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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Topic 2: Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Topic 3: Context Management & Reliability | 15% | - Context handling
|
| Topic 4: Tool Design & MCP Integration | 18% | - Tool integration
|
| Topic 5: Claude Code Configuration & Workflows | 20% | - Claude Code
|
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NEW QUESTION # 96
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.
Your automated reviewer uses a single prompt covering security issues, API design, and business-logic correctness. Your evaluation suite shows strong recall for API-design findings at 82% but poor recall for business-logic edge cases in quiz scoring at 34%. When you add few-shot examples of logic bugs to the prompt, logic recall improves to 41%, but API-design recall drops to 68%.
How should you address this trade-off to improve detection across both categories?
Answer: B
Explanation:
Option A separates competing objectives so each review call can devote its attention and examples to a coherent concern. Anthropic's Building Effective AI Agents describes sectioning as a parallelization pattern in which independent aspects are handled by separate model calls and then aggregated. It specifically notes that models generally perform better on complex tasks when each consideration receives focused attention.
The evaluation results demonstrate prompt interference: optimizing business-logic detection reduces API- design recall.
A security and API-design reviewer can use examples, terminology, and evidence criteria appropriate to interfaces and vulnerabilities. A business-logic reviewer can focus on state transitions, arithmetic boundaries, invariants, and domain-specific edge cases. Their structured findings can then be deduplicated and ranked before posting. Option B may improve only the weak category and can overfit to known quiz-scoring failures.
Option C assumes that model capacity alone removes prompt interference without evaluation evidence.
Option D adds substantial unrelated context and does not isolate the competing review objectives. The split prompts should be evaluated independently and together so consolidation does not reintroduce omissions or duplicate findings.
NEW QUESTION # 97
You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
You're tasked with adding real-time updates to the application. This could be implemented using WebSockets, Server-Sent Events, or polling, each with different complexity, browser support, and infrastructure requirements.
What's the most effective way to begin this task?
Answer: C
Explanation:
The implementation approach is unresolved and carries architectural consequences.
WebSockets, Server-Sent Events, and polling differ in connection lifecycle, bidirectional communication, proxy compatibility, scaling requirements, deployment topology, reconnection behavior, and operational complexity. Claude should therefore inspect the existing application and infrastructure before modifying code.
Anthropic recommends separating exploration and planning from implementation when the approach is uncertain, the work affects multiple files, or the developer is unfamiliar with the relevant architecture. In plan mode, Claude can read files and analyze the system without making changes, produce a detailed proposal, and allow the developer or team to review the plan before execution.
Option C creates the correct sequence: examine the frontend and backend architecture, document requirements, compare alternatives, identify infrastructure constraints, obtain approval, and only then implement the selected design with appropriate tests and observability.
NEW QUESTION # 98
You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
A customer contacts the agent about a warranty claim on a power drill. Resolving this requires multiple sequential tool calls: get_customer to look up their account, lookup_order to find the purchase details, and then either process_refund or escalate_to_human depending on warranty eligibility. You're implementing the agentic loop that orchestrates these steps using the Claude API.
What is the primary mechanism your application uses to determine whether to continue the loop or stop?
Answer: C
NEW QUESTION # 99
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.
The coordinator provides detailed step-by-step instructions to the web-search subagent, specifying exact search queries, source priorities, and date filters. Production monitoring reveals three issues: (1) the subagent reports "insufficient results" rather than trying alternative approaches when the pre-specified searches fail, (2) research quality drops for emerging topics that do not match expected patterns, and (3) the subagent rarely surfaces valuable tangential sources.
What is the most effective way to improve subagent adaptability?
Answer: A
Explanation:
Option A defines what successful research must achieve without hard-coding a brittle search procedure. The subagent receives measurable objectives-breadth, diversity, recency, and relevance-but retains authority to reformulate queries, follow promising leads, and adjust source selection when the topic does not match familiar patterns. Anthropic's account of its multi-agent research system states that subagents need an objective, expected output format, guidance about tools and sources, and clear task boundaries. This is more informative than option B's vague instruction, yet more adaptive than prescribing every query. Option C improves only one known failure path and still constrains the agent to a predefined procedure that may be unsuitable for emerging subjects. Option D adds orchestration complexity and a potentially incorrect classification step without solving over-prescription within either category. The coordinator should describe mandatory constraints separately from strategic guidance: authoritative-source requirements and time boundaries can remain fixed, while query formulation and exploration paths remain agent-controlled.
Evaluation should then measure source coverage, evidence quality, duplication, and discovery of relevant secondary leads rather than whether the subagent followed a particular series of searches.
NEW QUESTION # 100
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 hour of accumulated architectural context, while re-reading the three modified files refreshes stale information without redundantly analyzing all 12 files. This balances efficiency with codebase accuracy.
NEW QUESTION # 101
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