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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Eval, Testing, and Debugging	2.6%	- Testing and Debugging 1. Diagnose and debug application, agent, and integration issues 2. Test Claude integrations and agentic systems - Evaluation 1. Design and run evaluations for Claude-powered applications 2. Interpret evaluation results and improve application quality
Topic 2: 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. Implement safety controls and guardrails 2. Use hooks and other mechanisms to enforce application controls
Topic 3: Tools and MCPs	10.6%	- Tool Development and Integration 1. Handle tool schemas, invocation, and tool-use results 2. Design and implement custom tools for Claude applications and agents - Model Context Protocol 1. Build and integrate MCP servers 2. Apply MCP concepts and patterns for connecting models to external capabilities
Topic 4: Model Selection and Optimization	16.8%	- Performance and Cost Optimization 1. Use batching and other approaches to improve efficiency 2. Apply prompt caching and other cost optimization techniques - Model Selection 1. Evaluate quality, latency, capability, and cost tradeoffs 2. Select appropriate Claude models for task requirements
Topic 5: Agents and Workflows	14.7%	- Agent Architecture and Tradeoffs 1. Select appropriate agent architectures and patterns 2. Evaluate tradeoffs between agentic approaches and traditional workflows - Subagents and Agentic Frameworks 1. Apply appropriate agentic frameworks and orchestration patterns 2. Use subagents and coordinate multi-agent workflows - Claude Agent SDK and Agent Loops 1. Implement and manage custom agent loops 2. Build and configure agents using the Claude Agent SDK
Topic 6: Claude Code	3.1%	- Claude Code Configuration and Usage 1. Configure settings and development environments 2. Use Skills, plugins, and Claude Code capabilities effectively 3. Use CLAUDE.md and project configuration
Topic 7: Applications and Integration	33.1%	- Application Development and Integration 1. Handle multimodal and structured application inputs and outputs 2. Build and ship production-grade Claude-powered applications 3. Integrate Claude capabilities into existing software systems and workflows - Claude API and Client SDKs 1. Implement streaming and handle API errors 2. Construct and process API requests and responses 3. Integrate applications with the Claude API and supported client SDKs
Topic 8: Prompt and Context Engineering	11%	- Prompt Engineering 1. Design prompts appropriate to application requirements 2. Apply prompting techniques to improve reliability and output quality - Context Engineering 1. Manage context windows and application context 2. Apply context management strategies for agents and long-running workflows

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q12-Q17):

NEW QUESTION # 12
The team is debating whether to integrate with the Claude API directly or through a third-party abstraction layer that supports multiple LLM providers. The team has identified that all current and projected use cases run on Claude, no internal customer has requested LLM portability, and the team's product roadmap does not mention multi-provider support over the next two years. The third-party abstraction would add roughly 15 percent overhead in code complexity and introduce one additional dependency.
Which integration approach would you recommend?

A. The third-party abstraction layer, on the grounds that multi-provider support is valuable for any application as a matter of long-term flexibility across vendors.
B. Both integration paths in parallel, where the application uses each path on different runs to compare which performs better in production over time.
C. Direct integration with Claude through its SDK, because no multi-provider need exists and abstraction would add complexity that does not pay off.
D. A custom multi-provider abstraction layer the team builds in-house so that the team controls every part of the abstraction the application uses for its API calls.

Answer: C

Explanation:
D follows the principle of choosing the simplest architecture that satisfies demonstrated requirements .
The supplied examination item explicitly marks D. In this scenario, portability is neither a present functional requirement nor a foreseeable roadmap requirement. Adding an abstraction therefore creates measurable complexity without delivering an identified product capability.
Anthropic provides official general-purpose SDKs for Claude in multiple languages. These expose the Messages API directly while adding idiomatic interfaces, type safety, streaming support, retries, and error handling. Anthropic separately describes compatibility layers and framework-specific libraries as alternative integration surfaces rather than requirements for ordinary Claude development.
A third-party abstraction becomes rational when there is a genuine requirement for multiple providers, vendor switching, standardized cross-model interfaces, or an existing architectural platform that mandates it. None exists here. Building that flexibility speculatively increases dependencies, debugging surface, version- compatibility work, and the possibility that provider-specific Claude capabilities are hidden behind a lowest- common-denominator API.
A optimizes for hypothetical future requirements. B doubles operational complexity. C creates an even larger maintenance burden.
Relevant Claude Developer topics: Claude App Design, SDK selection, abstraction boundaries, dependency management, YAGNI, integration architecture, and provider portability requirements .

NEW QUESTION # 13
Your team is preparing a new Claude application for production, and the product team has asked for a cost projection. The team needs to estimate the cost based on expected request volume, average input length, and average output length. How would you build the projection?

A. Build a cost model that combines expected request volume and average input tokens, treating output tokens as a small enough share of cost to leave out of the projection.
B. Build a cost model that uses the average per-request cost from a similar Claude application the team built last year, scaling that figure by expected request volume.
C. Build a cost model based on expected request volume and the chosen model's pricing, treating average input and output token counts as variables to be estimated post-launch.
D. Build a cost model that combines expected request volume, average input tokens, average output tokens, the chosen model's pricing, and any caching benefits.

