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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Agentic Architecture & Orchestration | 27% | - Agentic loop design and stop_reason handling - Task decomposition and dynamic subagent selection - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Error recovery, guardrails and safety patterns - Session state management and workflow enforcement |
| Topic 2: Prompt Engineering & Structured Output | 20% | - System prompt design and persona alignment - JSON schema design and structured output enforcement - Explicit criteria definition and few-shot prompting - Validation, parsing and retry loop strategies |
| Topic 3: Context Management & Reliability | 15% | - Context window optimization and prioritization - Idempotency, consistency and failure resilience - Token budget management and cost control - Context pruning and summarization strategies |
| Topic 4: Tool Design & MCP Integration | 18% | - Tool schema design and interface boundaries - Error handling and tool response formatting - MCP tool, resource and prompt implementation - Tool distribution and permission controls - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 |
| Topic 5: Claude Code Configuration & Workflows | 20% | - CI/CD integration and non-interactive mode parameters - Custom slash commands and plan mode vs direct execution - CLAUDE.md hierarchy, precedence and @import rules - Hooks vs advisory instructions - Path-specific rules and .claude/rules/ configuration |
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NEW QUESTION # 154
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.
A developer asks the agent to investigate why a specific API endpoint intermittently returns 500 errors. The codebase has 200+ files and the developer doesn't know which components are involved. The agent must trace the error through routing, middleware, business logic, and database layers.
What task decomposition approach would be most effective?
Answer: C
Explanation:
The investigation path cannot be reliably predetermined because the responsible files, components, and execution sequence are unknown. The agent should begin with available evidence-such as route definitions, stack traces, logs, or endpoint references-and use each discovery to decide the next search, file read, or diagnostic action.
Anthropic distinguishes predefined workflows from agents that dynamically direct their own processes and tool usage. Agents are appropriate for open-ended problems where the required number and nature of the steps cannot be predicted or encoded as a fixed path. During execution, the agent should obtain ground truth from tool results and adapt its plan based on that environmental feedback. ( https://www.anthropic.com
/engineering/building-effective-agents )
Option A requires a comprehensive plan before the agent has inspected the code, so the plan would rest on unsupported assumptions. Option B forces every investigation through the same sequence even when an early discovery makes later steps irrelevant or identifies a different dependency path. Option C assumes the four layers can be investigated independently; tracing an intermittent request failure usually involves dependencies revealed sequentially across layers.
Option D implements an adaptive agent loop: inspect, form a hypothesis, use tools, evaluate the evidence, and generate the next subtask. The workflow should still include stopping conditions, testable hypotheses, and escalation when evidence remains inconclusive.
Official references/topics: Adaptive Agent Loops, Dynamic Task Decomposition, Tool Feedback, Open- Ended Coding Investigations.
NEW QUESTION # 155
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: C
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 # 156
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.
After the web-search agent finds 25 sources containing 120,000 tokens of raw content, the document-analysis agent extracts 15,000 tokens of key insights, and the synthesis agent produces a coherent 3,000-token narrative draft, the coordinator must pass context to the report-generation agent for the final output with proper source citations.
What context-passing strategy provides the best balance of completeness and efficiency?
Answer: D
Explanation:
Option A gives the report generator the finished narrative context plus the evidence required to verify and cite its claims. The 3,000-token synthesis draft supplies organization and conclusions, while the source index preserves URLs, supporting excerpts, and claim-to-source mappings without transferring 120,000 tokens of raw search material. Anthropic's effective context-engineering guidance recommends curating the smallest high-signal context and having subagents return condensed, distilled results rather than flooding later stages with their complete working history. Anthropic's citation guidance likewise depends on retaining specific supporting passages and source locations. Option B is complete but inefficient and introduces context pollution that can reduce attention to relevant evidence. Option C separates citation insertion from the reasoning that produced the claims, making incorrect or unsupported matches more likely. Option D retains source names but discards the passages needed to demonstrate that each source actually supports the associated claim. The structured index should use stable source IDs and include only the excerpts necessary for the report's factual claims, with validation that every cited claim maps to at least one evidence entry.
NEW QUESTION # 157
A retail organization observes that Claude occasionally gives different wording when summarizing identical customer reviews. The summaries remain accurate. What is the MOST likely explanation?
Answer: B
Explanation:
Claude generates text probabilistically, meaning multiple valid outputs may exist for the same prompt. Small wording differences are expected even when factual content remains consistent.
Lower temperature can improve consistency if deterministic outputs are preferred.
NEW QUESTION # 158
Your agent uses three tools: get_property_details(property_id) returns data including street address, get_price_history(property_id) returns historical pricing, and get_neighborhood_info(address) returns area statistics. You observe that get_neighborhood_info always requires get_property_details first just to extract the address, even when users specify the property by ID. This creates unnecessary latency and failure coupling - if the first call fails, the neighborhood request also fails. What tool design change best addresses this?
Answer: A
Explanation:
Allowing the neighborhood tool to accept the same identifier the user provides removes the need for the agent to chain through another tool just to extract an intermediate value. Resolving the address internally reduces latency, avoids unnecessary dependency on a prior tool call, and makes the workflow more reliable.
NEW QUESTION # 159
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