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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
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
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
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%	- Agent Architecture and Tradeoffs 1. Evaluate tradeoffs between agentic approaches and traditional workflows 2. Select appropriate agent architectures and patterns - Subagents and Agentic Frameworks 1. Use subagents and coordinate multi-agent workflows 2. Apply appropriate agentic frameworks and orchestration patterns - Claude Agent SDK and Agent Loops 1. Implement and manage custom agent loops 2. Build and configure agents using the Claude Agent SDK
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
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. Construct and process API requests and responses 2. Implement streaming and handle API errors 3. Integrate applications with the Claude API and supported client SDKs
Claude Code	3.1%	- Claude Code Configuration and Usage 1. Use Skills, plugins, and Claude Code capabilities effectively 2. Configure settings and development environments 3. Use CLAUDE.md and project configuration
Tools and MCPs	10.6%	- Model Context Protocol 1. Build and integrate MCP servers 2. Apply MCP concepts and patterns for connecting models to external capabilities - Tool Development and Integration 1. Design and implement custom tools for Claude applications and agents 2. Handle tool schemas, invocation, and tool-use results

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q85-Q90):

NEW QUESTION # 85
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. Increase the size of the agent's context window so it can hold the full ticket history at once.
B. Add detailed prompting that instructs the agent to track which sub-issues have been resolved and which remain.
C. Switch to a deterministic workflow that handles billing, shipping, and product issues in a fixed sequence.
D. Introduce an orchestrator agent that delegates billing, shipping, and product sub-issues to dedicated subagents.

Answer: D

NEW QUESTION # 86
Your Claude application's error handling currently logs every API error with the same severity level. The team wants to differentiate between errors that should page an on-call engineer and errors that should be logged for later review. How would you structure the error handling?

A. Categorize errors by severity based on impact and recoverability, then route each category to the appropriate channel for paging or logging.
B. Log every error with the same severity, on the grounds that differentiating severity adds complexity that does not pay off in most application setups over time.
C. Disable logging for any error that does not page, treating non-paging errors as not worth recording for later review either.
D. Page on every error, on the grounds that paging guarantees that no error is missed by the team during normal operation across the application's lifecycle.

Answer: A

Explanation:
Option C is correct because operational error handling should distinguish failures by impact, urgency, and recoverability. Anthropic's API documentation already separates error conditions by type and documents different recovery behaviors. For example, transient connection failures, rate limits, and many 5xx errors are automatically retried by official SDKs with exponential backoff, while other failures require application correction or investigation. Treating all of those conditions as the same operational severity creates either alert fatigue or missed incidents.
A production design should classify errors using criteria such as user impact, data integrity risk, security exposure, persistence after retry, affected request volume, and whether automatic recovery succeeds. High- severity failures that threaten service availability or correctness can page the on-call engineer. Recoverable or isolated failures can be logged with structured metadata, request IDs, and metrics for later review.
Option A pages on everything and quickly makes alerts noisy. Option B destroys observability for non-paging errors. Option D preserves the original defect by assigning the same severity to every condition. Therefore, C follows sound Claude application operations. Relevant Study Guide topics: API errors, retries, observability, incident response, severity classification, structured logging, and production operations.

NEW QUESTION # 87
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. Restart the task from the beginning whenever a process is interrupted.
B. Apply a checkpointing pattern that persists the agent's intermediate state and reloads that state when resuming an interrupted task.
C. Run two copies of the agent in parallel for every task and use whichever one finishes first as the source of truth.
D. Increase the agent's timeout to several hours so that interruptions become rare enough to ignore in practice.

Answer: B

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 # 88
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 out of version control to a separate spreadsheet that team members can edit freely as the application evolves over time.
B. Move all prompts to inline runtime construction so the team can update them quickly through a streamlined process outside the standard code review workflow.
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 # 89
Your Claude application receives untrusted input from external sources. The team is establishing how the application should treat this untrusted input.
Untrusted input would be...

A. Routed through a separate Claude application with more lax security controls so the original application does not handle the untrusted input directly.
B. Blocked entirely so the application accepts only trusted input, with no path for legitimate external input the application might process.
C. Treated identically to trusted input from internal sources, on the grounds that all input the application receives can be handled the same way.
D. Validated and sanitized before being incorporated into prompts, and treated as data the model should not interpret as instructions.

Answer: D

Explanation:
Option A is the appropriate trust-boundary treatment for external content. Untrusted text can contain malformed data, adversarial instructions, prompt-injection attempts, or content deliberately constructed to alter the agent's behavior. It should therefore be validated or screened before inclusion and clearly represented as untrusted data rather than authoritative application instructions.
Anthropic's prompt-injection guidance distinguishes direct attacks from indirect prompt injection, where Claude processes third-party content such as webpages, emails, documents, or tool output containing hostile instructions. Anthropic recommends input validation and screening, least-privilege access, safe treatment of untrusted tool content, and screening content before Claude acts on it. Importantly, Anthropic notes that tool- result content is treated as untrusted data rather than as a reliable place for application instructions.
B erases the trust distinction and exposes the application to instruction/data confusion. C introduces a deliberately weaker security boundary and does not sanitize the data. D prevents legitimate use cases unnecessarily; untrusted does not mean unusable-it means the data must be handled defensively.
Therefore, A correctly combines validation with explicit trust separation. Relevant Study Guide topics:
prompt injection, untrusted content, input validation, sanitization, data/instruction separation, least privilege, and defense in depth.

NEW QUESTION # 90
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