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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tools and Model Context Protocol (MCP) | 10.6% | - MCP server development - Tool integration and usage |
| Topic 2: Model Selection and Optimization | 16.8% | - Claude model family characteristics - Cost and token optimization - Latency and performance trade-offs |
| Topic 3: Prompt and Context Engineering | 11% | - Prompt design and structuring - Structured output handling - Context window management |
| Topic 4: Applications and Integration | 33.1% | - SDK and third-party integration - Claude Messages API - Vision capabilities - Streaming and Batch API |
| Topic 5: Claude Code | 3.1% | - Claude Code configuration and usage |
| Topic 6: Agents and Workflows | 14.7% | - Agent architecture principles - Workflow vs autonomous agents - Claude Agent SDK usage - Memory and context management |
| Topic 7: Evaluation, Testing, and Debugging | 2.6% | - Output evaluation and validation - Error handling and debugging |
| Topic 8: Security and Safety | 8.1% | - Guardrails and safety controls - AI application security |
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NEW QUESTION # 44
A Claude application is occasionally refusing to answer questions that should be in scope, including questions the application has answered correctly in the past. You want to investigate.
What is the first step of your investigation?
Answer: D
Explanation:
The supplied exam source marks B as correct. The first debugging action should be evidence collection rather than immediately modifying prompts or configuration. A trace exposes the complete conditions under which the refusal occurred: user input, system instructions, accumulated conversation state, tool results, parameters, and resulting model output.
This is important because a refusal can originate from several interacting causes. A particular input pattern may trigger it; system instructions may unintentionally conflict with the requested task; prior conversational context may alter Claude's interpretation; or contextual information may make an otherwise acceptable request appear out of scope. Without examining the failing execution path, changing instructions is speculative.
Option A jumps directly to remediation before identifying the cause. C provides useful aggregate analysis but is more appropriate after individual failing executions have been inspected. D is also valuable for comparative debugging, but determining what changed requires the detailed execution data that traces provide.
The correct troubleshooting sequence is therefore: capture or inspect representative failed traces, determine the trigger, compare against successful interactions, formulate a hypothesis, make a targeted change, and re- evaluate.
Relevant Claude Developer topics: Systems Life Cycle, observability, production debugging, tracing, failure analysis, refusals, regression investigation, and evaluation-driven remediation .
NEW QUESTION # 45
You are implementing a custom tool for your Claude agent. The tool needs to interact with an external pricing service that returns product data.
Which of the following best practices would you apply as you develop this tool?
Answer: B
Explanation:
Option D combines the three key properties of a reliable Claude tool: an explicit contract, clear tool-selection guidance, and controlled execution failure handling. Anthropic's tool documentation defines user tools using a name, detailed description, and JSON input_schema. The description should explain what the tool does, when it should and should not be used, parameter semantics, and relevant limitations. Anthropic emphasizes that precise descriptions materially improve Claude's ability to select the correct tool.
A clear schema prevents ambiguous parameter interpretation and allows validation before calling the external pricing API. Where stronger guarantees are required, Anthropic also supports strict tool use, which constrains generated tool inputs to the declared JSON Schema.
The application's execution layer must also convert pricing-service failures into explicit, handled error paths rather than uncontrolled exceptions. A deprives Claude of critical selection information. B increases malformed-call risk. C delegates infrastructure reliability to the reasoning loop instead of implementing appropriate integration error handling.
Therefore, D represents production-quality custom-tool construction. Relevant Study Guide topics: custom tools, JSON Schema, tool descriptions, validation, external API integration, and error handling.
NEW QUESTION # 46
Your team uses Claude Code across multiple repositories. You want the team's rules and general coding standards to apply to all repositories, and other rules to apply only to specific repositories. The team is currently duplicating instructions across every repository's CLAUDE.md file.
How would you address this?
Answer: A
Explanation:
B is directly supported by both the supplied examination source and Claude Code's configuration model. The source marks the hierarchical CLAUDE.md approach as correct. Claude Code supports instructions at multiple scopes, allowing broadly applicable standards to be separated from project-specific context rather than duplicated across every repository.
Anthropic documents several CLAUDE.md scopes. Organization-managed instructions can apply broadly; user-level instructions in ~/.claude/CLAUDE.md apply across a user's projects; project instructions in .
/CLAUDE.md or ./.claude/CLAUDE.md provide repository-specific architecture, conventions, commands, and workflows. Claude Code loads applicable files according to the directory hierarchy, allowing broad instructions and more specific local instructions to coexist.
This arrangement improves maintainability because common coding standards are defined once at the appropriate scope, while each repository retains only the context unique to that project. A documentation website does not automatically inject rules into Claude Code context. C creates inconsistent manual configuration. D improperly couples unrelated repositories to one repository's configuration.
Relevant Claude Developer topics: Confia Management, CLAUDE.md hierarchy, organization scope, user scope, project scope, repository configuration, instruction inheritance, and configuration reuse .
NEW QUESTION # 47
A new agent your team built handles customer support tickets, but it routinely gets confused when a single ticket spans billing, shipping, and product issues. The agent often loses track of which sub-issue it has already addressed and revisits the same one. The team is considering architectural changes.
What architectural change would you recommend?
Answer: B
Explanation:
Option C applies an orchestrator-worker architecture to a request containing several distinct domains. Rather than making one agent continuously switch between billing, shipping, and product reasoning, an orchestrator can decompose the ticket, delegate each concern to an appropriately scoped specialist, track completion, and consolidate the resulting recommendations.
Anthropic describes this architecture directly: an orchestrator dynamically breaks down a task, delegates subtasks to worker agents, and synthesizes their results. Anthropic's multi-agent Research system similarly uses a lead agent that coordinates specialized subagents operating with independent contexts.
A rigid workflow is inappropriate because not every ticket contains the same combination or ordering of issues. B can improve behavior but leaves one agent responsible for managing all competing concerns and state. D increases raw context capacity without addressing decomposition or responsibility boundaries.
C is therefore the strongest architectural change when separate issue categories can be handled independently and then reconciled by a coordinating component. Relevant Study Guide topics: orchestrator-workers, subagents, delegation, task decomposition, context isolation, coordination, and synthesis.
NEW QUESTION # 48
You are writing a system prompt for a Claude application that needs to produce output in a specific JSON shape. The downstream system will reject any output that does not match the schema.
Your prompt would need to...
Answer: D
Explanation:
The supplied question selects D . If a downstream component requires an exact machine-readable structure, the expected structure must be communicated explicitly rather than left to Claude's discretion. The prompt should define required fields, types, nesting, permissible values where relevant, and instruct Claude not to emit surrounding prose.
Anthropic's consistency guidance states that developers should precisely specify the desired output format when format consistency matters. More importantly, current Claude APIs provide Structured Outputs for cases requiring guaranteed JSON Schema conformance; Anthropic explicitly recommends Structured Outputs instead of prompt-only techniques when valid schema-compliant JSON is mandatory.
Therefore, D is the strongest prompt choice among the listed alternatives. In a contemporary production implementation, the design can be strengthened further by supplying the schema through Claude's structured- output configuration and performing downstream semantic validation where business rules exceed JSON Schema.
A permits arbitrary formatting. B intentionally creates inconsistent representations. C assumes post- processing can reliably reconstruct missing or ambiguously formatted information, which is significantly less robust than specifying the contract up front.
Relevant Claude Developer topics: system prompts, JSON formatting, structured outputs, schema constraints, output contracts, validation, and downstream integration reliability .
NEW QUESTION # 49
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