Pass CCDV-F Exam Guide

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.

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The supplied exam source marks D . Claude prompt caching is designed for repeated prompt prefixes, not semantic caching of generated answers. High-value cache candidates include stable system prompts, long instructions, tool definitions, shared background documents, repeated examples, and the previously accumulated prefix of a multi-turn conversation.

Anthropic explains that prompt caching reuses a matching prompt prefix and can substantially reduce processing time and input-token cost on subsequent requests. The cache operates across the request structure—tools, system content, and messages up to the relevant cache boundary. It is particularly useful for prompts with many examples, large repeated context, repetitive instructions, and long multi-turn conversations.

B describes response caching , which is a separate application-level technique and is not what Anthropic's prompt caching feature does. C targets the portion that usually changes most, making it a poor general cache boundary. A is incorrect because multi-turn workloads are a major prompt-caching use case.

Therefore, maximize reusable stable prefixes and place changing request-specific content after them where practical.

Relevant Claude Developer topics: Claude API Mechanics, prompt caching, cache prefixes, token-cost optimization, static context, system prompts, multi-turn conversations, and API efficiency .

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Option C applies an orchestrator-worker architecture to a request containing several distinct domains. Rather than making one agent continuously switch between billing, shipping, and product reasoning, an orchestrator can decompose the ticket, delegate each concern to an appropriately scoped specialist, track completion, and consolidate the resulting recommendations.

Anthropic describes this architecture directly: an orchestrator dynamically breaks down a task, delegates subtasks to worker agents, and synthesizes their results. Anthropic's multi-agent Research system similarly uses a lead agent that coordinates specialized subagents operating with independent contexts.

A rigid workflow is inappropriate because not every ticket contains the same combination or ordering of issues. B can improve behavior but leaves one agent responsible for managing all competing concerns and state. D increases raw context capacity without addressing decomposition or responsibility boundaries.

C is therefore the strongest architectural change when separate issue categories can be handled independently and then reconciled by a coordinating component. Relevant Study Guide topics: orchestrator-workers, subagents, delegation, task decomposition, context isolation, coordination, and synthesis.

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Option C satisfies both the enterprise routing requirement and sound application architecture. Claude is available through Amazon Bedrock, and Anthropic provides Bedrock-specific SDK integration rather than requiring applications to call api.anthropic.com directly. Current Anthropic documentation describes Claude in Amazon Bedrock as operating through AWS-managed infrastructure with AWS-native authentication, billing, and security boundaries. Newer Bedrock integrations use the Messages API shape, allowing substantial application logic to remain consistent across provider environments.

Anthropic SDKs also provide dedicated Bedrock clients—for example, Python includes AnthropicBedrockMantle for current Bedrock deployments. Keeping business logic separated from provider-specific authentication, endpoints, model identifiers, and transport configuration reduces migration and maintenance risk.

A violates architectural simplicity by duplicating every call unnecessarily. B knowingly violates the enterprise requirement until migration occurs. D directly ignores the contractual routing constraint and is therefore invalid regardless of technical feasibility.

The correct approach is to make Bedrock the configured inference provider while keeping higher-level application and agent behavior decoupled from provider-specific implementation details. Relevant Claude Developer topics are Claude API mechanics, cloud-provider integrations, Amazon Bedrock, SDK configuration, authentication boundaries, model invocation, and provider abstraction.

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