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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tool Design & MCP Integration | 18% | - Error handling and tool response formatting - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool distribution and permission controls - Tool schema design and interface boundaries - MCP tool, resource and prompt implementation |
| Topic 2: Prompt Engineering & Structured Output | 20% | - Validation, parsing and retry loop strategies - System prompt design and persona alignment - Explicit criteria definition and few-shot prompting - JSON schema design and structured output enforcement |
| Topic 3: Context Management & Reliability | 15% | - Context pruning and summarization strategies - Idempotency, consistency and failure resilience - Context window optimization and prioritization - Token budget management and cost control |
| Topic 4: Claude Code Configuration & Workflows | 20% | - Hooks vs advisory instructions - CLAUDE.md hierarchy, precedence and @import rules - Path-specific rules and .claude/rules/ configuration - CI/CD integration and non-interactive mode parameters - Custom slash commands and plan mode vs direct execution |
| Topic 5: Agentic Architecture & Orchestration | 27% | - Agentic loop design and stop_reason handling - Error recovery, guardrails and safety patterns - Session state management and workflow enforcement - Task decomposition and dynamic subagent selection - Multi-agent patterns: coordinator-subagent and hub-and-spoke |
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NEW QUESTION # 94
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.
In addition to your CI pipeline, your organization has enabled Claude's managed Code Review through the Claude GitHub App on this repository, and reviews run automatically on every pull request. Reviews average
18 findings per pull request. Developer feedback reveals three categories of unwanted noise: (1) style and formatting issues already enforced by your linter in CI, (2) findings on automatically generated template code under src/gen/, and (3) rendering-helper patterns that are intentional project conventions but get flagged because they resemble common anti-patterns. Only approximately four findings per pull request are genuine logic bugs.
What is the most effective way to reduce this noise while preserving the detection of genuine issues?
Answer: A
Explanation:
Option A uses the dedicated control surface for managed Claude Code Review. Anthropic's Code Review documentation states that a root-level REVIEW.md is injected into every review agent as the highest-priority instruction block. It can define skip paths, suppress categories already enforced by CI, recalibrate severity, cap nit volume, and require source evidence before reporting particular findings. The documentation explicitly identifies generated code, linting, and verification requirements as appropriate uses.
Option B configures a self-hosted GitHub Actions workflow, but the scenario concerns the separate managed Code Review service running on Anthropic's infrastructure. Instructions in that workflow do not control the managed reviewer. Option C provides useful general project context, but CLAUDE.md has lower review- specific authority: managed Code Review treats violations of it primarily as nit-level findings. REVIEW.md is the stronger and more precise mechanism for changing what the managed service reports. The file should skip src/gen/**, suppress style issues already enforced by CI, and require concrete behavioral evidence for rendering-helper warnings while continuing to report verified correctness and security defects.
NEW QUESTION # 95
After deploying automated code review, developers report that approximately 35% of flagged findings are false positives falling into consistent patterns: style suggestions contradicting team conventions, security warnings for patterns that are safe in your deployment context, and performance suggestions that would degrade your specific use case. You want to reduce false positives while maintaining the ability to catch genuine issues. Which approach best enables the model to generalize its judgment to novel code patterns it has not seen before?
Answer: D
Explanation:
Option B teaches Claude the actual decision boundaries behind the team's judgments.
Contrasting examples can demonstrate why one apparent security pattern is acceptable within the organization's deployment controls while a superficially similar implementation is genuinely unsafe. The same technique can distinguish established style conventions from deviations and intentional performance trade-offs from accidental inefficiencies.
Anthropic identifies relevant, diverse, and clearly structured examples as one of the most reliable ways to improve Claude's accuracy and consistency. Its official prompting guidance recommends examples that closely mirror the production task while covering important variations and edge cases. This encourages generalization based on demonstrated criteria instead of memorization of prohibited wording.
NEW QUESTION # 96
You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
When analyzing complex legal cases that cite multiple precedents, the document analysis subagent processes each sequentially. A landmark case citing 12 precedents takes over 3 minutes to analyze completely. What's the most effective way to reduce this latency while preserving the coordinator's ability to monitor and debug the system?
Answer: C
Explanation:
Parallelizing independent precedent analyses reduces latency, while keeping delegation at the coordinator level preserves centralized visibility, tracing, and debugging. Recursive delegation would make monitoring more difficult.
NEW QUESTION # 97
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 codebase exploration tool stores session IDs to allow engineers to continue investigations across work sessions. An engineer spent an hour yesterday analyzing a legacy authentication module, building context about its architecture and dependencies. They want to continue today. The session ID is valid, but version control shows 3 of the 12 files the agent previously read were modified overnight by a teammate's merge.
What approach best balances efficiency and accuracy?
Answer: B
Explanation:
Resuming preserves the prior architectural analysis, dependency mapping, and conversation history, avoiding the cost of repeating an hour of valid work. Anthropic states that resuming by session ID restores the agent's full prior context, including files read, analysis performed, and decisions made. ( https://code.claude.com/docs
/en/agent-sdk/sessions )
However, the session represents historical knowledge rather than a repository snapshot. Because three files changed externally, the agent must be told which files are affected and directed to re-read them before relying on earlier conclusions. Anthropic clarifies that sessions persist the conversation, not the filesystem. (
https://code.claude.com/docs/en/agent-sdk/sessions ) Targeted re-analysis updates the stale portion of the agent's model while retaining the nine unchanged files' established context.
Option A discards valuable work even though most of the analyzed code remains unchanged. Option C is accurate but inefficient because it reprocesses all twelve files regardless of whether they changed. Option D is unsafe: the agent may continue reasoning from obsolete function signatures, dependencies, or control flows.
The continuation prompt should identify the changed files, summarize the merge's purpose where known, and request a focused comparison against the earlier understanding. Any architectural conclusions affected by those changes should then be revised explicitly.
Official references/topics: Session Resume; Filesystem Versus Conversation State; Targeted Re-Analysis; Repository Change Awareness.
NEW QUESTION # 98
The web search agent has gathered several relevant sources for a research topic. The document analysis agent now needs to examine these sources. How does information typically flow between these two specialized subagents?
Answer: D
Explanation:
In a coordinated multi-agent pipeline, the coordinator mediates information flow. It collects outputs from the web search agent and includes the relevant findings in the prompt when invoking the document analysis agent, ensuring proper sequencing and context without tightly coupling the subagents.
NEW QUESTION # 99
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