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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Applications and Integration | 33.1% | - Streaming, Error Handling and Reliability - Claude API and Client SDKs - Message Batches and Prompt Caching - API Integration and Application Development - Multimodal and Structured Outputs - Software Engineering Fundamentals |
| Topic 2: Prompt and Context Engineering | 11% | - Prompt Engineering - Context Engineering - Context Management and Long-Context Techniques |
| Topic 3: Tools and MCPs | 10.6% | - Tool Use and Tool Schemas - Model Context Protocol - Building Custom Tools and MCP Servers |
| Topic 4: Security and Safety | 8.1% | - Safety and Responsible Development - Secure Tool Use and Guardrails - Application Security - Prompt Injection and Untrusted Content |
| Topic 5: Eval, Testing, and Debugging | 2.6% | - Evaluation, Testing, and Debugging |
| Topic 6: Claude Code | 3.1% | - Claude Code Configuration and Extensibility |
| Topic 7: Agents and Workflows | 14.7% | - Agent Architecture - Agent Patterns and Frameworks - Agent Construction with Claude |
| Topic 8: Model Selection and Optimization | 16.8% | - Performance Optimization - Model Capabilities and Trade-offs - Model Selection - Cost and Latency Optimization |
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NEW QUESTION # 93
A teammate is debugging a Claude application whose system prompt has grown to several hundred lines and now contains overlapping, contradictory, and obsolete instructions.
How would you advise the teammate?
Answer: A
Explanation:
Option B is correct. A large prompt containing overlapping, contradictory, or obsolete instructions creates instruction ambiguity and increases the likelihood that Claude will apply an unintended rule, over-weight irrelevant guidance, or produce inconsistent behavior.
Anthropic's current prompting guidance reinforces this principle: Claude performs best when instructions are clear, explicit, and direct. Prompt design should minimize ambiguity and provide the model with precisely the context and behavioral requirements needed for the task. Refactoring should therefore remove obsolete requirements, consolidate duplicated instructions, resolve contradictions, and establish one authoritative formulation for each behavioral rule. The revised prompt should then be regression-tested against representative inputs.
A makes an already overloaded prompt larger and relies on instruction ordering instead of eliminating conflicts. C addresses only one defect while retaining redundant and obsolete material. D does not inherently solve contradictions and may fragment authoritative instructions across multiple locations.
Therefore, B addresses the root cause. Relevant Study Guide topics: system prompt design, prompt maintainability, instruction clarity, conflicting instructions, prompt refactoring, regression evaluation, and context quality.
NEW QUESTION # 94
Your Claude application's outputs are inconsistent in format. The team's instructions are scattered across the system prompt and user messages, with some instructions stated only once and others repeated.
How would you fix the inconsistency?
Answer: A
Explanation:
Option B creates a clear separation between persistent application instructions and per-request user content.
Stable behavioral rules-role, output conventions, formatting constraints, policies, and other instructions intended to apply across requests-belong in the system-level instruction layer. User messages should primarily carry task-specific input and requirements that vary from interaction to interaction.
Anthropic's Messages API defines the system prompt specifically as the mechanism for providing overarching context and instructions to Claude. Current guidance likewise recommends the top-level system field for instructions that should apply from the beginning of the conversation, while more transient information belongs in conversation messages.
A mixes persistent application behavior with user-controlled content and weakens separation of concerns. C preserves the inconsistent architecture responsible for the problem. D duplicates instructions unnecessarily, wastes context tokens, and increases the possibility that future edits create conflicting versions of the same rule.
Consolidating stable instructions also makes prompts easier to version, evaluate, review, and maintain.
Therefore, B is the correct design. Relevant Study Guide topics: system prompts, instruction hierarchy, prompt organization, formatting consistency, separation of concerns, and maintainable prompt construction.
NEW QUESTION # 95
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 # 96
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 # 97
You are choosing between using STDIO-based communication and HTTP-based communication for an MCP server. The server will be invoked by a Claude Code session running locally.
Which communication pattern would you use?
Answer: D
Explanation:
Option D is directly supported by both the supplied examination material and Claude Code's MCP documentation. The examination source selects STDIO for this local-process scenario.
Claude Code's official MCP guidance distinguishes remote HTTP servers from local STDIO servers .
Anthropic states that STDIO servers run as local processes and communicate through standard input and standard output. They are particularly suitable for local tools requiring direct machine access or for custom scripts. Claude Code can launch the process itself and communicate with it without creating, exposing, securing, or maintaining a network listener.
HTTP is appropriate when the MCP server exists as a separately reachable network service, especially for cloud-hosted or shared services. That requirement is absent here: the server is being invoked locally from a Claude Code session.
A creates duplicate transport infrastructure without a stated availability requirement. B incorrectly assumes one transport is universally superior regardless of deployment topology. C describes HTTP polling rather than the normal MCP transport relationship and introduces unnecessary request overhead.
Therefore, local execution strongly favors STDIO; remote/shared deployment generally favors HTTP.
Relevant Claude Developer topics: MCP architecture, STDIO transport, HTTP transport, Claude Code integrations, local process communication, and deployment topology .
NEW QUESTION # 98
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