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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Agents and Workflows	14.7%	- Agent Architecture and Tradeoffs 1. Select appropriate agent architectures and patterns 2. Evaluate tradeoffs between agentic approaches and traditional workflows - 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
Security and Safety	8.1%	- Secure Application Design 1. Protect sensitive data and manage access appropriately 2. Apply secure-by-design practices to Claude-powered applications - Safety and Guardrails 1. Use hooks and other mechanisms to enforce application controls 2. Implement safety controls and guardrails
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
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
Claude Code	3.1%	- Claude Code Configuration and Usage 1. Use CLAUDE.md and project configuration 2. Configure settings and development environments 3. Use Skills, plugins, and Claude Code capabilities effectively
Applications and Integration	33.1%	- Claude API and Client SDKs 1. Integrate applications with the Claude API and supported client SDKs 2. Implement streaming and handle API errors 3. Construct and process API requests and responses - 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. 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
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. Apply prompting techniques to improve reliability and output quality 2. Design prompts appropriate to application requirements

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q90-Q95):

NEW QUESTION # 90
Your Claude application's content policy specifies categories of content it should not produce under any circumstance. The application currently has no mechanism to enforce this policy, and content matching these categories is appearing in the application's output.
How would you enforce the content policy?

A. Remove the content policy entirely and let any output reach users during normal operation, accepting whatever content the application produces in response to incoming traffic.
B. Enhance the system prompt to contain explicit instructions for the categories to avoid, complete with examples of each category. Treat the strengthened prompt as the primary enforcement mechanism for the application's content policy across all responses.
C. Move enforcement to users by asking them to report content policy violations after the violating content has already reached them in the application's responses.
D. Add deterministic output filtering that checks responses against the content policy before they reach users.

Answer: D

Explanation:
Option D is the strongest enforcement design because an unconditional content policy requires an application- level control between model generation and user delivery. Prompt instructions are valuable for steering Claude, but they are probabilistic controls and should not be treated as the sole enforcement mechanism when prohibited categories must never be exposed.
Anthropic's guardrail guidance recommends layered safeguards including screening, validation, monitoring, and filtering rather than relying exclusively on prompts. Its prompt-leak guidance specifically recommends output screening and post-processing, including deterministic techniques such as keyword matching, regular expressions, or other text-processing mechanisms where appropriate.
A improves the probability of policy compliance but cannot guarantee that every generated response will satisfy an externally defined application policy. B explicitly abandons the requirement. C detects violations only after exposure, which is unsuitable when the content must not reach users.
A production architecture can combine system instructions, structured classification, policy engines, deterministic rules, and model-based moderation, but the decisive requirement is enforcement before output delivery. Relevant Claude Developer topics are guardrails, output filtering, content moderation, deterministic enforcement, defense in depth, safe application boundaries, and production Claude application design.
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NEW QUESTION # 91
You are building an MCP server that exposes several internal data sources as MCP resources. The server needs to be deployed so multiple Claude applications can integrate with it.
How would you approach the build and deployment?

A. Deploy the MCP server only on individual developer machines, with the Claude applications unable to reach the server outside each developer's machine.
B. Author the server with clearly defined resources, tools, and prompts, choose a communication pattern, and deploy to an accessible hosting environment.
C. Build the MCP server with resource and tool definitions scoped to the first Claude application that needs it, and extend the definitions to additional applications as each integration is requested.
D. Bypass the MCP server and embed each data source directly in every Claude application that needs the data, with each application maintaining its own integration.

Answer: B

Explanation:
Option A correctly treats the MCP server as a reusable integration boundary rather than application-specific code. Model Context Protocol separates capability providers from consuming Claude applications by exposing standardized tools, resources, and prompts through an MCP-compatible interface. A shared server therefore needs explicit capability definitions, an appropriate transport or communication pattern, and a deployment location reachable by its intended clients.
Anthropic's MCP documentation distinguishes remote HTTP-based integrations from local/client-managed connections. For remotely shared services, the server must be reachable from the consuming environment; client-side MCP helpers additionally support broader MCP capabilities such as resources and prompts.
B unnecessarily couples the server design to the first consumer and encourages application-specific evolution of what should be a reusable service boundary. C prevents multi-application deployment because only local developer sessions could reach the service. D abandons MCP entirely and recreates duplicated integrations in every consuming application.
Thus, A provides the correct lifecycle: define the MCP contract, expose resources/tools/prompts appropriately, select the transport according to topology, deploy the service, and let multiple applications consume the standardized interface independently. Relevant Study Guide topics: MCP architecture, reusable services, tools, resources, prompts, transports, and deployment topology.

