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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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
Topic 2: 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
Topic 3: 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
Topic 4: 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 5: Applications and Integration	33.1%	- Claude API and Client SDKs 1. Construct and process API requests and responses 2. Integrate applications with the Claude API and supported client SDKs 3. Implement streaming and handle API errors - 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
Topic 6: Model Selection and Optimization	16.8%	- Model Selection 1. Select appropriate Claude models for task requirements 2. Evaluate quality, latency, capability, and cost tradeoffs - Performance and Cost Optimization 1. Use batching and other approaches to improve efficiency 2. Apply prompt caching and other cost optimization techniques
Topic 7: Eval, Testing, and Debugging	2.6%	- Testing and Debugging 1. Test Claude integrations and agentic systems 2. Diagnose and debug application, agent, and integration issues - Evaluation 1. Design and run evaluations for Claude-powered applications 2. Interpret evaluation results and improve application quality
Topic 8: Security and Safety	8.1%	- Secure Application Design 1. Protect sensitive data and manage access appropriately 2. Apply secure-by-design practices to Claude-powered applications - Safety and Guardrails 1. Implement safety controls and guardrails 2. Use hooks and other mechanisms to enforce application controls

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q18-Q23):

NEW QUESTION # 18
A new agent your team built handles customer support tickets, but it routinely gets confused when a single ticket spans billing, shipping, and product issues. The agent often loses track of which sub-issue it has already addressed and revisits the same one. The team is considering architectural changes.
What architectural change would you recommend?

A. Introduce an orchestrator agent that delegates billing, shipping, and product sub-issues to dedicated subagents.
B. Switch to a deterministic workflow that handles billing, shipping, and product issues in a fixed sequence.
C. Add detailed prompting that instructs the agent to track which sub-issues have been resolved and which remain.
D. Increase the size of the agent's context window so it can hold the full ticket history at once.

Answer: A

Explanation:
Option C applies an orchestrator-worker architecture to a request containing several distinct domains. Rather than making one agent continuously switch between billing, shipping, and product reasoning, an orchestrator can decompose the ticket, delegate each concern to an appropriately scoped specialist, track completion, and consolidate the resulting recommendations.
Anthropic describes this architecture directly: an orchestrator dynamically breaks down a task, delegates subtasks to worker agents, and synthesizes their results. Anthropic's multi-agent Research system similarly uses a lead agent that coordinates specialized subagents operating with independent contexts.
A rigid workflow is inappropriate because not every ticket contains the same combination or ordering of issues. B can improve behavior but leaves one agent responsible for managing all competing concerns and state. D increases raw context capacity without addressing decomposition or responsibility boundaries.
C is therefore the strongest architectural change when separate issue categories can be handled independently and then reconciled by a coordinating component. Relevant Study Guide topics: orchestrator-workers, subagents, delegation, task decomposition, context isolation, coordination, and synthesis.

NEW QUESTION # 19
You are choosing a Claude model for a high-volume classification task. Each classification is straightforward, latency requirements are tight, and per-request cost matters at scale.
Which model would you choose?

A. The largest, highest-capability Claude model, to maximize quality on every classification the application produces during normal operation across all requests.
B. Multiple Claude models in series, where each request runs through more than one model and the application combines the outputs into a final classification.
C. A smaller, faster Claude model, because the task is straightforward and the workload prioritizes latency and per-request cost at scale.
D. A mid-tier Claude model selected by default, because mid-tier models balance quality and cost in a way the team can apply across most tasks.

Answer: C

Explanation:
Option C matches Anthropic's efficiency-first model-selection guidance. For a straightforward, high-volume workload where latency and unit cost are explicit constraints, the correct starting point is a faster, economical model that can meet the task's quality threshold. Anthropic specifically lists high-volume straightforward tasks, tight latency requirements, and cost-sensitive implementations as cases where an efficiency-first model choice is appropriate.
The crucial qualification is that "smaller" does not mean accepting inadequate quality. The team should evaluate the candidate against representative classification examples and defined accuracy criteria. If it passes, moving to a larger model adds cost and often latency without delivering required business value.
A selects a mid-tier model by convention rather than workload evidence. B multiplies inference calls, generally increasing both latency and cost for a simple classification problem. D optimizes maximum capability even though the task does not require frontier-level reasoning.
Therefore, C is the appropriate initial model choice, followed by workload-specific validation. Relevant Study Guide topics: Claude model selection, efficiency-first design, classification workloads, throughput, latency, per-request economics, evaluation, and quality/cost tradeoffs.

NEW QUESTION # 20
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. 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.
B. Direct integration with Claude through its SDK, because no multi-provider need exists and abstraction would add complexity that does not pay off.
C. 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.
D. Both integration paths in parallel, where the application uses each path on different runs to compare which performs better in production over time.

Answer: B

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 # 21
You are designing a Claude application that will process customer support tickets in two stages: a triage stage that classifies tickets and a response stage that drafts replies. The team is debating whether to use a single Claude call that handles both stages or separate Claude calls for each stage.
How would you structure the application?

A. Use a single Claude call for triage and then use a non-Claude rule-based system for response generation, on the grounds that rule-based systems are more reliable for drafting replies.
B. Use multiple Claude calls in parallel that each draft a complete ticket reply, then have a fourth Claude call select the best one to send to the customer.
C. Use a single Claude call for both stages, on the grounds that a single call is cheaper than multiple calls in any production Claude application setup.
D. Use separate Claude calls for triage and response, because each stage has distinct inputs, outputs, and success criteria that benefit from focused prompts.

Answer: D

Explanation:
Option C follows a prompt-chaining/workflow decomposition pattern. Ticket classification and reply generation are materially different tasks. Triage may require a constrained category, priority, or routing decision, while response generation requires richer context, tone, policy compliance, and customer-facing language. Splitting the stages allows each Claude call to use a prompt, context set, model configuration, output format, and evaluation criterion appropriate to that specific responsibility.
Anthropic's guidance on effective agentic systems distinguishes predictable workflows from open-ended agents and recommends using the simplest architecture that meets requirements. Predefined workflows are particularly appropriate where work can be divided into clear processing stages.
This decomposition also creates useful engineering boundaries: the triage output can be validated before becoming input to the drafting stage; triage accuracy and response quality can be evaluated independently; and either stage can later be optimized without rewriting the entire process.
A unnecessarily restricts the second stage to rules. B introduces expensive parallel generation and a selection step without a stated requirement. D optimizes solely for call count while sacrificing task separation and observability.
The supplied exam source marks C. Relevant topics: Agent Patterns, prompt chaining, workflow decomposition, focused prompts, stage-level evaluation, and modular Claude application design.

NEW QUESTION # 22
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. 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.
C. The model's output, treating the response from each request as cacheable content the application can return on similar future queries.
D. The static portions of prompts that are repeated across requests, such as system prompts, instructions, or shared context.

Answer: D

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 # 23
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