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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Applications and Integration	33.1%	- Claude API and Client SDKs - Multimodal and Structured Outputs - Message Batches and Prompt Caching - API Integration and Application Development - Software Engineering Fundamentals - Streaming, Error Handling and Reliability
Claude Code	3.1%	- Claude Code Configuration and Extensibility
Agents and Workflows	14.7%	- Agent Construction with Claude - Agent Architecture - Agent Patterns and Frameworks
Eval, Testing, and Debugging	2.6%	- Evaluation, Testing, and Debugging
Tools and MCPs	10.6%	- Tool Use and Tool Schemas - Building Custom Tools and MCP Servers - Model Context Protocol
Model Selection and Optimization	16.8%	- Model Capabilities and Trade-offs - Cost and Latency Optimization - Performance Optimization - Model Selection
Prompt and Context Engineering	11%	- Context Engineering - Context Management and Long-Context Techniques - Prompt Engineering
Security and Safety	8.1%	- Prompt Injection and Untrusted Content - Application Security - Secure Tool Use and Guardrails - Safety and Responsible Development

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q96-Q101):

NEW QUESTION # 96
You are setting up a Claude application that requires API keys for several external services.
What is the best way to store the keys?

A. Use a single shared key across all external services, so any developer working on the application can find the keys easily during development.
B. Email the keys to each developer as needed and have each developer paste the keys into their local environment when they begin working on the application's code.
C. Store the keys in a secrets manager or environment-specific configuration that is not checked into source code, and load them at runtime.
D. Put the keys in the application's configuration file and check the configuration file into the team's repository alongside the rest of the source code.

Answer: C

Explanation:
Option B follows standard secrets-management practice and Anthropic's explicit guidance for API credentials.
API keys are authentication secrets and should not be embedded in source code or committed to repositories.
Anthropic's authentication documentation explicitly recommends storing API keys in a secrets manager, rotating them periodically, and revoking credentials suspected of compromise. Anthropic SDKs can load Claude credentials from environment variables such as ANTHROPIC_API_KEY, allowing the secret to be injected at runtime instead of compiled into the application.
The same principle applies to external service credentials used by a Claude application. Development, staging, and production should normally receive independently scoped credentials through the deployment environment or secret-management infrastructure.
A creates a high-probability credential leak because repository history can retain secrets even after a later deletion. C violates isolation and least privilege by sharing credentials across services and users. D uses email as an uncontrolled secret-distribution channel and creates inconsistent manual configuration.
Therefore, B supplies credentials only at runtime while keeping them outside source control and enabling rotation and environment-specific access. Relevant Study Guide topics: API-key management, secrets managers, environment configuration, credential rotation, least privilege, and secure configuration.

NEW QUESTION # 97
Your Claude application makes high-volume API calls during business hours and very few calls overnight.
The team is concerned about staying within rate limits during peak hours and wants to understand how the Claude API enforces those limits.
How would you proceed?

A. Review the API documentation for streaming endpoints and evaluate whether migrating peak-hour calls to streaming reduces exposure to rate limit enforcement.
B. Examine the peak-hour request patterns in your application logs and smooth traffic by distributing requests more evenly across the business-hours window.
C. Assess the average payload size of current API calls and consolidate requests where possible to reduce the total number of calls made during peak hours.
D. Identify the rate limits, design the application to stay within them during peak hours, and use exponential backoff when limits are reached.

Answer: D

Explanation:
The supplied question marks C , and Anthropic's API documentation directly supports it. Claude API rate limits are enforced using dimensions such as requests per minute (RPM), input tokens per minute (ITPM), and output tokens per minute (OTPM) . Anthropic also notes that short traffic bursts can exceed effective limits even when a longer-term average appears acceptable.
Applications should therefore determine the organization's actual configured limits, model peak traffic against those boundaries, throttle or queue work as necessary, and handle 429 responses correctly. Rate-limit responses include a retry-after value indicating when another request should be attempted. Anthropic's official SDKs automatically retry transient connection failures, rate-limit errors, and server errors with exponential backoff by default.
Streaming does not exempt requests from rate limits, making A technically incorrect. Consolidating payloads in B may reduce request count but could increase token consumption and does not by itself address all rate- limit dimensions. D may smooth traffic, but it is only one optimization and does not substitute for rate-limit- aware application logic.
Relevant Claude Developer topics: Claude API Mechanics, rate limits, RPM, ITPM, OTPM, HTTP 429, retry-after, exponential backoff, throttling, and capacity planning .

NEW QUESTION # 98
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 in a Skills directory recognized by Claude Code, where Claude can discover and load it during sessions for the team's repositories.
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 inside the application's source code as a regular library module and call it from the application code instead of from Claude Code.
D. 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.

Answer: A

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 # 99
Your Claude application is hitting context window limits when processing long customer service transcripts.
A junior developer suggests increasing the temperature parameter to fix the issue.
How would you respond?

A. Increase the temperature parameter as the junior developer suggested and observe whether the context window issue resolves over the next several runs of the application in production.
B. Explain that temperature controls sampling randomness and is unrelated to context capacity, then address the context issue through summarization or chunking.
C. Remove the system prompt entirely to make room for longer transcripts in each request, freeing up context window space the system prompt would otherwise consume.
D. Adjust the temperature parameter together with the max_tokens parameter, treating the combined adjustment as the team's mechanism for managing context window pressure during long-transcript processing.

Answer: B

NEW QUESTION # 100
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. Define the tool with a loose schema and let the model interpret the inputs flexibly on each call the agent makes.
B. Define the tool with a clear schema, write a precise description for when to call it, and handle pricing service errors explicitly.
C. Implement the tool with no error handling and let the agent loop catch failures whenever the pricing service returns an error during operation.
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: 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 # 101
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