Anthropic CCDV-F : Claude Certified Developer-Foundations

You are running Claude Code as part of an automated continuous integration pipeline. The pipeline needs Claude Code to execute a set of well-defined tasks without prompting for confirmation, and the output needs to be captured for downstream processing.
How would you configure the pipeline?

• A. Run Claude Code in headless mode with the required permissions configured in settings.json and capture its output for downstream processing.
• B. Disable Claude Code's confirmation prompts globally across all environments so the pipeline runs without interruption from any prompt.
• C. Replace Claude Code with a different tool that does not require any configuration to operate without confirmation prompts in the pipeline.
• D. Run Claude Code in interactive mode and have a developer manually approve every confirmation prompt while the pipeline executes its tasks.

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 explicit session expiration rules, state reset triggers, and rules for starting fresh sessions so stale context does not leak into new conversations.
• 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 a single short session timeout that applies across all conversations and treat the timeout as the application's complete session lifecycle mechanism.

You are building a Claude application that processes 10,000 customer emails overnight to extract structured data. The work is non-interactive, runs once daily, and has a flexible completion window of several hours.
Which Claude API would you use?

• A. The streaming responses API to process each email and return partial results to a database as the model generates them.
• B. The Batch API, which is designed for non-interactive workloads with flexible completion windows.
• C. The real-time Messages API, processing the emails one at a time sequentially to ensure consistent ordering of results.
• D. The real-time Messages API with concurrent requests to process the emails as fast as possible during the overnight window.

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

Your Claude application has been running for several conversation turns, and you notice the model occasionally references information that was discussed many turns ago but is no longer relevant. You suspect context drift is causing the model to weight stale content too heavily.
How would you address the drift?

• A. Truncate the conversation so the model sees only the most recent turn during each subsequent response.
• B. Apply compaction to summarize older portions of the conversation so the gist remains while the specifics carry less weight.
• C. Reset the conversation after every turn so the model loses all prior turns when generating a response.
• D. Increase the context window size so all turns of the conversation remain visible to the model in full detail.