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The team is debating whether to integrate with the Claude API directly or through a third-party abstraction layer that supports multiple LLM providers. The team has identified that all current and projected use cases run on Claude, no internal customer has requested LLM portability, and the team's product roadmap does not mention multi-provider support over the next two years. The third-party abstraction would add roughly 15 percent overhead in code complexity and introduce one additional dependency.
Which integration approach would you recommend?

• A. Direct integration with Claude through its SDK, because no multi-provider need exists and abstraction would add complexity that does not pay off.
• B. A custom multi-provider abstraction layer the team builds in-house so that the team controls every part of the abstraction the application uses for its API calls.
• C. The third-party abstraction layer, on the grounds that multi-provider support is valuable for any application as a matter of long-term flexibility across vendors.
• D. Both integration paths in parallel, where the application uses each path on different runs to compare which performs better in production over time.

You are configuring Claude Code for a new project. The team needs to set permissions, default model selections, and environment-specific behavior at the project level so the configuration is consistent across all developers working on the repository.
The Claude Code mechanism you would use is...

• A. A system prompt embedded in every Claude Code conversation by each developer at the start of every session in the project.
• B. The settings.json file, scoped at the project level so the configuration applies consistently across developers and persists with the repository.
• C. A shared spreadsheet that lists configuration values for team members to reference and update by hand as the project evolves.
• D. Environment variables that each developer sets on their own machine when working with Claude Code on the project.

Your Claude application runs long agentic workflows where the agent makes many tool calls, and the conversation history grows quickly. After about 20 tool calls, you notice the agent's responses become less focused and sometimes ignore earlier task constraints.
How would you address this?

• A. 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.
• B. 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.
• C. 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.
• D. 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.

You are designing a Claude application that maintains user sessions across multi-turn conversations. The product team has asked how the application will handle session lifecycle: when sessions should expire, how state is reset, and how the application avoids carrying stale context into new conversations.
How would you design session lifecycle?

• A. Define a single short session timeout that applies across all conversations and treat the timeout as the application's complete session lifecycle mechanism.
• B. Define state reset triggers tied to specific application events but apply them across all sessions globally, with no per-session expiration rules.
• C. Define explicit session expiration rules but rely on users to start new conversations when they want fresh context, with no automatic reset triggers in the application.
• D. Define explicit session expiration rules, state reset triggers, and rules for starting fresh sessions so stale context does not leak into new conversations.

A Claude application is occasionally refusing to answer questions that should be in scope, including questions the application has answered correctly in the past. You want to investigate.
What is the first step of your investigation?

• A. Assess whether the refusals cluster around specific input patterns, question types, or time periods by analyzing the distribution of refused requests in the logs.
• B. Test whether removing or adjusting those instructions resolves the behavior.
• C. Identify which questions have been refused and compare them against previously answered questions to determine what changed between the successful and refused interactions.
• D. Examine traces of the refused requests to identify what triggers the refusal, whether input patterns, system prompt content, or other context.