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| Section | Weight | Objectives |
|---|---|---|
| Tool Design & MCP Integration | 18% | - MCP tool, resource and prompt implementation - Error handling and tool response formatting - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool schema design and interface boundaries - Tool distribution and permission controls |
| Agentic Architecture & Orchestration | 27% | - Session state management and workflow enforcement - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Error recovery, guardrails and safety patterns - Agentic loop design and stop_reason handling - Task decomposition and dynamic subagent selection |
| Claude Code Configuration & Workflows | 20% | - Custom slash commands and plan mode vs direct execution - CLAUDE.md hierarchy, precedence and @import rules - Path-specific rules and .claude/rules/ configuration - CI/CD integration and non-interactive mode parameters - Hooks vs advisory instructions |
| Context Management & Reliability | 15% | - Context window optimization and prioritization - Context pruning and summarization strategies - Token budget management and cost control - Idempotency, consistency and failure resilience |
| Prompt Engineering & Structured Output | 20% | - System prompt design and persona alignment - Validation, parsing and retry loop strategies - JSON schema design and structured output enforcement - Explicit criteria definition and few-shot prompting |
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NEW QUESTION # 102
A company wants Claude to summarize thousands of support tickets efficiently. Which design scales BEST?
Answer: D
Explanation:
Hierarchical summarization is an effective strategy for large datasets. Claude summarizes smaller batches first, then combines intermediate summaries into higher-level reports, improving scalability while maintaining important information.
NEW QUESTION # 103
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.
An engineer asks the agent to find all files in the monorepo that import the @company/auth package to understand how authentication is used across services. Which built-in tool is most appropriate for this task?
Answer: C
Explanation:
Grep searches file contents across the repository, making it the correct tool for locating every source file that imports @company/auth. Glob searches filenames and paths, not code content.
NEW QUESTION # 104
The web-search agent has gathered several relevant sources for a research topic. The document-analysis agent now needs to examine those sources. How does information typically flow between these two specialized subagents?
Answer: B
Explanation:
Option A matches the standard coordinator-managed subagent model. The coordinator invokes the web- search agent, receives its final result, selects the useful URLs or source records, and then supplies them explicitly when invoking the document-analysis agent. This keeps task ownership, provenance, and execution order visible to the coordinator.
The Claude Agent SDK subagent documentation states that a subagent begins with a fresh context window and does not inherit the parent's conversation history or previous tool results. The information passed through the Agent tool's prompt is the primary parent-to-subagent context channel. Consequently, the analysis prompt must include the source URLs, documents, retrieval notes, and any questions the analyzer must answer.
Option B requires nested delegation that is neither stated nor necessary and reduces centralized observability.
Options C and D describe valid custom architectures only if the application has deliberately implemented a queue or shared memory system; neither is automatic Agent SDK behavior. Coordinator-mediated handoff is therefore the expected approach. For reliability, the handoff should use structured source records rather than an informal prose statement, ensuring that the analyzer receives identifiers, provenance, relevant excerpts, and analysis objectives.
NEW QUESTION # 105
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.
In production, you observe that simple fact-checking queries, such as "In what year was the Paris Climate Agreement signed?", traverse all four subagents sequentially, consuming more than 40 seconds and significant tokens per query. Complex comparative research benefits from the complete pipeline. Your query distribution is diverse and continues to evolve as users discover new applications.
What is the most effective approach to optimize for varying query complexity?
Answer: A
Explanation:
Option D allows orchestration effort to scale with the actual request. A simple factual query may require only the web-search agent and a direct coordinator response, whereas a comparative investigation may require web research, document analysis, synthesis, and report generation. Anthropic's Building Effective AI Agents describes the orchestrator-workers pattern as a central model dynamically identifying subtasks, delegating them, and combining the results. It is specifically appropriate when the required subtasks cannot be predicted reliably in advance. Anthropic's multi-agent research architecture likewise emphasizes varying the number of agents and tool calls according to task complexity. Option A introduces an inflexible binary decision and still sends every non-factual request through the full pipeline. Option B requires labeled data, ongoing retraining, and reliable definitions of the "optimal" agent combination. Option C is easier to implement but will become brittle as new query types appear. A capable coordinator can examine the requested output, necessary evidence, source requirements, and analytical depth at runtime, then invoke only the specialists that materially contribute to the answer.
NEW QUESTION # 106
Your pipeline includes a release-notes generation step that classifies and summarizes approximately 200 commits at the end of each weekly release cycle. Each commit is currently sent as a separate Messages API call using a Sonnet-tier Claude model. The release notes are not needed until the following morning, so results have approximately 12 hours of acceptable latency. Your team needs to reduce per-token API cost for this step while keeping the same model and prompts, with no change to the model tier or output quality. Which approach satisfies all these constraints?
Answer: C
Explanation:
Option C preserves each commit's existing model, prompt, and independent processing structure while applying Anthropic's reduced batch pricing. The official Message Batches documentation states that batch processing reduces input and output token costs by 50%. Requests are processed independently and asynchronously, and each response can be correlated with its original commit through a unique custom_id.
This workload is a strong batch candidate because it contains many independent requests and does not require an immediate response. Most batches complete within one hour, although processing can continue for up to
24 hours. The pipeline must therefore tolerate asynchronous completion and retrieve the results when processing finishes.
Option A changes the request architecture, increases the risk of context or output-limit failures, and makes individual retry and result association more difficult. Reducing request count does not inherently reduce per- token pricing. Option B improves throughput but concurrency does not change token rates. Option D would reduce cost but violates the explicit requirement to retain the same model tier. The Message Batches API is the only option satisfying every stated constraint.
NEW QUESTION # 107
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