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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
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
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
Tools and MCPs	10.6%	- Model Context Protocol 1. Apply MCP concepts and patterns for connecting models to external capabilities 2. Build and integrate MCP servers - Tool Development and Integration 1. Handle tool schemas, invocation, and tool-use results 2. Design and implement custom tools for Claude applications and agents
Applications and Integration	33.1%	- Claude API and Client SDKs 1. Integrate applications with the Claude API and supported client SDKs 2. Construct and process API requests and responses 3. Implement streaming and handle API errors - Application Development and Integration 1. Integrate Claude capabilities into existing software systems and workflows 2. Handle multimodal and structured application inputs and outputs 3. Build and ship production-grade Claude-powered applications
Eval, Testing, and Debugging	2.6%	- Testing and Debugging 1. Diagnose and debug application, agent, and integration issues 2. Test Claude integrations and agentic systems - Evaluation 1. Design and run evaluations for Claude-powered applications 2. Interpret evaluation results and improve application quality
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
Claude Code	3.1%	- Claude Code Configuration and Usage 1. Configure settings and development environments 2. Use Skills, plugins, and Claude Code capabilities effectively 3. Use CLAUDE.md and project configuration
Security and Safety	8.1%	- Safety and Guardrails 1. Implement safety controls and guardrails 2. Use hooks and other mechanisms to enforce application controls - Secure Application Design 1. Apply secure-by-design practices to Claude-powered applications 2. Protect sensitive data and manage access appropriately

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q34-Q39):

NEW QUESTION # 34
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. Explain that temperature controls sampling randomness and is unrelated to context capacity, then address the context issue through summarization or chunking.
B. 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.
C. 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.
D. 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.

Answer: A

Explanation:
Option A correctly separates sampling configuration from context management. Temperature historically controlled the randomness of token selection; it did not increase the number of tokens Claude could accept within a request. Anthropic's current Messages API documentation continues to describe temperature in terms of randomness and, for newer model generations, marks manual temperature control as deprecated. Therefore, changing temperature cannot solve a context-capacity problem.
Long transcripts instead require context-engineering techniques. Appropriate approaches include chunking documents, summarizing earlier material, retrieving only relevant sections, or using context editing
/compaction so high-value information remains visible while unnecessary material is removed. Anthropic's context-editing guidance explicitly supports summarization and replacement of growing conversation history to keep long-running workloads within usable context limits.
B incorrectly conflates generation parameters with context capacity. C may save some tokens but removes persistent application instructions and is therefore architecturally unsound. D modifies an unrelated parameter without addressing the root cause. Relevant Study Guide topics: context windows, token budgets, sampling parameters, summarization, chunking, and context engineering.

NEW QUESTION # 35
Your team's Claude agent has accumulated several customizations that bypass the SDK's defaults, including custom history management, retry logic, and error handling. A new team member has proposed reverting all the customizations to maintain the codebase more easily. The tech lead disagrees and says each customization was added for a reason.
How would you advise the team?

A. Revert all customizations to the SDK's defaults to standardize the codebase.
B. Migrate the agent off the SDK and rebuild it with a custom loop.
C. Decide on each customization individually based on its original reason and the SDK's current capabilities.
D. Keep all customizations, trusting that the tech lead's original reasoning is still valid.

Answer: C

Explanation:
The correct approach is to evaluate each customization independently against both its original requirement and the capabilities currently supplied by the Claude SDK. Custom code has an ongoing maintenance cost, but automatically removing it is unsafe because some modifications may encode genuine application requirements that the standard SDK behavior still does not address. Conversely, automatically retaining every customization can preserve obsolete complexity after the SDK has evolved.
This is particularly relevant because Anthropic's SDKs already provide capabilities that developers might otherwise implement themselves. Official Claude API documentation states that SDKs provide built-in request formatting, error handling, retries, timeouts, and other integration functionality. For example, transient connection, rate-limit, and server errors are retried automatically with exponential backoff.
Therefore, each custom history, retry, or error-management layer should be reviewed for its actual purpose. If an SDK primitive now provides equivalent behavior, removing duplicate code improves maintainability. If the customization still satisfies a distinct requirement, it should remain.
The supplied exam source explicitly identifies D as the intended answer. Relevant topics: Agent Construction, SDK capabilities, abstraction boundaries, technical-debt management, retries, error handling, and agent state management.

NEW QUESTION # 36
Your Claude application requests structured JSON output from the model. Most of the time the JSON is well- formed, but occasionally Claude returns malformed JSON that breaks downstream processing.
How would you handle the malformed output?

