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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Eval, Testing, and Debugging | 2.6% | - Evaluation, Testing, and Debugging |
| Topic 2: Applications and Integration | 33.1% | - Message Batches and Prompt Caching - Multimodal and Structured Outputs - API Integration and Application Development - Streaming, Error Handling and Reliability - Software Engineering Fundamentals - Claude API and Client SDKs |
| Topic 3: Model Selection and Optimization | 16.8% | - Model Selection - Performance Optimization - Model Capabilities and Trade-offs - Cost and Latency Optimization |
| Topic 4: Prompt and Context Engineering | 11% | - Prompt Engineering - Context Engineering - Context Management and Long-Context Techniques |
| Topic 5: Agents and Workflows | 14.7% | - Agent Construction with Claude - Agent Architecture - Agent Patterns and Frameworks |
| Topic 6: Tools and MCPs | 10.6% | - Building Custom Tools and MCP Servers - Model Context Protocol - Tool Use and Tool Schemas |
| Topic 7: Claude Code | 3.1% | - Claude Code Configuration and Extensibility |
| Topic 8: Security and Safety | 8.1% | - Secure Tool Use and Guardrails - Safety and Responsible Development - Application Security - Prompt Injection and Untrusted Content |
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NEW QUESTION # 75
Your Claude agent has access to a tool that retrieves customer records. A teammate has noticed that the agent occasionally calls the tool with arguments the schema does not declare, and the tool's downstream service returns an error each time. The teammate proposes loosening the schema so the tool accepts whatever arguments the model produces.
How would you respond?
Answer: D
Explanation:
The supplied question identifies B as the intended answer. Tool schemas are contracts between Claude and executable application code. When the downstream service accepts only a defined set of arguments, relaxing that schema merely shifts invalid data farther into the system and increases runtime failures.
Anthropic's current tooling provides an even stronger implementation of this principle through strict tool use
. Setting strict: true constrains tool inputs to the declared JSON Schema, preventing undeclared properties, missing required values, and incompatible parameter types where the supported schema subset is used.
Anthropic explicitly recommends strict tool use for validated parameters, type-safe function calls, and reliable agentic workflows.
In a non-strict or legacy implementation, the application should still validate arguments before dispatch and convert invalid calls into structured tool errors that Claude can interpret and potentially correct. A prompt instruction can reinforce behavior, but it should not replace deterministic validation. C and D weaken the system boundary and knowingly send invalid calls downstream.
Therefore, maintain the contract rather than adapting the contract to malformed model output.
Relevant Claude Developer topics: Agent Construction, tool schemas, strict tool use, JSON Schema, parameter validation, structured errors, retry behavior, and defensive execution boundaries .
NEW QUESTION # 76
You are building an MCP server that exposes several internal data sources as MCP resources. The server needs to be deployed so multiple Claude applications can integrate with it.
How would you approach the build and deployment?
Answer: C
Explanation:
Option A correctly treats the MCP server as a reusable integration boundary rather than application-specific code. Model Context Protocol separates capability providers from consuming Claude applications by exposing standardized tools, resources, and prompts through an MCP-compatible interface. A shared server therefore needs explicit capability definitions, an appropriate transport or communication pattern, and a deployment location reachable by its intended clients.
Anthropic's MCP documentation distinguishes remote HTTP-based integrations from local/client-managed connections. For remotely shared services, the server must be reachable from the consuming environment; client-side MCP helpers additionally support broader MCP capabilities such as resources and prompts.
B unnecessarily couples the server design to the first consumer and encourages application-specific evolution of what should be a reusable service boundary. C prevents multi-application deployment because only local developer sessions could reach the service. D abandons MCP entirely and recreates duplicated integrations in every consuming application.
Thus, A provides the correct lifecycle: define the MCP contract, expose resources/tools/prompts appropriately, select the transport according to topology, deploy the service, and let multiple applications consume the standardized interface independently. Relevant Study Guide topics: MCP architecture, reusable services, tools, resources, prompts, transports, and deployment topology.
