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| Section | Weight | Objectives |
|---|---|---|
| Agentic Architecture & Orchestration | 27% | - Designing agentic systems and workflows - Agent coordination and orchestration patterns - Selecting appropriate Claude architectures |
| Prompt Engineering & Structured Output | 20% | - Prompt design strategies - Structured output generation and validation - Improving Claude response quality and consistency |
| Context Management & Reliability | 15% | - Evaluation and reliability strategies - Managing context windows and information flow - Production deployment considerations |
| Claude Code Configuration & Workflows | 20% | - Claude Code usage and configuration - Integrating Claude Code into development processes - Developer productivity workflows |
| Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) concepts and integration - Tool safety, reliability, and usability - Designing effective tools for Claude applications |
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NEW QUESTION # 123
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.
Compliance requires that refunds exceeding $500 must automatically escalate to a human agent-this rule cannot be left to model discretion. Despite clear system prompt instructions, production logs show the agent occasionally processes high-value refunds directly (3% failure rate).
How should you achieve guaranteed compliance?
Answer: D
Explanation:
A mandatory compliance threshold must be enforced outside probabilistic model reasoning. A PreToolUse hook can inspect every attempted process_refund call before execution, compare its amount with the $500 threshold, and deny the call when the limit is exceeded. The host application can then create the human- escalation case using the validated customer and order context.
Anthropic describes hooks as deterministic controls that ensure required actions occur instead of relying on the model to choose them. Agent SDK documentation confirms that a single denying PreToolUse hook blocks the tool call, including MCP tools matched through their qualified tool names. ( https://docs.anthropic.com/en
/docs/claude-code/hooks-guide )
Options A and B improve expected behavior but cannot guarantee compliance. The stated production failure rate demonstrates that prompting alone is insufficient. Option C prevents the refund from completing, but the subsequent escalation still depends on Claude correctly interpreting the returned error and invoking the human tool. It therefore enforces the financial block but not the complete escalation requirement.
Option D enforces both sides of the policy at the orchestration boundary: deny the unauthorized action and route the case to an approved human process. The escalation action should be idempotent and auditable.
Official references/topics: PreToolUse hooks, deterministic policy enforcement, MCP tool interception, human-approval controls.
NEW QUESTION # 124
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 system implements automatic retries when validation fails. On each retry, the specific validation error is appended to the prompt. This retry-with-error-feedback approach resolves most failures within 2? attempts.
For which failure pattern would additional retries be LEAST effective?
Answer: B
Explanation:
Retry-with-error-feedback is effective when the required information is available and the defect concerns representation. Options A, C, and D are correctable formatting failures: the model can flatten an object into an array, remove thousands separators and emit an integer, or convert a datetime into the required date-only format. The validation message supplies enough information to revise the output.
Option B is fundamentally different. The complete co-author list is absent from the model's input and exists only in an external document. No number of retries can recover evidence that was never supplied. Repeated attempts may instead increase the probability of fabrication.
Anthropic's hallucination guidance recommends restricting responses to available documents, allowing the model to acknowledge missing information, and withholding or retracting claims that cannot be grounded in the source.
The correct response is therefore to return an explicit missing-data state, retrieve the external document through a tool, or route the record for enrichment. Retrying without changing the information boundary cannot solve the problem.
Current Anthropic Structured Outputs can eliminate many shape-related failures by guaranteeing JSON Schema conformance, but they still cannot create unavailable source facts.
NEW QUESTION # 125
The coordinator agent has AgentDefinition objects configured for all four specialized subagents, each with appropriate descriptions, prompts, and tool restrictions. During testing, you notice that the coordinator correctly reasons about when to delegate--it generates messages such as, "I'll ask the web-search agent to find sources on this topic"--but no subagent execution occurs. The coordinator then proceeds as if the delegation happened and continues with incomplete information. Logs show no errors. What is the most likely cause?
Answer: D
Explanation:
Option B identifies the missing executable capability. Defining specialized agents makes their configurations available, but the coordinator must still be permitted to call the tool that invokes them. Without that tool, Claude can describe an intended delegation in ordinary text but cannot create a subagent execution.
The current Claude Agent SDK documentation requires "Agent" in allowedTools to auto-approve subagent invocations. The tool was renamed from "Task" to "Agent" in Claude Code 2.1.63, so the terminology in the original candidate question required correction. Older integrations may still expose "Task" in initialization or permission records.
NEW QUESTION # 126
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.
During testing, you observe that in extended exploration sessions (30+ minutes), the agent starts giving inconsistent answers about code structure it discussed earlier. Engineers report having to repeat context about modules they've already explored. What's the most effective approach to address this?
Answer: C
Explanation:
Long exploration sessions fill the context window and degrade recall. A persistent scratchpad externalizes important architectural findings so the agent can re-read them instead of relying solely on increasingly crowded conversation context.
NEW QUESTION # 127
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: B
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 # 128
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