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| Section | Weight | Objectives |
|---|---|---|
| Context Management & Reliability | 15% | - Context window optimization and prioritization - Context pruning and summarization strategies - Idempotency, consistency and failure resilience - Token budget management and cost control |
| 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 distribution and permission controls - Tool schema design and interface boundaries |
| Prompt Engineering & Structured Output | 20% | - System prompt design and persona alignment - Explicit criteria definition and few-shot prompting - Validation, parsing and retry loop strategies - JSON schema design and structured output enforcement |
| Claude Code Configuration & Workflows | 20% | - Custom slash commands and plan mode vs direct execution - Hooks vs advisory instructions - CI/CD integration and non-interactive mode parameters - Path-specific rules and .claude/rules/ configuration - CLAUDE.md hierarchy, precedence and @import rules |
| Agentic Architecture & Orchestration | 27% | - Session state management and workflow enforcement - Agentic loop design and stop_reason handling - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Error recovery, guardrails and safety patterns - Task decomposition and dynamic subagent selection |
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NEW QUESTION # 81
Your test-generation process produces unit tests for new code, but reviews show that 55% are low-value:
trivial assertions that verify only that functions do not throw exceptions, tests that duplicate existing coverage, or tests that ignore your team's fixture conventions. How should you reduce the rate of low-value tests being generated in the first place?
Answer: B
Explanation:
The failures reflect missing project-specific knowledge: Claude does not know which fixtures are preferred, what the existing suite already covers, or what the team considers meaningful behaviour. Option C provides this information as persistent project context before test generation begins. This changes generation quality at the source instead of filtering weak tests after spending tokens to produce them.
Anthropic's CLAUDE.md documentation recommends storing shared build and test commands, coding standards, architectural decisions, conventions, and common workflows in a project CLAUDE.md. Testing guidance can define required behavioural assertions, fixture selection rules, duplication checks, naming conventions, and representative examples of acceptable and unacceptable tests.
Option A avoids difficult directories rather than improving the model's understanding and sacrifices potentially useful automation. Option B may remove some weak tests, but it doubles model work and asks another probabilistic call to infer quality criteria that should have been stated explicitly. Option D treats line coverage as a quality metric even though a trivial test can increase coverage without validating meaningful behaviour, while a valuable regression test may cover already executed lines with better assertions. Persistent, concrete testing standards therefore offer the most direct and scalable improvement.
NEW QUESTION # 82
The synthesis agent receives summarized findings from the web-search and document-analysis agents, then passes a consolidated summary to the report generator. During testing, you discover that the generated reports make factual claims without proper citations-the report generator cannot attribute statements to their original sources because that metadata was lost during the summarization steps. What is the most effective approach to ensure proper source attribution in the final reports?
Answer: C
Explanation:
Option B preserves provenance as structured data throughout every handoff. Each finding should include the summarized claim and an associated source record containing its URL or document identifier, page or section location, relevant excerpt, and possibly retrieval date and confidence. The synthesis agent can combine claims without destroying this relationship, and the report generator can produce citations deterministically.
Anthropic's multi-agent research system retains findings until a dedicated citation stage identifies the precise source locations supporting report claims. This demonstrates that citation provenance must remain available beyond initial retrieval and summarization.
Option A attempts to reconstruct provenance after it has been discarded, which is expensive and may associate a claim with a different or weaker source. Option C preserves evidence but wastes context by transferring approximately all raw search and document content. Option D is better than losing references entirely, but embedding citations only in prose makes programmatic validation, deduplication, and claim-to- source mapping less reliable. Structured content and source fields maintain both context efficiency and auditable attribution while allowing different agents to transform the narrative safely.
NEW QUESTION # 83
The agent verifies customer identity through a multi-step process before resetting passwords.
During testing, you notice that after the customer answers the third verification question, the agent asks them to provide their name again, as if the earlier exchange never happened. What's the most likely cause of this behavior?
Answer: D
Explanation:
Claude relies on the conversation history included in each API request to maintain continuity across turns. If earlier messages are not passed forward, the agent will not have access to previously provided details and may ask for information again as though the exchange never occurred.
NEW QUESTION # 84
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 relevant components and failure path are initially unknown, so the investigation should adapt as evidence emerges. This matches the orchestrator-workers pattern, where an agent dynamically decomposes a complex task rather than following a fixed sequence or launching broad parallel work prematurely.
NEW QUESTION # 85
A prompt asks Claude to produce Markdown tables. Which addition MOST increases consistency?
Answer: C
Explanation:
Explicit structural requirements reduce ambiguity. Providing exact column names, ordering, and formatting expectations helps Claude consistently generate Markdown tables that downstream users or systems can reliably consume.
NEW QUESTION # 86
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