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Last Updated: Aug 31, 2026
No. of Questions: 97 Questions & Answers with Testing Engine
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Evaluation, Testing, and Debugging | 2.6% | - Output evaluation and validation - Error handling and debugging |
| Topic 2: Prompt and Context Engineering | 11% | - Structured output handling - Context window management - Prompt design and structuring |
| Topic 3: Applications and Integration | 33.1% | - Claude Messages API - Streaming and Batch API - Vision capabilities - SDK and third-party integration |
| Topic 4: Model Selection and Optimization | 16.8% | - Latency and performance trade-offs - Claude model family characteristics - Cost and token optimization |
| Topic 5: Claude Code | 3.1% | - Claude Code configuration and usage |
| Topic 6: Tools and Model Context Protocol (MCP) | 10.6% | - Tool integration and usage - MCP server development |
| Topic 7: Security and Safety | 8.1% | - Guardrails and safety controls - AI application security |
| Topic 8: Agents and Workflows | 14.7% | - Workflow vs autonomous agents - Claude Agent SDK usage - Memory and context management - Agent architecture principles |
Question 1
A teammate has asked why your Claude application sometimes produces a response that includes the prompt text repeated back, and other times produces a response with the prompt text rephrased. They suspect a bug in the application's request construction.
How would you respond?
A. Explain that LLMs generate output token by token, and variation in how prompt content appears in output is a property of generation, not a bug in request construction.
B. Tell the teammate that the variation depends on which Claude model serves the request, and recommend pinning the application to a single model version to make the output behavior consistent.
C. Confirm that the variation is a bug in the application's request construction and start investigating which part of the application is producing the inconsistent prompt text.
D. Tell the teammate that the variation is caused by the application sending two different prompts on different runs and propose a code change that pins the system prompt to a single version.
Question 2
You are explaining to a stakeholder why running the same Claude prompt twice can produce slightly different results. The stakeholder is concerned this means the application is broken.
How would you address the stakeholder's concern?
A. Tell the stakeholder the variation comes from network latency and that switching to a faster network connection will produce more consistent results across runs.
B. Explain that LLMs are non-deterministic by default due to sampling, and describe how the application handles this through validation, retries, or temperature adjustment.
C. Tell the stakeholder the variation is a bug that the team will fix in the next release of the application, then create a work ticket to fix the bug.
D. Tell the stakeholder the variation is caused by Claude being updated continuously by Anthropic, and that switching to a fixed model snapshot will eliminate the variation entirely.
Question 3
You are setting up a CI/CD pipeline for a new Claude application. The pipeline needs to run automated checks on every pull request before code can be merged.
The CI/CD checks would include...
A. Automated linting and security scanning, with Claude integration testing handled manually during pre- release verification by a designated reviewer.
B. A full end-to-end production deployment on every pull request to catch all possible issues before any code is merged into the main branch.
C. Automated tests of the Claude integration only, with linting handled separately during local development on each developer's machine.
D. Automated tests of the Claude integration, linting, and any other standard quality gates the team applies to its other services.
Question 4
You are designing a Claude application that helps medical researchers analyze multi-step clinical case studies.
The application must work through differential diagnoses by considering symptom patterns, weighing evidence across competing hypotheses, and showing intermediate reasoning steps before producing a final recommendation. The team is choosing among Claude's available model options.
The model option best suited to this use case is...
A. Fast mode, which prioritizes the lowest possible latency at the expense of reasoning depth on complex tasks.
B. Zero-shot prompting alone with no model option adjustments, which keeps the application's configuration as simple as possible.
C. Extended thinking, which lets the model reason through the differential diagnosis steps before producing the final recommendation.
D. A smaller model with a tighter context window, which encourages the model to focus its limited capacity on the task.
Question 5
You have just shipped a new Claude-powered application to production. The development phase is complete, and the system is now in active use by internal teams.
The next phase of work for this system is...
A. Retiring the system, since shipping is the final lifecycle stage for any application that reaches production.
B. Running a formal post-deployment review that assesses the development phase before the team does any further work on the system.
C. Handing the system over to a separate operations team that will manage it independently of the development team going forward.
D. Operating and maintaining the system, including monitoring, responding to issues, and planning evolution.
Solutions:
| Question 1 Answer: A | Question 2 Answer: B | Question 3 Answer: D | Question 4 Answer: C | Question 5 Answer: D |
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