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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Security and Safety | 8.1% | - Guardrails and safety controls - AI application security |
| Topic 2: Tools and Model Context Protocol (MCP) | 10.6% | - Tool integration and usage - MCP server development |
| Topic 3: Claude Code | 3.1% | - Claude Code configuration and usage |
| Topic 4: Applications and Integration | 33.1% | - Claude Messages API - Streaming and Batch API - Vision capabilities - SDK and third-party integration |
| Topic 5: Evaluation, Testing, and Debugging | 2.6% | - Output evaluation and validation - Error handling and debugging |
| Topic 6: Model Selection and Optimization | 16.8% | - Latency and performance trade-offs - Claude model family characteristics - Cost and token optimization |
| Topic 7: Prompt and Context Engineering | 11% | - Prompt design and structuring - Structured output handling - Context window management |
| Topic 8: Agents and Workflows | 14.7% | - Memory and context management - Agent architecture principles - Workflow vs autonomous agents - Claude Agent SDK usage |
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NEW QUESTION # 64
The Claude application your team built has grown over six months, and the prompt-handling code has accumulated duplication and tangled control flow. The functionality is working, but new features are getting harder to add.
How would you address this?
Answer: D
Explanation:
The supplied Claude Developer item selects A . The symptoms-duplicated logic, tangled control flow, and increasing difficulty adding features-indicate accumulated technical debt in a component that has become structurally difficult to change. Because the problem is already materially reducing development velocity, deliberate refactoring should occur before additional feature pressure compounds it.
The objective is behavioral preservation with structural improvement. Duplicated prompt construction should be consolidated where the behavior is genuinely shared. Prompt preparation, configuration, API invocation, validation, error handling, and post-processing should have clear responsibilities. Control flow should be simplified so each stage is testable independently. Existing tests should first capture important current behavior so refactoring can proceed without silently changing application semantics.
B can be appropriate for minor cleanup, but the question describes broad systemic complexity. Mixing substantial structural refactoring into unrelated feature tickets makes scope, review, and regression analysis harder. C knowingly allows the debt to compound. D centralizes complexity rather than removing it and violates separation of concerns.
Relevant Claude Developer topics: SW Eng Foundations, refactoring, technical debt, DRY, separation of concerns, maintainability, modular design, regression testing, and control-flow simplification .
NEW QUESTION # 65
Your Claude agent has too many tools, and many of them have overlapping functionality. The agent often picks an inappropriate tool when several could plausibly handle a request.
How would you address the tool selection problem?
Answer: B
Explanation:
Option D addresses the architectural cause of the failure: an ambiguous tool surface. Claude chooses tools partly from their names, descriptions, schemas, and the relationship between the request and the capability described. If multiple tools appear to perform substantially the same task, selection becomes unnecessarily difficult.
Anthropic's tool-definition guidance explicitly recommends consolidating related operations into fewer tools and making descriptions clear about both what a tool does and when it should be used. The documentation notes that fewer, more capable tools reduce selection ambiguity and make the available tool surface easier for Claude to navigate.
Option A makes the ambiguity worse by expanding an already overlapping tool set. B destroys required application capabilities. C could improve selection somewhat, because examples can clarify complex inputs, but it leaves the underlying duplication intact. Examples complement good tool design; they are not a substitute for distinct tool responsibilities.
Therefore, D is the strongest solution: remove unused tools, merge functions that represent the same conceptual operation, and write discriminative descriptions defining appropriate and inappropriate usage.
Relevant Study Guide topics: tool design, tool selection, descriptions, tool consolidation, agent construction, and reducing ambiguity.
NEW QUESTION # 66
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: C
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 # 67
You are designing a Claude application that maintains user sessions across multi-turn conversations. The product team has asked how the application will handle session lifecycle: when sessions should expire, how state is reset, and how the application avoids carrying stale context into new conversations.
How would you design session lifecycle?
Answer: D
Explanation:
A is the correct lifecycle architecture. The supplied Claude Certified Developer Foundations item explicitly selects A . A session is a state boundary, so its lifecycle must define when accumulated conversation state remains valid and when that state must be discarded. Anthropic's Managed Agents documentation establishes that a session maintains conversation history across multiple interactions and provides explicit session operations including creation, update, archive, and deletion.
A therefore addresses all three required controls: expiration , reset , and fresh-session creation . Expiration prevents indefinitely retained conversational state. Reset triggers allow the application to clear state when events such as logout, workflow completion, tenant switching, or explicit user reset occur. Fresh-session rules prevent context belonging to an earlier task from influencing a logically unrelated conversation.
B relies exclusively on timeout and does not cover event-driven resets. C places lifecycle correctness on users instead of the application. D applies global reset behavior and omits independent per-session expiration semantics.
Relevant Claude Developer topics: Agent Architecture, session lifecycle, multi-turn state, context boundaries, state reset, stale-context prevention, and session management .
NEW QUESTION # 68
Your application uses the Messages API to handle multi-turn conversations. Each new turn resends the entire conversation history, and your token costs are growing as conversations get longer. You suspect there is a more efficient approach.
How would you address this?
Answer: C
Explanation:
Option A is the technically verified answer. Anthropic documents that multi-turn agentic requests resend the growing conversation context on subsequent turns. Prompt caching allows repeated prompt prefixes-such as system instructions, tool definitions, and prior unchanged conversation content-to be reused at the lower cache-read rate rather than repeatedly charged as ordinary uncached input. Anthropic specifically identifies caching repeated context as a major production cost optimization.
B is incorrect even though the supplied PDF contains a duplicated screenshot in which B appears selected.
The Batch API is designed for asynchronous workloads that can tolerate delayed completion; it is not the appropriate mechanism for every interactive turn in a multi-turn conversation. Because the user requested verified answers rather than blindly reproducing marked selections, A is retained.
C discards potentially essential conversation state arbitrarily. D is a valid context-management technique in some long-running workflows, but summarizing after every turn creates additional model work and loses detail; it does not exploit repeated-prefix caching.
Therefore, A directly addresses the stated cost pattern while preserving conversation fidelity. Relevant Study Guide topics: Messages API, stateless conversation history, prompt caching, cached input tokens, multi-turn applications, token economics, and cost optimization.
NEW QUESTION # 69
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