Answer: D

Explanation:
Option B is correct because Claude API cost is fundamentally driven by usage volume multiplied by the token economics of the selected model and features. A credible projection must therefore estimate requests, average input tokens per request, average output tokens per request, applicable input/output prices, and expected prompt-cache behavior. Anthropic's pricing documentation treats input, output, cache writes, and cache reads as distinct billable categories, with cache hits priced below standard input processing.
A practical forecast can be modeled as: request volume × expected per-request input cost plus request volume
× expected per-request output cost, adjusted for cache-write/read rates and any other applicable pricing modifiers. The model should also include ranges for variance rather than only one point estimate, because token lengths and cache-hit rates will fluctuate in production.
Option A assumes a historical application has the same token profile and pricing, which may be false. Option C omits output tokens even though output pricing can be a material share of cost. Option D postpones the very token estimates the product team needs before launch.
Therefore, B is the complete projection method. Relevant Study Guide topics: token accounting, model pricing, prompt caching, cost forecasting, workload sizing, and production economics.

NEW QUESTION # 14
You are configuring Claude Code for a new project. The team needs to set permissions, default model selections, and environment-specific behavior at the project level so the configuration is consistent across all developers working on the repository.
The Claude Code mechanism you would use is...

A. A shared spreadsheet that lists configuration values for team members to reference and update by hand as the project evolves.
B. Environment variables that each developer sets on their own machine when working with Claude Code on the project.
C. The settings.json file, scoped at the project level so the configuration applies consistently across developers and persists with the repository.
D. A system prompt embedded in every Claude Code conversation by each developer at the start of every session in the project.

Answer: C

Explanation:
D is directly aligned with Claude Code's configuration model. The supplied exam source marks D . Anthropic documents settings.json as the official mechanism for configuring Claude Code through hierarchical scopes.
Shared project configuration is stored in .claude/settings.json , which is intended to be checked into source control and shared with the team.
Project settings provide consistent repository-level behavior while still participating in Claude Code's configuration precedence. They can configure permissions, environment variables, tool behavior, and other supported project policies. Claude Code additionally distinguishes shared project settings from .claude
/settings.local.json, which is intentionally excluded from source control and is suitable for developer-specific experimentation or machine-local overrides.
A spreadsheet provides documentation but no executable configuration enforcement. B allows every developer's configuration to diverge and is appropriate only for values that genuinely belong to the local environment. C requires manual repetition and cannot reliably establish repository-wide policy.
Therefore, shared project settings.json is the appropriate configuration-as-code mechanism.
Relevant Claude Developer topics: Confia Management, Claude Code configuration, settings scopes, project settings, permissions, environment configuration, team consistency, and source-controlled configuration .

NEW QUESTION # 15
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. 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.
B. Define the tool with a clear schema, write a precise description for when to call it, and handle pricing service errors explicitly.
C. Define the tool with a loose schema and let the model interpret the inputs flexibly on each call the agent makes.
D. Implement the tool with no error handling and let the agent loop catch failures whenever the pricing service returns an error during operation.

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 # 16
Your team is debating how to manage the prompts used in your Claude application. Some prompts are checked into the code repository, some live in a separate configuration file, and some are constructed inline at runtime. The result is inconsistent, and a recent prompt change went out without code review.
What steps would you take?

A. Move all prompts to inline runtime construction so the team can update them quickly through a streamlined process outside the standard code review workflow.
B. Move all prompts out of version control to a separate spreadsheet that team members can edit freely as the application evolves over time.
C. Establish a single source of truth for prompts, version-control them alongside code, and require code review for prompt changes.
D. Establish a single source of truth for prompts but keep change review optional, allowing developers to update prompts directly when changes are urgent.

Answer: C

Explanation:
Option D treats prompts as production configuration and executable application behavior rather than informal text. A prompt modification can change output format, tool-selection behavior, policy enforcement, downstream compatibility, token usage, or model quality just as materially as an ordinary source-code change. Consequently, prompt changes need traceability, review, testing, and rollback.
A single source of truth prevents different application paths from silently using inconsistent prompt versions.
Version control provides an immutable history showing who changed the prompt, what changed, and which application release used it. Pull-request review enables peer assessment and automated evaluation before deployment. The same configuration-as-code principle appears throughout Claude Code's project configuration model, where shared project configuration is designed to live with version-controlled project artifacts.
A removes governance and creates an uncontrolled external state. B increases hidden runtime behavior and bypasses normal review. C solves fragmentation but leaves the exact governance failure described in the scenario-the ability to ship prompt changes without review.
Therefore, D provides reproducibility and controlled change management. Relevant Study Guide topics:
prompt versioning, configuration management, source control, change review, reproducibility, regression testing, and rollback.

NEW QUESTION # 17
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The second version is the web-based format of the Claude Certified Developer-Foundations (CCDV-F) practice test. Browsers such as Internet Explorer, Microsoft Edge, Firefox, Safari, and Chrome support the web-based practice exam. You don't have to install excessive plugins or software to attempt this Claude Certified Developer-Foundations (CCDV-F) practice test.

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