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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Model Selection and Optimization	16.8%	- Performance and Cost Optimization 1. Apply prompt caching and other cost optimization techniques 2. Use batching and other approaches to improve efficiency - Model Selection 1. Select appropriate Claude models for task requirements 2. Evaluate quality, latency, capability, and cost tradeoffs
Eval, Testing, and Debugging	2.6%	- Evaluation 1. Design and run evaluations for Claude-powered applications 2. Interpret evaluation results and improve application quality - Testing and Debugging 1. Diagnose and debug application, agent, and integration issues 2. Test Claude integrations and agentic systems
Claude Code	3.1%	- Claude Code Configuration and Usage 1. Configure settings and development environments 2. Use CLAUDE.md and project configuration 3. Use Skills, plugins, and Claude Code capabilities effectively
Prompt and Context Engineering	11%	- Context Engineering 1. Manage context windows and application context 2. Apply context management strategies for agents and long-running workflows - Prompt Engineering 1. Design prompts appropriate to application requirements 2. Apply prompting techniques to improve reliability and output quality
Agents and Workflows	14.7%	- Agent Architecture and Tradeoffs 1. Evaluate tradeoffs between agentic approaches and traditional workflows 2. Select appropriate agent architectures and patterns - Claude Agent SDK and Agent Loops 1. Build and configure agents using the Claude Agent SDK 2. Implement and manage custom agent loops - Subagents and Agentic Frameworks 1. Use subagents and coordinate multi-agent workflows 2. Apply appropriate agentic frameworks and orchestration patterns
Applications and Integration	33.1%	- Application Development and Integration 1. Build and ship production-grade Claude-powered applications 2. Handle multimodal and structured application inputs and outputs 3. Integrate Claude capabilities into existing software systems and workflows - Claude API and Client SDKs 1. Implement streaming and handle API errors 2. Integrate applications with the Claude API and supported client SDKs 3. Construct and process API requests and responses
Tools and MCPs	10.6%	- Tool Development and Integration 1. Design and implement custom tools for Claude applications and agents 2. Handle tool schemas, invocation, and tool-use results - Model Context Protocol 1. Apply MCP concepts and patterns for connecting models to external capabilities 2. Build and integrate MCP servers
Security and Safety	8.1%	- Secure Application Design 1. Apply secure-by-design practices to Claude-powered applications 2. Protect sensitive data and manage access appropriately - Safety and Guardrails 1. Use hooks and other mechanisms to enforce application controls 2. Implement safety controls and guardrails

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q15-Q20):

NEW QUESTION # 15
A teammate is reviewing the team's threat model for a Claude application and has asked you to identify the categories of AI-specific threats that the model should cover. The teammate has already listed traditional web application threats and wants to know what additional categories apply to a Claude application.
Which AI-specific threat categories would you add?

A. Prompt injection, data leakage from prompts or context, jailbreak attempts, and unsafe model output that bypasses application controls.
B. Network-level denial of service and physical infrastructure attacks, because these categories cover the threats most likely to affect any Claude application in production.
C. Supply chain attacks on the Claude SDK because the SDK itself is the only point of vulnerability that a Claude application introduces beyond traditional web application threats.
D. Cross-site scripting and SQL injection, because these traditional web application threats apply with equal weight to any application that uses Claude in any way.

Answer: A

Explanation:
Option C correctly identifies threat categories introduced or significantly amplified by LLM-based application architecture. Prompt injection attempts to manipulate Claude into following adversarial instructions.
Jailbreaks seek to circumvent behavioral or application safeguards. Data leakage can expose confidential information contained in system prompts, retrieved context, tool results, or conversation state. Unsafe model output becomes particularly serious when output is consumed by downstream systems or translated into tool actions.
Anthropic's official guardrail documentation explicitly distinguishes jailbreaks and direct prompt injection from indirect prompt injection. It recommends input screening, hardened system prompts, structured handling of untrusted tool content, least-privilege access, output screening, and continuous monitoring. Anthropic also warns that sensitive prompt/context data can leak and recommends post-processing and output filtering where required.
Traditional threats such as SQL injection or XSS remain relevant to the surrounding application, but they are not the additional AI-specific categories requested. B similarly describes conventional infrastructure threats.
D is far too narrow because the SDK is only one component of the attack surface.
The supplied exam source explicitly marks C. Relevant topics: Claude App Design, threat modeling, prompt injection, jailbreaks, data leakage, output safety, least privilege, and defense in depth.

NEW QUESTION # 16
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?

