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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
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
Eval, Testing, and Debugging	2.6%	- Evaluation 1. Interpret evaluation results and improve application quality 2. Design and run evaluations for Claude-powered applications - Testing and Debugging 1. Diagnose and debug application, agent, and integration issues 2. Test Claude integrations and agentic systems
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
Claude Code	3.1%	- Claude Code Configuration and Usage 1. Use Skills, plugins, and Claude Code capabilities effectively 2. Use CLAUDE.md and project configuration 3. Configure settings and development environments
Prompt and Context Engineering	11%	- Context Engineering 1. Apply context management strategies for agents and long-running workflows 2. Manage context windows and application context - Prompt Engineering 1. Design prompts appropriate to application requirements 2. Apply prompting techniques to improve reliability and output quality
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. 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
Model Selection and Optimization	16.8%	- Model Selection 1. Evaluate quality, latency, capability, and cost tradeoffs 2. Select appropriate Claude models for task requirements - Performance and Cost Optimization 1. Apply prompt caching and other cost optimization techniques 2. Use batching and other approaches to improve efficiency
Agents and Workflows	14.7%	- Claude Agent SDK and Agent Loops 1. Implement and manage custom agent loops 2. Build and configure agents using the Claude Agent SDK - Subagents and Agentic Frameworks 1. Apply appropriate agentic frameworks and orchestration patterns 2. Use subagents and coordinate multi-agent workflows - Agent Architecture and Tradeoffs 1. Evaluate tradeoffs between agentic approaches and traditional workflows 2. Select appropriate agent architectures and patterns

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q64-Q69):

NEW QUESTION # 64
Your Claude application validates structured output but has been treating validation failures as terminal errors. Each validation failure causes the entire user request to fail. The team wants to handle validation failures more gracefully.
How would you handle the validation failures?

A. Pass validation failures directly to downstream systems and let each downstream system decide how to handle the malformed output.
B. Tell users that validation failures are unavoidable and instruct them to perform manual accuracy checks before relying on outputs.
C. Disable output validation until the underlying cause of validation failures has been identified and addressed in a future release.
D. Treat validation failures as a recognized error path that triggers retry, repair, or fallback logic before failing the user request.

Answer: D

Explanation:
C converts validation failure from an uncontrolled terminal condition into a first-class recoverable error path . The supplied exam item identifies C as correct. If structured output does not meet the application's contract, it should never be forwarded as though valid, but immediate user-visible failure is also unnecessary when bounded recovery is possible.
A robust flow can retry generation, ask Claude to repair the malformed structure using the validation error as feedback, switch to an approved fallback path, or ultimately return a controlled failure if the retry budget is exhausted. The application must cap these recovery attempts to avoid unbounded loops.
Anthropic's Structured Outputs documentation explains that unconstrained model generation can produce parsing errors, missing fields, inconsistent types, or schema violations that otherwise require error handling and retries. Current Structured Outputs can eliminate many schema-level failures through constrained decoding, although exceptional conditions such as refusal or output truncation still require explicit handling.
A violates the validation boundary. B removes a protective control. D transfers an engineering reliability responsibility to end users.
Relevant Claude Developer topics: Claude App Design, structured output, validation, retries, repair loops, fallback logic, bounded recovery, error paths, and resilient downstream integration .

NEW QUESTION # 65
A Claude application that worked well in testing is now occasionally returning outputs that mention information not present in the input. The development team initially assumed the model was hallucinating, so they asked you to troubleshoot.
What would you do first?

A. Add a system prompt instruction telling the model not to invent information, on the grounds that prompt-level instructions are the fastest fix for hallucination concerns.
B. Apply a retrieval-augmented generation pattern to ground the responses in source content before any further investigation of the production traces.
C. Examine production traces to identify whether the issue is hallucination by the model, context loss, prompt injection, or another failure mode before recommending a fix.
D. Replace the current model with a larger one to reduce the chance of hallucination, on the grounds that larger models tend to hallucinate less in typical applications.

