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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
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. Select appropriate Claude models for task requirements 2. Evaluate quality, latency, capability, and cost tradeoffs
Applications and Integration	33.1%	- 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 - Application Development and Integration 1. Integrate Claude capabilities into existing software systems and workflows 2. Build and ship production-grade Claude-powered applications 3. Handle multimodal and structured application inputs and outputs
Prompt and Context Engineering	11%	- Prompt Engineering 1. Apply prompting techniques to improve reliability and output quality 2. Design prompts appropriate to application requirements - Context Engineering 1. Apply context management strategies for agents and long-running workflows 2. Manage context windows and application context
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. Build and integrate MCP servers 2. Apply MCP concepts and patterns for connecting models to external capabilities
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
Agents and Workflows	14.7%	- 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 - Agent Architecture and Tradeoffs 1. Evaluate tradeoffs between agentic approaches and traditional workflows 2. Select appropriate agent architectures and patterns
Claude Code	3.1%	- Claude Code Configuration and Usage 1. Use CLAUDE.md and project configuration 2. Configure settings and development environments 3. Use Skills, plugins, and Claude Code capabilities effectively
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. Test Claude integrations and agentic systems 2. Diagnose and debug application, agent, and integration issues

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q19-Q24):

NEW QUESTION # 19
You are setting up a Claude application that will run a mix of multi-turn conversations and one-off requests.
You want to use caching techniques to reduce token costs where they apply. A teammate suggests caching the model's output as well, so the application does not have to make duplicate Claude calls when similar queries arrive.
You would apply prompt caching to...

A. Nothing, because prompt caching does not affect cost in any application that mixes multi-turn conversations and one-off requests in a single deployment.
B. The static portions of prompts that are repeated across requests, such as system prompts, instructions, or shared context.
C. The model's output, treating the response from each request as cacheable content the application can return on similar future queries.
D. Only the user's input portion of each request because user input is the part of the prompt that varies the most across the application's normal operation.

Answer: B

Explanation:
The supplied exam source marks D . Claude prompt caching is designed for repeated prompt prefixes, not semantic caching of generated answers. High-value cache candidates include stable system prompts, long instructions, tool definitions, shared background documents, repeated examples, and the previously accumulated prefix of a multi-turn conversation.
Anthropic explains that prompt caching reuses a matching prompt prefix and can substantially reduce processing time and input-token cost on subsequent requests. The cache operates across the request structure- tools, system content, and messages up to the relevant cache boundary. It is particularly useful for prompts with many examples, large repeated context, repetitive instructions, and long multi-turn conversations.
B describes response caching , which is a separate application-level technique and is not what Anthropic's prompt caching feature does. C targets the portion that usually changes most, making it a poor general cache boundary. A is incorrect because multi-turn workloads are a major prompt-caching use case.
Therefore, maximize reusable stable prefixes and place changing request-specific content after them where practical.
Relevant Claude Developer topics: Claude API Mechanics, prompt caching, cache prefixes, token-cost optimization, static context, system prompts, multi-turn conversations, and API efficiency .

NEW QUESTION # 20
You are designing an agent that processes vendor invoices. The work involves a small number of well- understood steps, but occasionally an invoice arrives in an unexpected format that requires the system to decide between rerouting, requesting clarification, or flagging for human review.
The most appropriate architecture for this system is...

A. A workflow for the standard path with an agent invoked at the decision point for unexpected formats.
B. A manager agent that delegates each step of standard invoice processing to a dedicated subagent, with a separate subagent handling each unexpected format.
C. A fully autonomous agent that handles every invoice from start to finish across all formats.
D. A single large prompt that processes every incoming invoice, both standard and unexpected, in one model call.

Answer: A

Explanation:
Option D applies the correct hybrid pattern. Anthropic distinguishes workflows-where LLMs and tools execute along predefined code paths-from agents, where the model dynamically determines how to proceed.
Anthropic recommends choosing the simplest architecture that meets the requirement: workflows provide predictability and consistency for well-understood tasks, while agents are valuable where flexible model- driven decisions are necessary.
Standard invoice processing is explicitly described as a small number of known steps. That makes a deterministic workflow the appropriate default because the path can be tested, monitored, and reproduced.
The unexpected-format branch is different: the system must reason among rerouting, requesting clarification, or human escalation. That decision point is where agentic flexibility adds value.
A makes the entire process autonomous even though most steps do not require autonomy, increasing cost, latency, and unpredictability. B introduces multiple specialized agents despite no demonstrated need for that complexity. C collapses deterministic processing and exception handling into one monolithic model call, making validation and debugging harder.
Therefore, D combines workflow predictability with targeted agentic reasoning. Relevant Study Guide topics:
workflows versus agents, routing, exception handling, hybrid agentic architecture, escalation, and complexity minimization.

NEW QUESTION # 21
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 # 22
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?

A. Keep the schema strict, validate arguments before dispatching, and return a structured error so the agent can retry.
B. Remove the schema entirely and rely on the downstream service to reject invalid calls, treating the downstream service as the team's primary enforcement layer.
C. Add a system prompt instruction telling the model to produce schema-conforming arguments, treating the prompt instruction as the primary mechanism for keeping the agent's tool calls valid.
D. Loosen the schema as the teammate proposed so the downstream service receives every call the agent makes during normal operation.

Answer: A

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 # 23
A teammate has asked how to extend Claude Code with a custom Skill that the team can invoke during sessions. The Skill consists of a set of instructions and a few support scripts the team wants Claude to be able to call when the Skill is loaded.
Where is the right place to define the Skill?

A. Define the Skill as a long inline instruction at the top of every CLAUDE.md file in the team's repositories so Claude has access to it on every session.
B. Define the Skill in a personal scratch directory on each developer's machine and load it manually before each Claude Code session that needs it.
C. Define the Skill in a Skills directory recognized by Claude Code, where Claude can discover and load it during sessions for the team's repositories.
D. Define the Skill inside the application's source code as a regular library module and call it from the application code instead of from Claude Code.

Answer: C

Explanation:
Option C matches Claude Code's documented Skill architecture. Agent Skills are filesystem-based extension artifacts rather than ordinary application modules or repeated prompt fragments. A Skill is represented by a directory containing a required SKILL.md file and can include optional supporting scripts, templates, examples, and reference material.
Anthropic documents project Skills under .claude/skills/ < skill-name > /SKILL.md. Project-level Skills can be shared through Git and automatically discovered when Claude Code loads project settings. Supporting scripts can reside alongside the Skill and be referenced from SKILL.md.
A incorrectly embeds reusable procedural material into every CLAUDE.md file, creating duplication and loading instructions even when they are irrelevant. B creates a conventional source-code library but does not register a Claude Code Skill. D makes the capability dependent on undocumented, developer-specific setup and undermines team reuse.
Therefore, C uses the extension mechanism specifically designed for discoverable, reusable Claude capabilities. Relevant Study Guide topics: Agent Skills, .claude/skills, SKILL.md, supporting resources, filesystem discovery, project-level configuration, and reusable Claude Code capabilities.

NEW QUESTION # 24
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