md instructions address the root cause and improve the initial review judgment instead of repairing its output afterward.
NEW QUESTION # 153
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.
An engineer who recently joined the team asks the agent to explain the authentication and authorization architecture before making security improvements. The codebase contains more than 800 files across multiple services.
What exploration strategy will most effectively build understanding while respecting context limits?
Answer: D
Explanation:
Option D builds an evidence-based architectural map without indiscriminately loading hundreds of files.
Authentication entry points may include route handlers, middleware registration, token-validation functions, session constructors, or identity-provider callbacks. After Grep identifies those anchors, targeted Read operations can establish their responsibilities, while imports, call sites, and configuration references reveal the downstream authorization flow. Anthropic's large-codebase guidance recommends scoping Claude to the portion of a repository touched by the task because unrelated instructions and file reads consume tokens and degrade performance. Its common workflows guidance similarly recommends beginning broadly and narrowing into specific components. Option A launches agents before the relevant service boundaries are known and may produce overlapping, inconsistent investigations. Option B is an uncontrolled lexical sweep that will include tests, documentation, unrelated tokens, and incidental terminology. Option C appropriately checks project guidance but incorrectly delegates technical file selection to a new engineer who may not know the architecture. Incremental dependency tracing maintains context efficiency while producing a verifiable end-to-end map grounded in actual code paths.
NEW QUESTION # 154
Your automated code review is missing genuine bugs in pull requests. Investigation reveals that the review prompt includes this 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 findings are genuine logic errors that the model investigated but chose not to report. The team requires the review output to remain structured, with every 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: B
Explanation:
The prompt explicitly instructs Claude to suppress uncertain and lower-severity findings. Increasing reasoning depth cannot override that reporting policy: Claude may identify a real defect during analysis and still omit it from the final response. Option B separates comprehensive detection from acceptance filtering, removing the source of the false negatives while retaining metadata needed for automated decisions.
Anthropic's current code-review prompting guidance recommends reporting every issue, including uncertain or lower-severity candidates, and attaching confidence and estimated severity so a separate verification or filtering stage can rank them. A structured schema can additionally require fields such as file path, line number, category, confidence, severity, evidence, and recommended action.
Option A may improve investigation quality but leaves the suppression instruction intact. Option C removes the problematic threshold but also discards the explicit structured classification needed by downstream processing. Option D repeats the same review policy, so the second pass can suppress the same findings as the first. Option B preserves recall and machine-readable output while allowing deterministic, adjustable downstream thresholds. This is more reliable than embedding an overly restrictive acceptance decision inside the model's initial detection task.
NEW QUESTION # 155
A legal department requires Claude to answer only when sufficient evidence exists in retrieved documents. What should Claude do if evidence is insufficient?
Answer: A
Explanation:
When reliable supporting evidence is unavailable, Claude should explicitly acknowledge uncertainty rather than speculate. Transparent handling of missing information builds trust and reduces the risk of presenting unsupported legal guidance.
NEW QUESTION # 156
When researching "renewable-energy adoption," the web-search agent returns recent statistics showing 35% adoption in 2024, while the document-analysis agent extracts an 18% adoption figure from an internal 2021 report. The synthesis agent incorrectly treats the figures as contradictory instead of recognizing that they may show growth over time. What change would best enable the synthesis agent to interpret such temporal differences correctly?
Answer: B
Explanation:
The two statistics cannot be compared correctly without temporal metadata. Option A preserves when each value was measured, not merely when its document was retrieved. The structured record should ideally distinguish publication date, observation period, geographic scope, population, methodology, unit, and metric definition. The synthesis agent can then determine whether the figures represent a trend, conflicting measurements of the same period, or incomparable populations.
Anthropic's multi-agent research guidance emphasizes specifying output formats and source requirements so downstream agents receive the context required for accurate synthesis. Its structured-output capability enables these provenance fields to be required and schema validated.
Option B removes valuable historical evidence and still does not guarantee that returned sources describe the same measurement period. Option C assumes newer automatically means more reliable and could discard an authoritative historical baseline. Option D preserves older material but imposes an interpretation without examining whether methodology, geography, or definitions changed. Explicit temporal and methodological metadata allows the synthesis agent to state a defensible conclusion such as apparent growth from 18% in
2021 to 35% in 2024 while preserving any comparability caveats.
NEW QUESTION # 157
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