Prepare with Anthropic : CCDV-F exam braindumps as your best preparation materials

Your Claude application's token costs have grown faster than expected. The team has not been tracking token usage by feature, so the team cannot identify which features are driving cost. The team is debating how to respond.
How would you respond?

• A. Switch every feature to the smallest model to cut cost broadly across the application during normal operation.
• B. Reduce token usage uniformly across all features by half, applying the cut evenly across the application during normal operation.
• C. Add token usage tracking by feature to the application's logging so the team can identify which features drive cost before recommending changes.
• D. Tell the team that cost growth is unavoidable as the application scales and that no investigation will change the trajectory of the application's cost over the next several quarters.

The Anthropic API deprecated a request parameter that your Claude application uses in approximately 40 places across the codebase. The deprecation notice gives a six-month window before the parameter is removed and recommends a replacement parameter with slightly different semantics.
You would respond to the deprecation by...

• A. Swapping all 40 call sites in a single change right away to prevent drawn-out migration work that will delay ongoing functioning.
• B. Adding regression tests to cover the parameter's behavior, then migrating call sites in batches that you validate against regression tests.
• C. Keeping the deprecated parameter in place while writing a wrapper function around it to insulate the rest of the codebase from the eventual change.
• D. Migrating all 40 call sites in a single change near the removal date to ensure the deprecated parameter continues to work as long as possible.

You are deciding between deploying a Claude-powered agent on Anthropic's hosted infrastructure or self- hosting under a "bring your own cloud" model in your own AWS account. The agent processes customer data subject to your enterprise's data residency policies, but the team wants to ship quickly and avoid managing infrastructure.
Which deployment model would you recommend?

• A. Deploying on Anthropic-hosted infrastructure to meet the team's shipping timeline, and flagging the data residency requirement for a follow-up compliance review after launch.
• B. Self-hosting under BYOC for an initial pilot, then evaluating whether to migrate to Anthropic-hosted infrastructure once the agent's data-handling patterns are better understood.
• C. Self-hosting under BYOC to satisfy the data residency requirement, while working with the infrastructure team to reduce the operational overhead of managing the deployment.
• D. Deploying on Anthropic-hosted infrastructure while the team drafts a request to update the enterprise data residency policy to accommodate hosted AI deployments.

Your team is debating how to manage the prompts used in your Claude application. Some prompts are checked into the code repository, some live in a separate configuration file, and some are constructed inline at runtime. The result is inconsistent, and a recent prompt change went out without code review.
What steps would you take?

• A. Move all prompts to inline runtime construction so the team can update them quickly through a streamlined process outside the standard code review workflow.
• B. Establish a single source of truth for prompts, version-control them alongside code, and require code review for prompt changes.
• C. Establish a single source of truth for prompts but keep change review optional, allowing developers to update prompts directly when changes are urgent.
• D. Move all prompts out of version control to a separate spreadsheet that team members can edit freely as the application evolves over time.

You are designing a multi-step Claude workflow where some steps must reason without seeing the full prior conversation history. The team wants to keep specific context isolated to specific steps.
The context engineering technique you would use is...

• A. Augmenting the context with all available content at every step so each step has access to the entire prior history of the workflow during its reasoning.
• B. Using a single global prompt that applies to every step in the workflow no matter what each step is reasoning about during its run.
• C. Embedding the full prior history in each step regardless of whether the step needs the prior history for its reasoning.
• D. Context isolation through subagents or multi-step agentic workflows that scope each step's context to only what the step needs.