Understood! Let’s make the practice set easier but still relevant for a strong mid-to-senior QA candidate (not as hardcore as the previous senior-level set). I’ll give you 30 questions per section (120 total), focusing on practical, conceptual, and scenario-based questions that are approachable but still challenging enough for NVIDIA’s role.

✅ Section 1: QA Methodology, Linux & Docker (30 Questions)

1. What is the difference between Verification and Validation?
2. Explain the Software Testing Life Cycle (STLC).
3. What are entry and exit criteria in testing?
4. How do you prioritize test cases when time is limited?
5. What is a Requirement Traceability Matrix (RTM) and why is it important?
6. Explain Smoke Testing vs Sanity Testing.
7. What is Regression Testing and when do you perform it?
8. How do you handle flaky tests in automation?
9. What is the difference between Black-box and White-box testing?
10. Explain the Bug Lifecycle with states.
11. How do you measure test coverage?
12. What is risk-based testing and when do you apply it?
13. How do you ensure test data consistency across environments?
14. Which Linux command shows CPU and memory usage in real time?
15. How do you find which process is using a specific port in Linux?
16. How do you check disk usage in Linux?
17. How do you view running processes in Linux?
18. How do you search for a keyword inside a file in Linux?
19. What is the difference between Docker image and Docker container?
20. How do you list all running Docker containers?
21. How do you check logs of a Docker container?
22. How do you persist data in Docker containers?
23. How do you remove all stopped containers in Docker?
24. How do you troubleshoot a failing Docker container?
25. What is the purpose of Docker volumes?
26. How do you build a Docker image from a Dockerfile?
27. How do you run a container in detached mode?
28. How do you check the size of Docker images on your system?
29. How do you copy files from a container to the host?
30. How do you restart a Docker container after changes?

✅ Section 2: Gen AI & AI Tools (30 Questions)

1. What is Generative AI and how is it different from traditional AI?
2. What is a Large Language Model (LLM)?
3. Explain prompt engineering in simple terms.
4. What is temperature in LLMs and how does it affect output?
5. What is tokenization in NLP?
6. How do you test an AI chatbot for correctness?
7. What is hallucination in LLMs?
8. How do you measure accuracy of an AI model?
9. What is BLEU score used for?
10. What is model drift and why is it important?
11. How do you ensure reproducibility in AI experiments?
12. What is the difference between fine-tuning and prompt-tuning?
13. How do you test bias in AI models?
14. What is RAG (Retrieval-Augmented Generation)?
15. How do you test latency of an AI model?
16. How do you validate structured outputs like JSON from an LLM?
17. What is A/B testing in AI systems?
18. How do you test multi-language support in an AI model?
19. What is context window in LLMs?
20. How do you handle adversarial prompts in testing?
21. What is model evaluation pipeline?
22. How do you integrate AI testing into CI/CD?
23. What is LoRA in model fine-tuning?
24. How do you test safety filters in AI systems?
25. What is perplexity in language models?
26. How do you test streaming responses from an AI API?
27. What is zero-shot vs few-shot learning?
28. How do you test fallback mechanisms for AI services?
29. What is model versioning and why is it important?
30. How do you monitor AI performance in production?

✅ Section 3: Automation Framework, Programming & Kubernetes (30 Questions)

1. What is an automation framework and why do we need it?
2. Explain Page Object Model (POM).
3. What is the difference between Pytest and unittest in Python?
4. How do you run tests in parallel using Pytest?
5. How do you generate HTML reports in Pytest?
6. How do you handle test data in automation?
7. How do you integrate automation with Jenkins?
8. How do you schedule tests in Jenkins?
9. How do you trigger tests on code commit?
10. How do you store test artifacts in CI/CD?
11. Write a Python function to reverse a string.
12. Write a Python function to check if a number is prime.
13. How do you read a JSON file in Python?
14. How do you handle exceptions in Python?
15. How do you use virtual environments in Python?
16. What is Docker Compose and why is it useful?
17. How do you deploy a containerized app using Kubernetes?
18. What is a Kubernetes Pod?
19. What is the difference between Deployment and StatefulSet in K8s?
20. How do you check logs of a pod in Kubernetes?
21. How do you restart a pod in Kubernetes?
22. How do you scale a deployment in Kubernetes?
23. What is a ConfigMap in Kubernetes?
24. What is a Secret in Kubernetes?
25. How do you expose a service in Kubernetes?
26. How do you check the status of all pods in a namespace?
27. How do you delete a pod in Kubernetes?
28. How do you run a Job in Kubernetes?
29. What is kubectl describe used for?
30. How do you troubleshoot a CrashLoopBackOff pod?

