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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Context Management & Reliability | 15% | - Managing context windows and information flow - Evaluation and reliability strategies - Production deployment considerations |
| Topic 2: Claude Code Configuration & Workflows | 20% | - Claude Code usage and configuration - Developer productivity workflows - Integrating Claude Code into development processes |
| Topic 3: Tool Design & MCP Integration | 18% | - Designing effective tools for Claude applications - Tool safety, reliability, and usability - Model Context Protocol (MCP) concepts and integration |
| Topic 4: Prompt Engineering & Structured Output | 20% | - Structured output generation and validation - Prompt design strategies - Improving Claude response quality and consistency |
| Topic 5: Agentic Architecture & Orchestration | 27% | - Designing agentic systems and workflows - Selecting appropriate Claude architectures - Agent coordination and orchestration patterns |
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NEW QUESTION # 184
An architect needs Claude to produce structured JSON that downstream software can parse automatically. Which prompting strategy is BEST?
Answer: D
Explanation:
Providing an explicit schema clearly communicates the expected output format. Claude follows structured instructions effectively, making JSON generation significantly more reliable when field names, nesting, and formatting expectations are precisely described.
NEW QUESTION # 185
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.
In addition to your CI pipeline, your organization has enabled Claude's managed Code Review through the Claude GitHub App on this repository, and reviews run automatically on every pull request. Reviews average 18 findings per pull request. Developer feedback reveals three categories of unwanted noise: (1) style and formatting issues already enforced by your linter in CI, (2) findings on automatically generated template code under src/gen/, and (3) rendering- helper patterns that are intentional project conventions but get flagged because they resemble common anti-patterns. Only approximately four findings per pull request are genuine logic bugs.
What is the most effective way to reduce this noise while preserving the detection of genuine issues?
Answer: C
Explanation:
Option A uses the dedicated control surface for managed Claude Code Review. Anthropic's Code Review documentation states that a root-level REVIEW.md is injected into every review agent as the highest-priority instruction block. It can define skip paths, suppress categories already enforced by CI, recalibrate severity, cap nit volume, and require source evidence before reporting particular findings. The documentation explicitly identifies generated code, linting, and verification requirements as appropriate uses.
NEW QUESTION # 186
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: C
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.
Option A is unlikely because properly written AgentDefinition.description values already tell Claude when each agent should be used. Option C misinterprets context isolation: the parent supplies the subagent's assignment through the Agent tool's prompt, and no additional automatic forwarding setting is required.
Option D would normally produce truncation evidence or a max_tokens stop reason rather than silent absence of every invocation. The coordinator needs both agent definitions and permission to use the invocation tool.
NEW QUESTION # 187
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.
An engineer's exploration subagent spent 30 minutes analyzing a legacy payment system, reading 47 files and documenting data flows. The session was interrupted when the engineer's connection dropped. While away, a teammate merged a PR that renamed two utility functions.
The engineer wants to continue the same exploration.
What's the most effective approach?
Answer: B
Explanation:
Resuming the existing subagent preserves the expensive investigative context: files already inspected, data-flow relationships, hypotheses, and intermediate conclusions. Anthropic documents that session history contains prompts, tool calls, tool results, and responses, allowing an interrupted investigation to continue with its prior analysis intact. Subagent transcripts also persist within their parent session and can be resumed after an interruption or restart.
The engineer must nevertheless disclose the renamed utility functions. Anthropic explicitly distinguishes conversation persistence from filesystem persistence: resuming restores what the agent previously knew, but it does not freeze or snapshot the repository. Without the update, the subagent may search for obsolete symbols, misinterpret broken references, or rely on stale file paths.
NEW QUESTION # 188
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.
Production logs reveal inconsistent error handling: when lookup_order fails, the agent sometimes retries 5+ times (wasteful when the order ID doesn't exist), sometimes escalates immediately (premature for temporary network issues), and sometimes asks users for clarification (inappropriate when the issue is a backend permission error). Investigation shows your MCP tool returns uniform error responses: { " isError " : true, " content " : [{ " type " : " text " , " text " : " Operation failed " }]} . The agent cannot distinguish between error types.
What's the most effective improvement?
Answer: B
Explanation:
The agent is behaving inconsistently because every failure is represented identically. "Operation failed" contains no information about permanence, user correctability, authorization, or whether another attempt is likely to succeed. Adding structured fields converts the error into an actionable interface contract.
Anthropic instructs tool implementations to mark failures using an error flag and return information Claude can use to retry with corrected input, ask for clarification, or explain a limitation. Tool design should expose sufficient detail for the model to choose the correct next action rather than forcing it to infer the cause from a generic message. ( https://platform.claude.com/docs/en/agents-and-tools/tool-use/build-a-tool-using-agent?
utm_source=chatgpt.com )
With error_category , isRetryable , and a causal description, the agent can retry transient network failures, request corrected identifiers for validation errors, and escalate or report permission failures without pointless repetitions. Option B retries permanent validation and permission failures unnecessarily. Option C adds another tool call and failure point merely to classify information the original tool already possesses. Option D relies on parsing variable text and examples rather than providing explicit machine-readable semantics.
The tool should preserve isError: true , return a stable structured error object, include a safe customer-facing message where appropriate, and avoid exposing internal secrets or stack traces.
Official references/topics: MCP error contracts, actionable tool results, retryability metadata, agent- computer interface design.
NEW QUESTION # 189
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