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| Section | Weight | Objectives |
|---|---|---|
| Tool Design & MCP Integration | 18% | - MCP tool, resource and prompt implementation - Tool distribution and permission controls - Error handling and tool response formatting - Tool schema design and interface boundaries - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 |
| Prompt Engineering & Structured Output | 20% | - System prompt design and persona alignment - Explicit criteria definition and few-shot prompting - JSON schema design and structured output enforcement - Validation, parsing and retry loop strategies |
| Claude Code Configuration & Workflows | 20% | - Custom slash commands and plan mode vs direct execution - CI/CD integration and non-interactive mode parameters - Path-specific rules and .claude/rules/ configuration - Hooks vs advisory instructions - CLAUDE.md hierarchy, precedence and @import rules |
| Agentic Architecture & Orchestration | 27% | - Agentic loop design and stop_reason handling - Session state management and workflow enforcement - Task decomposition and dynamic subagent selection - Error recovery, guardrails and safety patterns - Multi-agent patterns: coordinator-subagent and hub-and-spoke |
| Context Management & Reliability | 15% | - Context pruning and summarization strategies - Idempotency, consistency and failure resilience - Token budget management and cost control - Context window optimization and prioritization |
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NEW QUESTION # 42
Users report that final reports sometimes lack depth on specific subtopics. Investigation shows that the document-analysis agent frequently identifies evidence gaps--for example, noting that
"the retrieved sources discuss API authentication but lack details about token-refresh patterns." Under the current strict pipeline, this insight is not actionable because searching has already finished. What is the most effective architectural change?
Answer: B
Explanation:
Option C converts the linear pipeline into a controlled iterative research loop. The document- analysis agent is best positioned to identify precisely what the retrieved evidence fails to answer.
It should return structured gaps containing the missing question, evidence already examined, preferred source characteristics, and the coverage criterion that remains unsatisfied. The coordinator can then issue focused searches and resubmit the new material for analysis.
Anthropic's multi-agent research architecture uses a lead agent to decompose work, coordinate research agents, assess returned information, and continue investigation when further evidence is required. The guidance also stresses clear objectives, output formats, source requirements, and task boundaries to prevent gaps and duplicated work.
NEW QUESTION # 43
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.
During initial testing of the automated review pipeline, you notice that reviews on large pull requests containing more than 50 changed files sometimes take over 20 minutes and cost $8-$12 per run because of extensive agentic loops. Claude reads files, runs analysis tools, and iterates many times. Your team needs each invocation to abort once it reaches both a fixed iteration count and a fixed dollar amount, enforced by Claude Code itself rather than by the surrounding job runner.
Which configuration change directly enforces both per-invocation caps?
Answer: C
Explanation:
Option C applies the two native stopping controls required by the question. Anthropic's Claude Code CLI reference defines --max-turns as the maximum number of agentic turns permitted in print mode and --max- budget-usd as the maximum API expenditure for that invocation. When the turn limit is reached, the run exits with an error. Spending by subagents counts toward the budget cap, and current Claude Code versions stop remaining background subagents when the limit is reached. These flags therefore control the complete invocation rather than merely one model request.
Option A lowers expected cost per iteration but establishes no hard ceiling on either iterations or total expenditure. Option B limits wall-clock execution at the CI-runner level and only observes spending retrospectively; it does not enforce a Claude Code budget. Option D controls permission behavior, not iteration count, execution time, or token spending. The pipeline should use both flags, capture the resulting nonzero exit or structured error status, and report whether a review ended normally or was terminated by a configured operational limit.
NEW QUESTION # 44
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 asks the agent to understand how the caching layer works before adding a new cache-invalidation trigger. Initial Grep searches show that caching logic spans 15 files containing decorators, middleware, and service classes-approximately 8,000 lines in total.
What is the most effective next step for building understanding while managing context constraints?
Answer: A
Explanation:
Option A establishes the caching architecture before consuming thousands of implementation lines. The base class or protocol reveals the supported operations, lifecycle, cache-key rules, and invalidation contract.
Imports, subclasses, registrations, and call sites can then direct the agent toward only those concrete implementations relevant to the proposed trigger. Anthropic's effective context-engineering guidance explains that context is finite and that recall degrades as irrelevant tokens accumulate. It identifies Glob and Grep as mechanisms for retrieving code just in time rather than loading an entire repository upfront. Option B assumes file size or naming determines architectural importance; a small interface may define the system while a large service file contains incidental caching calls. Option C loads approximately 8,000 lines before establishing which portions matter. Option D is overly lexical and can miss invalidation implemented through event publication, key-version changes, inherited methods, or generically named hooks. Dependency-guided exploration provides a compact conceptual model first and expands the investigation only when concrete relationships show that additional files are relevant.
NEW QUESTION # 45
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction pipeline processes invoices and extracts line items, subtotals, tax amounts, and grand totals. During evaluation, you discover that in 18% of extractions, the sum of extracted line item amounts doesn't match the extracted grand total--sometimes due to OCR errors in the source document, sometimes due to extraction mistakes by the model. Downstream accounting systems reject records with mismatched totals.
What's the most effective approach to improve extraction reliability?
Answer: D
Explanation:
The pipeline must preserve source evidence while making inconsistencies explicit. Option D records the amount stated on the invoice separately from the total derived from extracted line items. A mismatch then becomes a machine-detectable validation condition rather than an invisible extraction defect.
This approach is superior because it does not silently overwrite source data or ask another model to guess which value is correct. Anthropic's evaluation guidance recommends automated, code- based grading whenever the criterion can be expressed deterministically. Arithmetic reconciliation is precisely such a criterion.
In production, the summation should preferably be calculated by application code using normalized decimal values, even though the option describes the model populating calculated_total. The essential design principle remains the same: preserve stated_total, compute an independent total, compare them, and route discrepancies for adjudication.
NEW QUESTION # 46
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 agent has spent 25 minutes exploring a game engine's rendering subsystem -reading shader code, buffer management, and frame synchronization logic. An engineer now asks it to understand how the physics engine integrates with rendering for collision debug overlays. You notice recent responses reference "typical rendering patterns" rather than the specific VulkanPipeline and FrameGraph classes it discovered earlier.
What's the most effective approach?
Answer: A
Explanation:
The current context is showing degradation and losing project-specific details. A focused summary preserves the relevant VulkanPipeline and FrameGraph findings, while a fresh sub- agent provides clean context for exploring physics and relating it to rendering.
NEW QUESTION # 47
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