✅ Section 4: General / Problem Solving (30 Questions)

1. How do you handle a critical bug found just before release?
2. How do you prioritize tasks when everything seems urgent?
3. How do you estimate testing effort for a new feature?
4. How do you handle conflicts with developers over bug severity?
5. How do you debug intermittent test failures?
6. How do you communicate risk to stakeholders?
7. How do you handle last-minute requirement changes?
8. How do you ensure quality in Agile?
9. How do you measure QA effectiveness?
10. How do you handle production issues reported by customers?
11. How do you design a test plan for a new feature?
12. How do you handle time zone challenges in global teams?
13. How do you ensure test coverage for critical features?
14. How do you handle tight deadlines without compromising quality?
15. How do you mentor junior QA engineers?
16. How do you handle escalations from management?
17. How do you ensure security testing is included in QA?
18. How do you handle flaky tests in CI/CD?
19. How do you ensure documentation quality?
20. How do you handle dependencies between teams?
21. How do you ensure compliance testing for regulated products?
22. How do you handle customer-reported bugs efficiently?
23. How do you ensure continuous improvement in QA?
24. How do you handle test environment instability?
25. How do you ensure knowledge sharing in QA teams?
26. How do you handle performance testing for a new release?
27. How do you ensure security of test data?
28. How do you handle automation debt in a project?
29. How do you ensure QA metrics are meaningful?
30. How do you handle stress during critical releases?

✅ Total: 120 easier but still relevant questions.

👉 Do you want me to create a downloadable Markdown workbook with:

• All 120 questions
• Space for your answers
• Difficulty tags (Easy/Medium)
• Checklist for progress tracking?

Or should I keep it as text only?

Here’s a condensed list of senior-level practice questions for each session (without answers, so you can self-practice). These are drawn from the advanced guide I just created:

✅ Session 1: QA Methodology, Linux & Docker

1. Design a risk-based test strategy for a GPU driver release across multiple OSes.
2. How do you reduce flake rate from 2.8% to <0.5%? Governance + technical steps.
3. How do you capture and analyze a Linux kernel panic tied to a GPU driver?
4. Diagnose I/O latency spikes during model training on Linux. Which tools and steps?
5. Explain Docker isolation primitives (namespaces, cgroups, seccomp) and hardening.
6. How do you build secure, reproducible CUDA-enabled Docker images?
7. Networking inside Docker is flaky under load. Outline a debug plan.
8. How do you ensure environment reproducibility across customer setups?
9. Optimize a test matrix for OS × GPU × CUDA × App Type without full combinatorial explosion.
10. When do you choose bare-metal vs VM vs container for testing?
11. How do you scale soak/stress tests without starving shared labs?
12. Define a defect triage policy and approach to escape analysis.

✅ Session 2: Gen AI & AI Tools

1. Design an evaluation plan for a RAG-powered feature (metrics, datasets).
2. How do you benchmark LLM serving performance on A100 vs H100?
3. Strategies for hallucination mitigation and measurement.
4. Create a prompt-injection/jailbreak test plan for an LLM with tool calls.
5. Compare fine-tuning vs LoRA vs prompt-tuning and QA implications.
6. How do you avoid evaluation leakage and ensure statistical validity?
7. What CI/CD gates do you enforce for GenAI features?
8. How do you ensure reliable JSON outputs from LLMs?
9. Outline data governance for training/eval datasets (PII, lineage).
10. How do you stabilize response variability across deployments?
11. How do you test cost/performance trade-offs for API vs self-hosted LLM?
12. Institutionalizing ethical guardrails for AI systems—what’s your approach?