NEW QUESTION # 92
You maintain a Claude application that uses Claude Sonnet 4.5 across several production workflows.
Anthropic released Claude Sonnet 4.7, which your evaluation suite shows performing 8% better on your highest-volume task. However, this version produces different output formatting on two of your structured- extraction prompts that downstream consumers parse with regex-based code.
To roll out the upgrade, you would...

A. Keep the older model in production to preserve the fragile parsing layer that may risk breaking with any model change.
B. Rewrite the downstream parsing code to accept a wider range of output formats to ensure the application is resilient to future model upgrades without requiring prompt changes.
C. Switch the model identifier in the application's configuration to the new model, then monitor production for parsing failures to be fixed as failures surface.
D. Adjust the prompts to constrain output format, re-run evaluations against the expected parsing-layer schema, then roll the model out with a feature flag and the option to revert per workflow.

Answer: D

Explanation:
Option A is correct because a model upgrade should be treated as a controlled application change, not a simple identifier substitution. Anthropic's evaluation guidance recommends defining measurable success criteria and running task-specific evaluations that mirror real production behavior, including edge cases. Its model-migration guidance likewise recommends testing replacement models before moving production workloads.
Here, the new model improves the highest-volume task but changes output formatting on structured-extraction prompts. That means the migration has both a quality benefit and a compatibility risk. The correct response is to tighten the output contract, re-run evaluations against the parsing/schema boundary, then deploy progressively with a feature flag and a per-workflow rollback path. This limits blast radius and preserves a known-good recovery option.
Option B pushes an unverified behavior change directly into production. Option C makes downstream parsing permissive, which can conceal schema drift instead of enforcing a stable contract. Option D permanently preserves a fragile implementation and discards the measured quality improvement.
Taking the model version stated in the question as the scenario, A is the correct lifecycle strategy. Relevant Study Guide topics: model migration, regression evaluation, structured output, compatibility testing, progressive rollout, rollback, and production change management.

NEW QUESTION # 93
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. 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.
B. 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.
C. 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.
D. 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.

Answer: C

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 # 94
A new Claude model release includes performance improvements for several reasoning tasks but has changed the format of its responses to system prompts that use multi-section instructions. Your application uses multi- section system prompts heavily. Initial evaluation on the application's actual workload shows the new model performs 8 percent better on reasoning tasks but produces malformed output on roughly 3 percent of requests because of the format change. The team is debating whether to upgrade.
How would you decide?

A. Adapt the application's system prompt to the new model's format expectations and re-evaluate, then upgrade only if the adapted prompt eliminates the malformed output while preserving the reasoning improvements.
B. Upgrade and add a downstream validation step that catches the 3 percent malformed output before it reaches users, treating the validation step as the team's mitigation for the format change.
C. Stay on the previous model permanently to avoid the malformed output rate and any future format changes that subsequent model releases might introduce in the application.
D. Upgrade immediately, because the 8 percent reasoning improvement outweighs the 3 percent malformed output rate across the application's typical request distribution.

Answer: A

Explanation:
The supplied examination page marks B . The scenario already demonstrates why model upgrades must be treated as evaluated software changes rather than automatic replacements: the new model improves one metric while introducing a regression in another.
Anthropic's official model-selection guidance recommends creating benchmark tests specific to the application's use case, testing models with the application's actual prompts and data, comparing response quality and edge-case performance, and weighing performance against operational tradeoffs. Therefore, the correct action is to adapt the multi-section system prompt to the new model's behavior and repeat the evaluation. Only after the formatting regression is eliminated-or reduced below an explicitly acceptable threshold-should the upgrade proceed.
A incorrectly assumes that an 8% reasoning improvement numerically compensates for a 3% malformed- output rate; these metrics measure different consequences and cannot simply be subtracted. C treats the known incompatibility only downstream instead of first correcting the prompt/model interaction. D permanently rejects future improvement and is inconsistent with controlled lifecycle evolution.
The engineering principle is migration through regression testing and adaptation , not blind upgrading or permanent version avoidance.
Relevant Claude Developer topics: Systems Life Cycle, model migration, regression evaluation, prompt adaptation, compatibility testing, deployment gates, and continuous evolution .

NEW QUESTION # 95
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