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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering & Structured Output | 20% | - Structured output generation and validation - Improving Claude response quality and consistency - Prompt design strategies |
| Topic 2: Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) concepts and integration - Tool safety, reliability, and usability - Designing effective tools for Claude applications |
| Topic 3: Agentic Architecture & Orchestration | 27% | - Designing agentic systems and workflows - Agent coordination and orchestration patterns - Selecting appropriate Claude architectures |
| Topic 4: Claude Code Configuration & Workflows | 20% | - Claude Code usage and configuration - Developer productivity workflows - Integrating Claude Code into development processes |
| Topic 5: Context Management & Reliability | 15% | - Production deployment considerations - Evaluation and reliability strategies - Managing context windows and information flow |
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NEW QUESTION # 132
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 # 133
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 CI pipeline performs security-focused code reviews on approximately 50 pull requests daily, currently costing $150 per day using the synchronous API. Reviews are non-blocking-developers merge after tests pass and address findings in follow-up commits. You are evaluating the Message Batches API for its 50% cost reduction.
What factor most determines whether batch processing is appropriate for this use case?
Answer: C
Explanation:
Option C identifies the fundamental trade-off introduced by batch processing: lower cost in exchange for asynchronous completion and potentially substantial latency. Anthropic states that most Message Batches complete within one hour, but results may become available only when all requests finish or after 24 hours, whichever occurs first. Therefore, the workflow must remain useful even if security findings arrive considerably later than they would through synchronous requests.
The reviews are explicitly non-blocking, so batch processing can be suitable if developers can still act on delayed findings through follow-up commits. Option B describes an implementation requirement because batch results may be returned out of submission order, but Anthropic provides a deterministic solution through each request's unique custom_id. Option A incorrectly focuses on near-instant feedback, which batch processing does not provide. Option D is not decisive because batches support independent Messages API requests containing system prompts, tool use, and multi-turn conversation content. The primary decision is therefore whether the maximum practical feedback delay is acceptable to the development workflow.
Anthropic Message Batches documentation
NEW QUESTION # 134
After the web search and document analysis subagents complete their tasks, the coordinator needs to spawn the synthesis subagent to synthesize the findings. What is the correct approach for providing the synthesis subagent with the information it needs?
Answer: C
Explanation:
The synthesis subagent requires the actual content from prior subagents to perform its task.
Including the complete findings in its prompt ensures it has the necessary context to accurately synthesize information without relying on indirect references or external state.
NEW QUESTION # 135
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.
During a billing dispute resolution, your agent successfully retrieves customer info via get_customer and order details via lookup_order , but when attempting to call process_refund , the tool returns a timeout error.
The agent has enough information to explain the charges and verify refund eligibility, but cannot actually process the refund due to the backend failure.
What approach best balances first-contact resolution with appropriate error handling?
Answer: B
Explanation:
First-contact resolution does not require pretending that every backend operation succeeded. The agent can still resolve the informational portion of the interaction by explaining the charge and confirming eligibility using the successfully retrieved customer and order data. It must then distinguish that verified conclusion from the uncompleted refund transaction.
Anthropic's tool-error guidance states that Claude should receive the failure information so it can retry, request clarification, or explain the limitation. A tool timeout must therefore be surfaced honestly rather than converted into an unsupported success claim. ( https://platform.claude.com/docs/en/agents-and-tools/tool-use
/build-a-tool-using-agent?utm_source=chatgpt.com ) Anthropic also emphasizes transparent, simple agent designs and carefully constructed tool interfaces, which support explicit disclosure of tool failure and controlled escalation. ( https://www.anthropic.com/engineering/building-effective-agents ) Option A can create an unbounded or excessively long interaction; retries should be limited and conditioned on retryability. Option B falsely represents an incomplete financial operation as completed. Option D discards the useful work already performed and escalates before providing the customer with the available explanation.
Option C preserves trust, delivers the information already established, clearly states what remains incomplete, and gives the customer a practical next step through bounded retry or human escalation.
Official references/topics: Graceful tool failure, transparent customer communication, bounded retry, human escalation.
NEW QUESTION # 136
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 test generation produces unit tests for new code, but reviews show that 55% are low-value:
trivial assertions that only verify functions do not throw exceptions, tests duplicating existing coverage, or tests ignoring your team's fixture conventions.
How do you reduce the rate of low-value tests being generated in the first place?
Answer: B
Explanation:
Option D improves the generation conditions instead of filtering defective output afterward.
Anthropic recommends using CLAUDE.md for repository-wide standards and information that Claude should apply in every session. The file can define what constitutes a useful test, require assertions on observable behavior, identify approved fixture factories, prohibit duplication of existing scenarios, and provide contrasting examples of strong and trivial tests. These instructions are available before Claude decides what tests to create.
NEW QUESTION # 137
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