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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tool Design & MCP Integration | 18% | - Tool integration
|
| Topic 2: Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Topic 3: Context Management & Reliability | 15% | - Context handling
|
| Topic 4: Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Topic 5: Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
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NEW QUESTION # 66
The web-search agent has gathered several relevant sources for a research topic. The document-analysis agent now needs to examine those sources. How does information typically flow between these two specialized subagents?
Answer: B
Explanation:
Option A matches the standard coordinator-managed subagent model. The coordinator invokes the web- search agent, receives its final result, selects the useful URLs or source records, and then supplies them explicitly when invoking the document-analysis agent. This keeps task ownership, provenance, and execution order visible to the coordinator.
The Claude Agent SDK subagent documentation states that a subagent begins with a fresh context window and does not inherit the parent's conversation history or previous tool results. The information passed through the Agent tool's prompt is the primary parent-to-subagent context channel. Consequently, the analysis prompt must include the source URLs, documents, retrieval notes, and any questions the analyzer must answer.
Option B requires nested delegation that is neither stated nor necessary and reduces centralized observability.
Options C and D describe valid custom architectures only if the application has deliberately implemented a queue or shared memory system; neither is automatic Agent SDK behavior. Coordinator-mediated handoff is therefore the expected approach. For reliability, the handoff should use structured source records rather than an informal prose statement, ensuring that the analyzer receives identifiers, provenance, relevant excerpts, and analysis objectives.
NEW QUESTION # 67
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've asked Claude to write a data migration script, but the initial output doesn't correctly handle records with null values in required fields. What's the most effective way to iterate toward a working solution?
Answer: A
Explanation:
A concrete failing test gives Claude an objective specification and immediate feedback. This supports targeted, iterative correction more effectively than vague instructions, a full rewrite, or manual editing.
NEW QUESTION # 68
Your update_user_profile tool accepts a user_id (required) and an optional fields_to_update object. In testing, Claude frequently omits user_id or passes incorrectly structured data. What is most critical for helping Claude understand what parameter values to provide?
Answer: B
Explanation:
Enforcing required fields and type constraints through a JSON Schema gives the agent clear rules about what parameters must be provided and their expected structure, preventing omissions or incorrectly formatted data.
NEW QUESTION # 69
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction uses tool use with a JSON schema in which property_type is defined as an enum:
house, apartment, condo, or townhouse. After deployment, 8% of extractions fail schema validation. Investigation reveals that listings mention many uncommon property types--"studio,"
"loft," "duplex," "mobile home," "tiny house," and "converted warehouse"--and new types continue appearing regularly.
What is the most effective long-term solution?
Answer: C
Explanation:
Option A is the most sustainable open-world design. The property-type domain is demonstrably not closed: uncommon values already occur, and new labels continue to appear. An enum is appropriate only when the valid vocabulary is stable and exhaustive. If the source contains a legitimate value outside that vocabulary, the schema either rejects it or pressures the model to map it inaccurately. A free-form string preserves the source meaning, while deterministic post- processing can normalize "studio," "loft," "duplex," and future terms into an evolving canonical taxonomy. Anthropic's Structured Outputs documentation distinguishes structural schema conformance from the semantic decisions made within that schema; it also documents that JSON Schema constraints such as enums govern allowable output shapes and values.
NEW QUESTION # 70
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.
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 # 71
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