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Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

Your Claude application makes high-volume API calls during business hours and very few calls overnight.
The team is concerned about staying within rate limits during peak hours and wants to understand how the Claude API enforces those limits.
How would you proceed?

A. Review the API documentation for streaming endpoints and evaluate whether migrating peak-hour calls to streaming reduces exposure to rate limit enforcement.
B. Identify the rate limits, design the application to stay within them during peak hours, and use exponential backoff when limits are reached.
C. Assess the average payload size of current API calls and consolidate requests where possible to reduce the total number of calls made during peak hours.
D. Examine the peak-hour request patterns in your application logs and smooth traffic by distributing requests more evenly across the business-hours window.

Question 2

You are integrating Claude into an application written in Python. The Claude SDK provides a Python client that wraps the underlying REST API.
How would you integrate the SDK?

A. Use a different LLM provider's SDK and translate the responses into Claude's API shape so the application can switch providers in the future.
B. Call the REST API directly with raw HTTP requests so the application avoids the SDK's abstraction between the application code and the API.
C. Use the Claude Python SDK and let it handle authentication, retries, and response parsing through its standard documented patterns for Python integrations.
D. Skip the SDK and embed Claude calls in shell commands invoked from Python, so that the application runs the calls outside the main Python process.

Question 3

Your Claude application returns confident-sounding answers, but occasionally those answers contain factual errors that downstream systems treat as ground truth. The team is concerned about the application's confidence-versus-accuracy gap.
How would you address the gap?

A. Reject every response the application produces until a manual accuracy review is conducted on each response by a human reviewer before any downstream system uses it.
B. Apply skepticism toward confident output by adding validation steps, sourcing requirements, or confidence calibration before treating outputs as ground truth.
C. Lower the model's temperature so the model's responses sound less confident and downstream systems are less likely to treat the responses as ground truth in normal operation.
D. Add a disclaimer to every output telling users to verify the accuracy of the output and treat the disclaimer as the primary mechanism for managing the confidence-versus-accuracy gap.

Question 4

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. Using a single global prompt that applies to every step in the workflow no matter what each step is reasoning about during its run.
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. 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.

Question 5

Your Claude application's API keys are stored in a secrets manager. The team is debating whether the same key should be used in development, staging, and production environments.
How would you handle the keys across environments?

A. Rotate the same key across environments at random intervals on the grounds that random rotation provides isolation between environments without requiring distinct keys.
B. Use a single development key everywhere on the grounds that production keys are too risky to deploy across the application's three environments during normal operation.
C. Use distinct keys for each environment so a compromise in one environment does not affect the others during normal operation across the application's lifecycle.
D. Use the same key across all environments for simplicity and treat the propagation of any compromise as a known operational tradeoff for the team's key management approach.

Solutions: