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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Agentic Architecture & Orchestration | 27% | - Agentic loop design and stop_reason handling - Task decomposition and dynamic subagent selection - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Session state management and workflow enforcement - Error recovery, guardrails and safety patterns |
| Topic 2: Claude Code Configuration & Workflows | 20% | - Hooks vs advisory instructions - CLAUDE.md hierarchy, precedence and @import rules - Custom slash commands and plan mode vs direct execution - Path-specific rules and .claude/rules/ configuration - CI/CD integration and non-interactive mode parameters |
| Topic 3: Tool Design & MCP Integration | 18% | - Error handling and tool response formatting - Tool schema design and interface boundaries - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool distribution and permission controls - MCP tool, resource and prompt implementation |
| Topic 4: Prompt Engineering & Structured Output | 20% | - JSON schema design and structured output enforcement - System prompt design and persona alignment - Validation, parsing and retry loop strategies - Explicit criteria definition and few-shot prompting |
| Topic 5: Context Management & Reliability | 15% | - Context window optimization and prioritization - Context pruning and summarization strategies - Token budget management and cost control - Idempotency, consistency and failure resilience |
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NEW QUESTION # 46
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 code review is missing genuine bugs in pull requests. Investigation reveals that your review prompt includes the instruction: "Only flag critical issues that would definitely cause production failures.
Ignore minor concerns and anything you are uncertain about." Developers confirm that some missed bugs are genuine logic errors that the model investigated but chose not to report. The team requires the review output to remain structured, with each finding tagged with metadata, and actionable.
Which prompt change both removes the cause of the suppressed findings and preserves structured, tagged output for downstream filtering?
Answer: D
Explanation:
Option B removes the prompt-level suppression responsible for the false negatives while preserving machine- readable metadata. Anthropic's current code-review prompting guidance warns that instructions such as "only report high-severity issues" or "be conservative" may be followed literally: Claude can identify genuine defects during analysis but omit them from its output. Anthropic recommends requesting all findings and applying filtering separately.
Confidence and severity fields allow downstream code to apply adjustable thresholds without forcing the model to discard evidence during generation. A schema can require fields such as file, line, description, severity, confidence, evidence, and recommended action; Anthropic's Structured Outputs documentation supports enforcing such a response contract. Option A repeats the same suppressive instruction and is likely to reproduce the same omissions. Option C removes the explicit reporting structure and leaves filtering behavior undefined. Option D may improve analysis depth, but extended reasoning does not override a direct instruction to suppress uncertain findings. Separating detection from deterministic filtering preserves recall, structure, and operational control.
NEW QUESTION # 47
In production, final reports frequently contain claims without proper source attribution.
Investigation shows that while the web search and document analysis agents correctly attach citations to their outputs, the synthesis agent loses track of which sources support which conclusions when combining findings. What's the most effective architectural change?
Answer: B
Explanation:
Structured claim-to-source mappings ensure that the synthesis agent can merge findings without losing attribution. By preserving these explicit links through the workflow, the final report can include accurate citations for all claims, maintaining reliability and traceability.
NEW QUESTION # 48
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.
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: B
Explanation:
The Claude Messages API is stateless. Each API request must include the conversation history that Claude requires for the current response. If the application sends only the customer's third verification answer, Claude does not automatically retrieve the earlier turns containing the customer's name and previous answers.
From the model's perspective, that information is absent.
Anthropic's Messages API documentation states that applications must send the full conversational history to build a multi-turn interaction. The correct implementation appends each user message and assistant response to the messages array and resends the accumulated sequence on every subsequent request. ( https://platform.
claude.com/docs/en/build-with-claude/working-with-messages )
Option A cannot restore information that was omitted from the request. Instructions to "remember" do not create server-side conversational state. Option C describes application-specific behavior for which the scenario provides no evidence. Option D is factually incorrect: Claude does not have a default two-turn retention limit. Its effective conversational awareness depends on the messages provided and the model's context window.
For a verification workflow, the application should also persist structured verification state separately from prose history. That state may include completed checks, pending checks, attempt counts, and a verified customer identifier. This improves reliability while the conversational transcript preserves the natural interaction.
Official references/topics: Stateless Messages API, multi-turn history construction, structured workflow state, conversational continuity.
NEW QUESTION # 49
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 implementing a complex graph traversal algorithm with specific performance requirements and edge cases to handle (disconnected nodes, cycles, weighted edges). You want to structure your workflow for efficient iterative refinement with Claude. What approach will most effectively enable progressive improvement across multiple iterations?
Answer: A
Explanation:
Tests provide an executable specification and objective feedback for each iteration. Claude can implement against the suite, run it, inspect failures, and progressively refine the algorithm until correctness and performance requirements are met. Anthropic recommends running tests and fixing failures as an iterative Claude Code workflow.
NEW QUESTION # 50
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: A
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.
Option B configures a self-hosted GitHub Actions workflow, but the scenario concerns the separate managed Code Review service running on Anthropic's infrastructure. Instructions in that workflow do not control the managed reviewer. Option C provides useful general project context, but CLAUDE.md has lower review- specific authority: managed Code Review treats violations of it primarily as nit-level findings. REVIEW.md is the stronger and more precise mechanism for changing what the managed service reports. The file should skip src/gen/**, suppress style issues already enforced by CI, and require concrete behavioral evidence for rendering-helper warnings while continuing to report verified correctness and security defects.
NEW QUESTION # 51
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