A. Define the tool with a clear schema, write a precise description for when to call it, and handle pricing service errors explicitly.
B. Omit the tool description and let the model infer when to use the tool based on the tool's name and the rest of the prompt context.
C. Implement the tool with no error handling and let the agent loop catch failures whenever the pricing service returns an error during operation.
D. Define the tool with a loose schema and let the model interpret the inputs flexibly on each call the agent makes.

Answer: A

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 # 17
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?

A. Build the MCP server with resource and tool definitions scoped to the first Claude application that needs it, and extend the definitions to additional applications as each integration is requested.
B. Deploy the MCP server only on individual developer machines, with the Claude applications unable to reach the server outside each developer's machine.
C. Bypass the MCP server and embed each data source directly in every Claude application that needs the data, with each application maintaining its own integration.
D. Author the server with clearly defined resources, tools, and prompts, choose a communication pattern, and deploy to an accessible hosting environment.

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 # 18
Your Claude application uses tool calling to fetch patient data and generate summary reports. The flow occasionally fails because the model returns a tool_use block that references arguments not present in the schema, and your application code does not handle this case gracefully.
How would you address this?

A. Log invalid tool_use blocks when they occur and allow the tool dispatch to proceed, relying on the tool's own error handling to surface failures back to the application.
B. Validate the tool_use block's arguments against the tool schema before dispatching the tool and handle invalid arguments as a recognized error path.
C. Stop using tool calling entirely and replace tools with prompted text generation that asks the model to describe what it would do.
D. Retry the same request repeatedly until the model returns a valid tool_use block that matches the schema as expected.

Answer: B

Explanation:
The question presents a trust-boundary failure: model-generated tool arguments are being accepted without sufficient validation before an external operation executes. A tool call should therefore be treated like input arriving at any typed application interface-the application must ensure that the tool name and arguments satisfy the declared contract before dispatch.
Anthropic describes tool use as a contract in which the application defines the tool's schema and Claude returns structured tool_use input. Current Claude functionality also supports strict: true, which guarantees schema-compliant tool inputs through constrained generation and specifically addresses invented parameters, incompatible types, and missing required fields. For applications not using strict tool use, explicit application- side validation and a controlled error path remain essential.
B knowingly executes invalid input. C relies on uncontrolled repetition rather than deterministic validation and bounded recovery. D eliminates a useful capability instead of correcting its execution boundary.
Therefore, A provides the correct defensive design. Relevant Study Guide topics: tool calling, tool_use, input_schema, strict tool use, schema validation, defensive execution, error handling, and secure tool dispatch.

NEW QUESTION # 19
A teammate has asked you to explain when a Skill would be the right choice over an MCP server. The teammate is unsure how the two differ in practice when both can be reused across teams.
How would you explain the distinction?

A. A Skill bundles prompts, scripts, and data into a package the model loads as a unit while an MCP server exposes resources, tools, and prompts through a standard client interface.
B. A Skill is the older mechanism and an MCP server is the newer one, so the team should prefer an MCP server for any reusable capability that the team builds going forward.
C. A Skill is preferable for cross-team reuse because it loads more efficiently than an MCP server during normal operation in the team's typical multi-team workloads.
D. A Skill and an MCP server are equivalent extension mechanisms that the team can use interchangeably for any reusable capability that needs to be accessible across teams.

Answer: A

Explanation:
The supplied Claude Developer question explicitly identifies D . The two mechanisms solve different extension problems. A Skill is a reusable package of domain expertise and workflow guidance. Anthropic describes Agent Skills as modular, filesystem-based capabilities containing instructions, metadata, and optional supporting resources such as scripts and templates, which Claude can load when relevant.
MCP, by contrast, defines a standardized mechanism for exposing external capabilities and context.
Anthropic's MCP integration supports MCP tools and, through client-side helpers, MCP prompts and resources. This makes MCP appropriate when Claude must communicate with an external server or reusable service interface rather than simply load packaged expertise.
Thus, Skills are appropriate for packaging repeatable instructions, procedures, scripts, templates, or domain knowledge. MCP servers are appropriate for exposing callable operations, external data resources, and shared service capabilities using the Model Context Protocol.
A incorrectly treats them as interchangeable. B incorrectly describes the difference as merely generational. C invents a universal efficiency advantage that is not the architectural distinction.
Relevant Claude Developer topics: Agent Construction, Agent Skills, MCP, reusable capabilities, resources, prompts, tools, extension architecture, and cross-team component reuse .

NEW QUESTION # 20
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