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| Section | Weight | Objectives |
|---|---|---|
| Claude Code | 3.1% | - Claude Code Configuration and Extensibility |
| Security and Safety | 8.1% | - Application Security - Prompt Injection and Untrusted Content - Secure Tool Use and Guardrails - Safety and Responsible Development |
| Applications and Integration | 33.1% | - Streaming, Error Handling and Reliability - Multimodal and Structured Outputs - Claude API and Client SDKs - Software Engineering Fundamentals - Message Batches and Prompt Caching - API Integration and Application Development |
| Model Selection and Optimization | 16.8% | - Cost and Latency Optimization - Model Selection - Model Capabilities and Trade-offs - Performance Optimization |
| Agents and Workflows | 14.7% | - Agent Architecture - Agent Construction with Claude - Agent Patterns and Frameworks |
| Tools and MCPs | 10.6% | - Building Custom Tools and MCP Servers - Model Context Protocol - Tool Use and Tool Schemas |
| Prompt and Context Engineering | 11% | - Context Management and Long-Context Techniques - Prompt Engineering - Context Engineering |
| Eval, Testing, and Debugging | 2.6% | - Evaluation, Testing, and Debugging |
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NEW QUESTION # 63
Your team is preparing to roll out a configuration change that updates several prompt versions across a Claude application used by multiple downstream systems. The change has already been tested in staging, but the team has not assessed how the prompt change will affect each downstream system that depends on the application's output.
What would you do before rolling out the change?
Answer: A
Explanation:
A is the appropriate configuration-change control. The supplied examination item selects A . A prompt is not merely editorial text; in a Claude application it functions as executable behavioral configuration. Changing a prompt can alter output structure, field population, language, classifications, tool-use decisions, refusal behavior, or other assumptions on which downstream consumers depend.
Staging success therefore proves only the scenarios actually covered by staging. Before production rollout, the team must perform impact analysis across every dependent system, identify contractual expectations, run representative regressions, and coordinate changes where a consumer may be affected. This is especially important where downstream applications parse structured output or expect stable semantics.
Anthropic's Structured Outputs guidance illustrates why interface contracts matter: missing fields, inconsistent types, and schema violations can break consuming applications. Even when output remains syntactically valid, prompt changes can produce semantic changes that require consumer validation.
B assumes staging coverage is universal. C communicates the change without determining its consequences.
D arbitrarily defers systems instead of assessing them.
Relevant Claude Developer topics: Confia Management, configuration impact assessment, prompt versioning, dependency management, change control, downstream contracts, regression testing, and coordinated deployment .
NEW QUESTION # 64
You are extending a Claude agent with a capability that needs to be reusable across multiple teams in the organization, with each team able to invoke and use it independently.
How would you build the capability?
Answer: D
Explanation:
Option C is correct because Skills and MCP are first-class Claude extension mechanisms intended to make capabilities reusable rather than embedding one-off logic inside a single agent. Claude documentation describes Skills as reusable knowledge, instructions, and workflows that can be loaded when relevant. It describes MCP as the standard mechanism for connecting Claude to external services, tools, prompts, and resources. Claude Code plugins can package Skills and MCP servers for distribution across projects and teams.
Option A tightly couples the capability to one team's agent and requires copying code, creating version drift and duplicated maintenance. Option B is technically reusable software, but it leaves every consuming Claude application responsible for its own integration and does not expose the capability through Claude's native extension interfaces. Option D assumes an existing built-in tool is the correct abstraction even though the scenario specifically requires an independently reusable capability.
The precise choice between Skill and MCP depends on what is being reused: use a Skill for reusable instructions/workflows; use MCP when the capability exposes external data or actions. Therefore, C best reflects the Claude Developer extension model. Relevant topics: Agent Skills, MCP, plugins, tool integration, reuse, and cross-team capability distribution.
NEW QUESTION # 65
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: D
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 # 66
Your enterprise has a contract with AWS that requires Claude API calls to flow through Amazon Bedrock rather than the direct Anthropic API. Your team is building a new Claude application and is unfamiliar with this constraint.
How would you build the application?
Answer: D
Explanation:
Option C satisfies both the enterprise routing requirement and sound application architecture. Claude is available through Amazon Bedrock, and Anthropic provides Bedrock-specific SDK integration rather than requiring applications to call api.anthropic.com directly. Current Anthropic documentation describes Claude in Amazon Bedrock as operating through AWS-managed infrastructure with AWS-native authentication, billing, and security boundaries. Newer Bedrock integrations use the Messages API shape, allowing substantial application logic to remain consistent across provider environments.
Anthropic SDKs also provide dedicated Bedrock clients-for example, Python includes AnthropicBedrockMantle for current Bedrock deployments. Keeping business logic separated from provider- specific authentication, endpoints, model identifiers, and transport configuration reduces migration and maintenance risk.
A violates architectural simplicity by duplicating every call unnecessarily. B knowingly violates the enterprise requirement until migration occurs. D directly ignores the contractual routing constraint and is therefore invalid regardless of technical feasibility.
The correct approach is to make Bedrock the configured inference provider while keeping higher-level application and agent behavior decoupled from provider-specific implementation details. Relevant Claude Developer topics are Claude API mechanics, cloud-provider integrations, Amazon Bedrock, SDK configuration, authentication boundaries, model invocation, and provider abstraction.
NEW QUESTION # 67
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 # 68
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