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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Apply MCP concepts and patterns for connecting models to external capabilities 2. Build and integrate MCP servers
Topic 2: 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. Apply prompt caching and other cost optimization techniques 2. Use batching and other approaches to improve efficiency
Topic 3: 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
Topic 4: 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. Use subagents and coordinate multi-agent workflows 2. Apply appropriate agentic frameworks and orchestration patterns - Agent Architecture and Tradeoffs 1. Select appropriate agent architectures and patterns 2. Evaluate tradeoffs between agentic approaches and traditional workflows
Topic 5: 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
Topic 6: 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
Topic 7: Applications and Integration	33.1%	- Application Development and Integration 1. Handle multimodal and structured application inputs and outputs 2. Integrate Claude capabilities into existing software systems and workflows 3. Build and ship production-grade Claude-powered applications - 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
Topic 8: Prompt and Context Engineering	11%	- Context Engineering 1. Manage context windows and application context 2. Apply context management strategies for agents and long-running workflows - Prompt Engineering 1. Design prompts appropriate to application requirements 2. Apply prompting techniques to improve reliability and output quality

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q65-Q70):

NEW QUESTION # 65
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. Restart the task from the beginning whenever a process is interrupted.
C. Increase the agent's timeout to several hours so that interruptions become rare enough to ignore in practice.
D. Apply a checkpointing pattern that persists the agent's intermediate state and reloads that state when resuming an interrupted task.

Answer: D

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 # 66
Your Claude application uses structured output that is consumed by downstream code. The team wants to handle malformed or unexpected output gracefully so it does not crash downstream systems.
The best choice for handling this issue would be to...

A. Crash on any unexpected output and require manual recovery on affected requests the application handles during normal operation.
B. Silently drop any output that does not match expectations so downstream systems do not see the malformed output the application receives from the model.
C. Use the output the model produces and leave schema enforcement to a later phase of production after the application is more complete.
D. Apply defensive parsing to handle unexpected fields, missing values, and type mismatches that the downstream code might otherwise crash on.

Answer: D

Explanation:
Option D applies a fundamental production engineering principle: treat externally generated data as potentially malformed and parse it defensively before use. Downstream application logic should not assume that every field exists, every type is correct, or every unexpected property can safely be ignored. Instead, the parser should validate expected structures, handle optional or missing values deliberately, reject incompatible types, and convert failures into controlled application errors rather than process crashes.
Anthropic's Structured Outputs documentation identifies exactly these failure classes for unconstrained model output: malformed JSON, missing required fields, inconsistent types, and schema violations can break downstream applications. Current Structured Outputs and strict tool-use capabilities can eliminate many schema-level failures through constrained decoding, but defensive handling remains an important software boundary when unexpected data can still arise from external services, legacy paths, or semantic validation requirements.
A creates unnecessary outages. B hides failures and discards potentially recoverable data without observability. C deliberately postpones a reliability requirement until after deployment.
The supplied question identifies D as correct. Relevant topics: Claude App Design, defensive programming, structured output, schema validation, parsing, error handling, downstream reliability, and type safety.

NEW QUESTION # 67
Your application uses the Messages API to handle multi-turn conversations. Each new turn resends the entire conversation history, and your token costs are growing as conversations get longer. You suspect there is a more efficient approach.
How would you address this?

A. Summarize each conversation after every turn and replace the full history with the summary on the next request, reducing token count at the cost of fidelity.
B. Truncate every conversation to the last two messages so that token usage stays low and costs remain predictable across the application's normal operation.
C. Use prompt caching to reuse the static portions of the conversation context across turns instead of paying for them at the normal input-token rate on every request the application sends.
D. Switch to the Batch API for every turn so the per-call cost is reduced, treating the batch as the team's primary cost-control mechanism for multi-turn work.