✅ Session 3: Automation Framework, Programming & Kubernetes

1. Design an enterprise automation platform for multiple teams.
2. How do you ensure hermetic builds and cross-platform consistency?
3. Implement deterministic test sharding for K8s workers (concept or code).
4. How do you make parallel tests reliable in Python?
5. Compare contract testing vs E2E and rollout strategy.
6. How do you integrate distributed tracing into test runs?
7. Architect a K8s test grid for GPU-based workloads.
8. How do you manage K8s security and secrets for test jobs?
9. How do you implement ephemeral preview environments per PR?
10. Reduce 5,000 E2E tests from 3h to <45m without quality loss.
11. How do you perform failure forensics for pods in K8s?
12. Provide a K8s Job template for sharded test execution.

✅ Session 4: General / Problem Solving

1. Go/No-Go decision with incomplete perf data and known P2s—what’s your process?
2. Outline a post-release outage RCA framework.
3. Which QA metrics matter at senior level (leading vs lagging)?
4. Debug a Python deadlock in an automation controller.
5. How do you make tests robust in a distributed system with message duplication?
6. Logical puzzle: 2 GPUs, 10 OS×Driver×CUDA combos, limited time—maximize confidence.
7. Build vs Buy for a test data management tool—decision factors.
8. How do you lead through disagreement with devs on a P1 bug?
9. Design a self-serve QA platform for the org.
10. Add security-focused QA to an existing pipeline.
11. Create a 1-page Go/No-Go summary for execs—what’s in it?
12. How do you uplevel the QA team strategically?

✅ Do you want me to package these into a downloadable Markdown file (with sections, numbering, and space for your answers), so you can print or use as a practice workbook? Or should I also include difficulty tags and expected time per question in that file?

Here’s the full expanded list of 30+ senior-level questions per section (total 120+ questions). These are designed for deep practice—covering strategy, architecture, debugging, and leadership scenarios.

✅ Section 1: QA Methodology, Linux & Docker (30 Questions)

1. Design a risk-based test strategy for a GPU driver release across multiple OSes.
2. Define exit criteria for a high-risk release with multiple dependencies.
3. How do you measure test effectiveness beyond code coverage?
4. Explain Defect Removal Efficiency (DRE) and how you’d track it.
5. How do you enforce flake budgets across teams?
6. What’s your approach to test impact analysis in large repos?
7. How do you ensure traceability from requirements to tests in a CI/CD world?
8. How do you design a test matrix for combinatorial explosion (OS × GPU × CUDA)?
9. When do you choose bare-metal vs VM vs container for testing?
10. How do you scale soak/stress tests without starving shared labs?
11. Define a defect triage policy and approach to escape analysis.
12. How do you handle security testing in QA for Linux-based systems?
13. Explain kernel panic debugging steps for GPU drivers.
14. How do you capture and analyze vmcore dumps?
15. Which Linux perf tools do you use for CPU/memory bottlenecks?
16. How do you debug NUMA-related performance issues?
17. Explain cgroups v2 and how you’d use it for resource isolation.
18. How do you monitor GPU utilization in Linux under load?
19. Explain Docker isolation primitives (namespaces, seccomp, AppArmor).
20. How do you harden a Docker container for CI environments?
21. How do you build secure, reproducible CUDA-enabled Docker images?
22. Explain multi-stage Docker builds and why they matter.
23. How do you troubleshoot networking issues inside Docker?
24. How do you persist test artifacts in ephemeral containers?
25. How do you manage Docker image sprawl in large orgs?
26. How do you enforce SBOM and vulnerability scanning in CI?
27. How do you ensure reproducibility across customer environments?
28. How do you validate driver compatibility across multiple Linux kernels?
29. How do you design chaos experiments for GPU workloads?
30. How do you integrate Linux kernel self-tests into your QA pipeline?

✅ Section 2: Gen AI & AI Tools (30 Questions)