Answer: C

Explanation:
Option A follows disciplined production debugging: diagnose the actual failure mode before changing architecture or prompts. An output containing unsupported information might indeed be hallucination, but similar symptoms can result from stale conversation state, incorrect retrieval, unexpected tool output, prompt injection, incorrect request construction, or mismatched model/configuration versions.
A production trace should capture the user input, system instructions, relevant conversation history, retrieved content, tool calls and results, model/version, request parameters, response, and identifiers necessary to compare successful and failing cases. This establishes whether the model invented a fact or whether that fact entered context through another path.
B changes the model before establishing causality. C may eventually be useful if the confirmed problem is insufficient grounding, but implementing RAG before diagnosis can hide rather than explain the defect. D similarly changes prompting before verifying that prompt behavior is responsible.
The engineering sequence should be observe, reproduce, classify the failure, form a hypothesis, apply a targeted correction, and validate the correction with evaluations. Relevant Study Guide topics: production troubleshooting, observability, tracing, hallucination analysis, prompt injection, context failures, regression diagnosis, and lifecycle monitoring.

NEW QUESTION # 66
Your agent is processing tasks that take 30 to 60 minutes to complete. Each task has well-defined intermediate checkpoints, and the team wants the agent to be able to resume from the most recent checkpoint if a process is interrupted.
How would you implement this resumability?

A. Run two copies of the agent in parallel for every task and use whichever one finishes first as the source of truth.
B. Increase the agent's timeout to several hours so that interruptions become rare enough to ignore in practice.
C. Apply a checkpointing pattern that persists the agent's intermediate state and reloads that state when resuming an interrupted task.
D. Restart the task from the beginning whenever a process is interrupted.

Answer: C

Explanation:
Option B is the correct fault-tolerance pattern for a long-running stateful workflow. A checkpoint captures enough durable execution state-completed steps, intermediate results, pending work, identifiers, and other necessary task state-to restart from a known consistent point rather than replaying the entire workflow after interruption.
This pattern is consistent with Claude's current stateful agent architecture. Anthropic's Managed Agents documentation describes persistent sessions that preserve conversation history across interactions. When a session becomes idle, its sandbox can be checkpointed so filesystem and execution artifacts are available when work resumes. The certification concept is broader than that specific hosted implementation: long- running agent systems should externalize recoverable state at meaningful boundaries.
A longer timeout does not protect against process crashes, infrastructure restarts, network failures, or deployment interruptions. C doubles resource consumption and creates consistency problems without providing deterministic recovery. D wastes completed work and may repeat external side effects.
Therefore, B provides controlled resumability and minimizes repeated processing. Relevant Study Guide topics: checkpointing, persistent state, resumable workflows, long-running agents, fault tolerance, idempotency, and recovery architecture.

NEW QUESTION # 67
The product team has described a new Claude feature in business terms: "agents should help our analysts produce client memos faster." You need to convert this into actionable technical requirements for the engineering team.
Your first step would be to...

A. Examine what model capabilities and tier options are available and determine which best supports the memo drafting workflow described by the product team.
B. Assess what similar agent-based features have been built internally or in the industry and use those precedents to scope the technical approach.
C. Interpret the functional and infrastructure requirements implied by the business goal.
D. Ask the analysts about the current memo production process to see where they think Claude could be introduced as a prompt-driven drafting step.

Answer: C

Explanation:
The supplied examination source identifies D as correct. The statement "produce client memos faster" is a business objective , not an implementable engineering requirement. The first engineering activity is therefore requirements decomposition: determine what functionality is required and what technical characteristics must support it.
Functional requirements might specify how analysts provide source information, what stages the agent performs, whether it researches, outlines, drafts, revises, or cites material, which systems it accesses, what output it returns, and where human approval occurs. Infrastructure and non-functional requirements then define latency, access control, confidentiality, context size, availability, auditability, model selection, integration interfaces, token cost, observability, and operational constraints.
Anthropic's developer lifecycle similarly separates defining measurable success criteria from choosing and implementing model capabilities. Its evaluation guidance emphasizes establishing specific, measurable success criteria before optimizing a model or prompt. Claude's agent configuration also separates implementation components such as the model, system prompt, tools, MCP servers, and Skills-choices that logically follow requirement definition.
A is useful discovery but prematurely assumes a prompt-driven drafting implementation. B is precedent- driven rather than requirement-driven. C selects technology before defining what the system must accomplish.
Relevant topics: Understanding Reqs, functional requirements, infrastructure requirements, non- functional requirements, acceptance criteria, and solution decomposition .

NEW QUESTION # 68
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. Implement the tool with no error handling and let the agent loop catch failures whenever the pricing service returns an error during operation.
B. Define the tool with a loose schema and let the model interpret the inputs flexibly on each call the agent makes.
C. Define the tool with a clear schema, write a precise description for when to call it, and handle pricing service errors explicitly.
D. 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.

Answer: C

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 # 69
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