Answer: C

Explanation:
Option A is the technically verified answer. Anthropic documents that multi-turn agentic requests resend the growing conversation context on subsequent turns. Prompt caching allows repeated prompt prefixes-such as system instructions, tool definitions, and prior unchanged conversation content-to be reused at the lower cache-read rate rather than repeatedly charged as ordinary uncached input. Anthropic specifically identifies caching repeated context as a major production cost optimization.
B is incorrect even though the supplied PDF contains a duplicated screenshot in which B appears selected.
The Batch API is designed for asynchronous workloads that can tolerate delayed completion; it is not the appropriate mechanism for every interactive turn in a multi-turn conversation. Because the user requested verified answers rather than blindly reproducing marked selections, A is retained.
C discards potentially essential conversation state arbitrarily. D is a valid context-management technique in some long-running workflows, but summarizing after every turn creates additional model work and loses detail; it does not exploit repeated-prefix caching.
Therefore, A directly addresses the stated cost pattern while preserving conversation fidelity. Relevant Study Guide topics: Messages API, stateless conversation history, prompt caching, cached input tokens, multi-turn applications, token economics, and cost optimization.

NEW QUESTION # 68
You are setting up a CI/CD pipeline for a new Claude application. The pipeline needs to run automated checks on every pull request before code can be merged.
The CI/CD checks would include...

A. Automated linting and security scanning, with Claude integration testing handled manually during pre- release verification by a designated reviewer.
B. A full end-to-end production deployment on every pull request to catch all possible issues before any code is merged into the main branch.
C. Automated tests of the Claude integration, linting, and any other standard quality gates the team applies to its other services.
D. Automated tests of the Claude integration only, with linting handled separately during local development on each developer's machine.

Answer: C

Explanation:
Option A treats a Claude application as production software subject to the same disciplined engineering controls as other services while adding tests for its model integration. CI should provide repeatable feedback before merge, including deterministic unit/integration tests, linting, type or static checks where applicable, security checks, and organization-specific quality gates.
Claude-specific behavior should also be tested systematically rather than reserved for manual release verification. Anthropic's evaluation guidance recommends task-specific test cases reflecting realistic inputs and edge cases, measurable success criteria, and automation whenever practical. Claude Code documentation also explicitly identifies CI automation, including GitHub Actions and GitLab CI/CD, as supported development workflows.
B is excessive because every pull request should not directly deploy to production merely to obtain end-to-end coverage; testing environments and staged deployment exist for that purpose. C makes linting dependent on individual developer discipline rather than a shared merge gate. D delays Claude integration verification until pre-release and makes a repeatable automated check manual.
Therefore, A provides comprehensive and consistent pull-request validation. Relevant Study Guide topics: CI
/CD, automated testing, integration testing, linting, evaluations, regression prevention, and quality gates.

NEW QUESTION # 69
A teammate has asked why your Claude application sometimes produces a response that includes the prompt text repeated back, and other times produces a response with the prompt text rephrased. They suspect a bug in the application's request construction.
How would you respond?

A. Tell the teammate that the variation depends on which Claude model serves the request, and recommend pinning the application to a single model version to make the output behavior consistent.
B. Explain that LLMs generate output token by token, and variation in how prompt content appears in output is a property of generation, not a bug in request construction.
C. Tell the teammate that the variation is caused by the application sending two different prompts on different runs and propose a code change that pins the system prompt to a single version.
D. Confirm that the variation is a bug in the application's request construction and start investigating which part of the application is producing the inconsistent prompt text.

Answer: B

Explanation:
Option B reflects the fundamental generative behavior of large language models. Claude does not retrieve a fixed response template for a given prompt. Instead, it generates the response incrementally, selecting subsequent tokens according to the probability distribution produced by the model. Consequently, semantically equivalent runs can differ in wording, ordering, whether prompt language is quoted, and whether it is paraphrased.
Anthropic's API documentation explicitly describes sampling parameters as controlling randomness in subsequent-token selection and also states that even configurations intended to minimize randomness do not make output completely deterministic. Current Claude model generations increasingly manage sampling internally, but the underlying principle remains: generative output is not guaranteed to be textually identical across equivalent calls.
Therefore, seeing the original prompt echoed in one response and paraphrased in another does not, by itself, demonstrate malformed request construction. A real request-construction problem should instead be established through traces showing different system instructions, messages, parameters, or context.
A, C, and D incorrectly infer specific implementation faults without supporting evidence.
The supplied question identifies B as the correct response. Relevant topics: SW Eng Foundations, token generation, sampling, nondeterminism, debugging, and model behavior.

NEW QUESTION # 70
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