A. Retry the same request repeatedly until valid JSON appears in the model's response, with the retry loop adding delay to the application's response time on affected requests.
B. Add output validation that parses Claude's response against the expected schema and treats malformed output as a recognized error path with retry or fallback handling.
C. Switch to free-form text output so the application no longer depends on JSON parsing for any of the responses it sends to downstream systems during normal operation.
D. Manually inspect every response before downstream processing so a human reviewer catches any malformed JSON before the application passes the response to downstream systems.

Answer: B

Explanation:
Option B establishes a controlled boundary between probabilistic model output and deterministic downstream code. When structured data is machine-consumed, malformed JSON must be recognized as an explicit application error rather than allowed to propagate into parsers, databases, or other services.
Anthropic's Structured Outputs documentation identifies malformed JSON, missing fields, inconsistent types, and schema violations as exactly the kinds of failures that can break downstream systems when unconstrained output is used. Current Claude capabilities can constrain responses using JSON Schema, and SDK helpers can provide parsing and validation.
Even when an application uses an older or unconstrained generation path, it should parse against the expected schema, record validation failure, and enter a bounded recovery path such as retry, repair, fallback, or controlled rejection. A human review of every request is unscalable. C removes a useful structured interface instead of making it reliable. D performs uncontrolled blind retries and provides no schema-aware error handling or bounded fallback strategy.
Therefore, B gives the application explicit failure semantics and protects downstream systems. Relevant Study Guide topics: structured output, JSON validation, schema enforcement, retries, fallback handling, defensive parsing, and downstream reliability.

NEW QUESTION # 37
A teammate has asked you to explain why the team's Claude application is billed for output tokens at a different rate than input tokens. They had assumed the rate was the same for both.
How would you explain the difference?

A. Output tokens are not billed at all, because cost is determined entirely by the input tokens sent to the model on each request.
B. Output tokens are typically billed at a lower rate than input tokens, because output tokens are cheaper to produce than input tokens are to process.
C. Output tokens are typically billed at a higher rate than input tokens, and cost models for the application should reflect both rates separately.
D. Output tokens are typically billed at the same rate as input tokens, and the apparent rate difference is a billing error to report to Anthropic.

Answer: C

Explanation:
The supplied Claude Certified Developer Foundations question identifies D as the correct answer. Anthropic prices input and output tokens separately, so application cost models must independently account for the number of tokens supplied to Claude and the number generated in response.
Anthropic's official pricing documentation confirms that output-token pricing is normally higher than base input-token pricing. For example, current Claude models maintain distinct columns for base input tokens and output tokens; the exact dollar amounts depend on the selected model. This distinction matters because an application with relatively small prompts but very large generated responses can incur substantial output- token expenditure even when input volume is modest.
Therefore, cost forecasting should use approximately: input tokens × input rate + output tokens × output rate , with additional adjustments where prompt caching, batch processing, server tools, or other pricing modifiers apply. Option A incorrectly assumes equivalent rates, B reverses the normal relationship, and C incorrectly excludes generated tokens from billing.
Relevant Claude Developer topics: Claude API Mechanics, token accounting, model pricing, input/output token usage, cost modeling, prompt caching, and production API economics .

NEW QUESTION # 38
You are designing a Claude application that helps medical researchers analyze multi-step clinical case studies.
The application must work through differential diagnoses by considering symptom patterns, weighing evidence across competing hypotheses, and showing intermediate reasoning steps before producing a final recommendation. The team is choosing among Claude's available model options.
The model option best suited to this use case is...

A. Extended thinking, which lets the model reason through the differential diagnosis steps before producing the final recommendation.
B. Fast mode, which prioritizes the lowest possible latency at the expense of reasoning depth on complex tasks.
C. A smaller model with a tighter context window, which encourages the model to focus its limited capacity on the task.
D. Zero-shot prompting alone with no model option adjustments, which keeps the application's configuration as simple as possible.

Answer: A

Explanation:
Option B is the best answer because the task requires deliberate multi-step reasoning over competing hypotheses before a final recommendation. Anthropic's thinking documentation explains that enabling thinking gives Claude additional reasoning capacity before producing the final response, and larger thinking budgets can improve performance on complex analytical tasks. That maps directly to a differential-diagnosis workflow involving evidence comparison, hypothesis elimination, and multi-stage synthesis.
Option A uses ordinary zero-shot prompting and does not specifically allocate additional reasoning capacity.
Option C explicitly optimizes for minimum latency at the expense of reasoning depth, which conflicts with the complexity requirement. Option D reduces model and context capacity and therefore has no principled relationship to better differential reasoning.
One current-platform nuance is important: manual "extended thinking" is the legacy configuration on supported Claude 4.5/4.6 models, while newer models use adaptive thinking. That does not change the question's underlying answer: among the listed choices, B is the only option that intentionally provides a reasoning phase for complex analysis. Relevant Study Guide topics: thinking, reasoning budgets, complex- task model configuration, latency-quality tradeoffs, and model selection.

NEW QUESTION # 39
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