NEW QUESTION # 77
You are designing a Claude application that will require structured JSON output for downstream processing.
The output schema is well-defined, and downstream systems will reject malformed JSON.
Answer: B
Explanation:
Option C establishes the strongest application boundary between probabilistic model generation and deterministic downstream processing. When another component requires JSON with a known contract, the application should explicitly define the expected structure and ensure that model output conforms to it before downstream execution. Anthropic's current Structured Outputs guidance states that structured outputs constrain responses to a specific schema and are intended to provide valid, parseable data for downstream processing. The current Claude API supports JSON Schema through output_config.format, while SDK helpers can additionally parse and validate returned data.
The underlying engineering principle remains the same even when structured-output enforcement is unavailable: never allow unvalidated model-generated structures to become trusted machine input. Option A provides insufficient contractual control. Option B defines the schema but pushes validation too late, increasing the probability that malformed or semantically invalid data reaches dependent components. Option D sacrifices machine reliability entirely.
Therefore, C correctly combines schema specification, explicit format guidance, and validation. This corresponds to Claude Developer topics covering structured outputs, defensive application design, schema validation, and reliable model-to-system interfaces. The question and options are reproduced from the supplied examination set.
NEW QUESTION # 78
A teammate has asked you to explain why your Claude agent's tools include detailed descriptions in the tool definition, even when the tool name is already descriptive. The teammate suggests removing the descriptions to simplify the tool definitions.
How would you respond?
Answer: A
Explanation:
The supplied exam source identifies C . Tool descriptions are part of Claude's runtime decision context. A descriptive function name can indicate what a tool appears to do, but it may not clearly tell Claude when to use it, when not to use it, what assumptions apply, or how it differs from similar tools .
Anthropic's official tool-use documentation states that Claude determines whether to call a tool based on the user's request and the tool's description. Its tool-definition guidance defines description as detailed plaintext describing what the tool does, when it should be used, and how it behaves. Anthropic's troubleshooting guidance also identifies ambiguous descriptions as a cause of selecting the wrong tool and recommends differentiating tools by when they should be used, not only by what they do.
A can supplement descriptions with examples but should not universally replace them. B incorrectly assumes names provide sufficient semantics. D removes the information from Claude's actual decision context.
Therefore, tool descriptions are operational metadata for model routing, not merely documentation for developers.
Relevant Claude Developer topics: Agent Construction, tool definitions, tool descriptions, tool selection, schema design, disambiguation, routing, and reliable tool use .
NEW QUESTION # 79
Your Claude application's prompt was written months ago and has not been updated. The team has discovered through evals that the prompt produces good results on common cases but underperforms on a specific category of inputs that has grown in volume.
How would you respond?
Answer: C
Explanation:
The correct response is iterative prompt improvement backed by evaluation , making A the appropriate choice. The supplied Claude Certified Developer Foundations material explicitly selects A. When production input distribution changes, a prompt that previously met requirements can become inadequate. The correct engineering response is not to preserve the prompt merely because it once worked; prompts are application components that should evolve with observed workload behavior.
Anthropic's official evaluation guidance describes prompt development as a cycle involving test cases, an initial prompt, iterative testing and refinement, final validation, and deployment. Success criteria should be specific and measurable, and evaluations should include representative cases that expose known failure modes.
The underperforming category should therefore be incorporated into the evaluation set. The team can modify instructions, examples, context organization, or other prompt components, then compare the revised prompt against both the newly important category and existing common cases. This prevents improvement in one segment from silently producing regressions elsewhere.
B avoids the defect instead of correcting it. C risks degrading previously successful behavior. D creates unnecessary architectural fragmentation.
Relevant Claude Developer topics: prompt iteration, evaluation-driven development, regression testing, representative test sets, prompt optimization, and production feedback loops .
NEW QUESTION # 80
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