1. Design an evaluation plan for a RAG-powered feature (metrics, datasets).
2. How do you benchmark LLM serving performance on A100 vs H100?
3. How do you measure hallucination rates in LLM outputs?
4. Explain BLEU, ROUGE, and BERTScore—when to use each.
5. How do you test bias and fairness in LLM responses?
6. How do you design adversarial prompt tests for jailbreak detection?
7. How do you enforce safety guardrails in GenAI systems?
8. How do you validate retrieval accuracy in RAG pipelines?
9. How do you test context window overflow scenarios?
10. How do you ensure deterministic outputs for regression testing?
11. How do you integrate AI model testing into CI/CD?
12. How do you monitor model drift in production?
13. How do you validate model reproducibility across environments?
14. How do you test multi-modal models (text + image)?
15. How do you benchmark latency and throughput for LLM APIs?
16. How do you test cost-performance trade-offs for API vs self-hosted LLM?
17. How do you validate LoRA fine-tuned models for catastrophic forgetting?
18. How do you design golden datasets for AI regression testing?
19. How do you test structured output reliability (JSON, XML)?
20. How do you enforce schema validation in LLM outputs?
21. How do you test tool-augmented LLMs for security?
22. How do you validate prompt templates at scale?
23. How do you test streaming responses for latency and correctness?
24. How do you measure energy efficiency of AI inference?
25. How do you validate GPU memory utilization under load?
26. How do you test fallback mechanisms for AI services?
27. How do you validate A/B experiments for AI features?
28. How do you enforce ethical compliance in AI QA?
29. How do you test multi-language support in LLMs?
30. How do you validate safety filters for harmful content?

✅ Section 3: Automation Framework, Programming & Kubernetes (30 Questions)

1. Design an enterprise automation platform for multiple teams.
2. How do you enforce coding standards in automation frameworks?
3. How do you implement test selection based on code changes?
4. How do you design parallel test execution for 10,000 tests?
5. How do you implement deterministic test sharding in Python?
6. How do you handle test data versioning in CI/CD?
7. How do you integrate observability into test runs?
8. How do you implement distributed tracing for test failures?
9. How do you design self-healing test infrastructure?
10. How do you enforce test flakiness budgets in CI?
11. How do you implement retry logic without masking real bugs?
12. How do you design contract testing strategy for microservices?
13. How do you integrate security scanning into automation pipelines?
14. How do you implement test impact analysis in monorepos?
15. How do you optimize Python test performance for large suites?
16. How do you enforce type safety in Python automation code?
17. How do you design async test execution in Python?
18. How do you implement resource cleanup in distributed tests?
19. How do you design K8s-based test grids for GPU workloads?
20. How do you manage K8s secrets for test jobs?
21. How do you enforce PodSecurity standards in test clusters?
22. How do you implement ephemeral environments per PR?
23. How do you scale test runners dynamically in K8s?
24. How do you implement node affinity for GPU tests in K8s?
25. How do you monitor test job health in K8s?
26. How do you debug failing pods in a CI pipeline?
27. How do you enforce RBAC policies for QA pipelines?
28. How do you implement chaos testing in K8s environments?
29. How do you integrate Helm/Kustomize for test deployments?
30. How do you enforce cost controls for large-scale test runs?

✅ Section 4: General / Problem Solving (30 Questions)

1. How do you make a Go/No-Go decision with incomplete data?
2. How do you handle conflicting priorities across teams?
3. How do you estimate testing effort for a complex feature?
4. How do you debug intermittent failures in CI?
5. How do you handle production outages caused by QA misses?
6. How do you design a postmortem process for escaped defects?
7. How do you measure QA ROI in an organization?
8. How do you communicate risk to executives effectively?
9. How do you handle disagreements with developers on bug severity?
10. How do you design a QA roadmap for a new product line?
11. How do you mentor junior QA engineers effectively?
12. How do you handle time zone challenges in global QA teams?
13. How do you design KPIs for QA teams?
14. How do you handle last-minute scope changes before release?
15. How do you design a QA hiring strategy for scaling teams?
16. How do you handle technical debt in test automation?
17. How do you design a self-service QA platform for developers?
18. How do you enforce quality gates in CI/CD pipelines?
19. How do you handle security incidents discovered during QA?
20. How do you design a risk register for QA activities?
21. How do you handle compliance testing for regulated industries?
22. How do you design a QA budget for a large program?
23. How do you handle vendor QA assessments?
24. How do you design a QA knowledge base for the org?
25. How do you handle escalations from customers on quality issues?
26. How do you design a QA metrics dashboard for leadership?
27. How do you handle cross-functional dependencies in QA?
28. How do you design a QA training program for new hires?
29. How do you handle QA in an Agile vs DevOps environment?
30. How do you design a continuous improvement plan for QA?

✅ Total: 120+ questions across 4 sections.

👉 Do you want me to create a downloadable Markdown workbook with:

• All 120+ questions
• Space for your answers
• Difficulty tags (Easy/Medium/Hard)
• Estimated time per question
• Checklist for tracking progress?

Or keep it as text only?