CCDV-F Practice Questions

A.  

Add a system prompt instruction telling the model to produce schema-conforming arguments, treating the prompt instruction as the primary mechanism for keeping the agent's tool calls valid.

B.  

Keep the schema strict, validate arguments before dispatching, and return a structured error so the agent can retry.

C.  

Remove the schema entirely and rely on the downstream service to reject invalid calls, treating the downstream service as the team's primary enforcement layer.

D.  

Loosen the schema as the teammate proposed so the downstream service receives every call the agent makes during normal operation.

A.  

Zero-shot prompting alone with no model option adjustments, which keeps the application's configuration as simple as possible.

B.  

Extended thinking, which lets the model reason through the differential diagnosis steps before producing the final recommendation.

C.  

Fast mode, which prioritizes the lowest possible latency at the expense of reasoning depth on complex tasks.

D.  

A smaller model with a tighter context window, which encourages the model to focus its limited capacity on the task.

A.  

Remove tool calling from the workflow entirely so the agent operates as a single text-generation step with no tool outputs accumulating in the context window.

B.  

Apply context engineering techniques such as tool output pruning or compaction to keep the active task state visible while reducing the volume of older content.

C.  

Increase the model's context window so the agent can hold every tool output at full detail across the entire workflow no matter how many tool calls it accumulates.

D.  

Restart the agent every five tool calls to prevent any drift, with the agent losing all task state at each restart point during the workflow.

A.  

As a custom tool embedded in this team's agent only, with other teams able to copy the implementation into their own agents when they need the capability.

B.  

As a shared library that each team imports into its own Claude application code, with each team responsible for keeping the library up to date in its integration.

C.  

As a Skill or MCP server because both are purpose-built for cross-team reuse independently by each consuming team.

D.  

As a wrapper around an existing built-in tool that adds the missing functionality, on the grounds that built-in tools cover the reuse pattern when extended carefully.

A.  

Define the application boundary explicitly, identify what content can leave the boundary for Claude, and add filtering or redaction at the boundary.

B.  

Add a prompt instruction in the system prompt specifying the categories of sensitive information Claude should disregard when processing user-submitted text.

C.  

Log all user-submitted text before it is sent to Claude and review the logs periodically to identify whether sensitive information is reaching the model.

D.  

Apply filtering at the boundary for the most commonly observed sensitive data patterns and expand coverage to additional patterns based on findings from production monitoring.

A.  

Replace Claude Code with a different tool that does not require any configuration to operate without confirmation prompts in the pipeline.

B.  

Run Claude Code in headless mode with the required permissions configured in settings.json and capture its output for downstream processing.

C.  

Disable Claude Code's confirmation prompts globally across all environments so the pipeline runs without interruption from any prompt.

D.  

Run Claude Code in interactive mode and have a developer manually approve every confirmation prompt while the pipeline executes its tasks.

A.  

Stop using all plugins until the team can rebuild equivalent functionality directly into the application code, treating plugin avoidance as a way to remove version-related risk.

B.  

Add explicit plugin version tracking to the project's configuration so the team can identify, pin, and upgrade plugin versions deliberately.

C.  

Treat plugins as untrackable third-party code and rely on plugin authors to communicate breaking changes when they happen, with no internal version tracking.

D.  

Upgrade every plugin to the latest version on a regular cadence to keep version drift small, on the grounds that drift contributes to regression risk.

A.  

Upgrade immediately, because the 8 percent reasoning improvement outweighs the 3 percent malformed output rate across the application's typical request distribution.

B.  

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.

C.  

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.

D.  

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.