I. Effective Study Methods for the NCP-AIO Exam
1. “Tool-to-Scenario Mapping” — Learn tools in real-world context
Why it matters: NCP-AIO doesn’t test you on memorizing commands. It tests your ability to think and act like a real AI infrastructure engineer.
How to apply: Every time you study a tool (e.g., Slurm, BCM, DCGM, Fleet Command), focus on:
When and why to use it (scenario).
How to use it (command-line or GUI).
What to check when something goes wrong.
Example:
Don’t just memorize dcgmi health -c. Understand: “If a GPU node fails a health check, how should this affect Slurm scheduling? Should the node be marked unavailable?”
2. Pomodoro Technique + Active Recall
Use 25-minute focused study sessions followed by 5-minute breaks.
After each session, close your notes and explain what you just learned—verbally or in writing.
Example:
After learning how to configure MIG, try explaining from memory:
“Enable MIG → Create instances → Assign to workload → Monitor usage”
Recalling from memory strengthens retention far more than re-reading notes.
3. Spaced Repetition Based on Ebbinghaus Forgetting Curve
Review key concepts on Day 2, Day 4, and Day 7 after the first learning.
Ask yourself active questions each time:
“How does Fleet Command register edge devices?”
“What role does QOS play in Slurm job priority?”
Recommended tools:
Use flashcard platforms like Anki to build your own question decks.
4. Build a “Tool & Workflow Map”
Visually map which tools are used in which stage of the AI ops workflow:
GPU Resource Management: MIG, Slurm, K8s GPU Plugin
Deployment & Setup: BCM, GPU Operator, DOCA
Monitoring & Diagnostics: DCGM, nvidia-smi, Prometheus
Containerization: NVIDIA Container Toolkit, Fleet Command
This helps you understand the big picture and improve recall under real exam pressure.
5. Hands-On Practice is Critical
Even without full hardware access, simulate environments to practice:
Install and use Slurm (even in a VM).
Set up K8s with fake GPU plugins.
Deploy DCGM exporter to Prometheus.
Write YAML files for GPU pods (
nvidia.com/gpu).
The more you practice operations, the better you'll perform on scenario-based questions.
II. Practical Exam-Taking Strategies
1. Focus on Real-World Thinking
Most questions present operational scenarios, not trivia. Always ask:
“If I were the on-call engineer, what would I check or do first?”
Example:
Question: "A Kubernetes pod fails to access GPU. What should you do?"
Wrong: Check the Slurm queue
Correct: Check if the NVIDIA device plugin DaemonSet is running
2. Master Key Terminology and Acronyms
You must fully understand terms like:
BCM – Base Command Manager
MIG – Multi-Instance GPU
DCGM – Data Center GPU Manager
QOS – Quality of Service
RBAC, SlurmDBD, Fleet Node, NCCL, NVSwitch, DOCA
Tip: Build a glossary where each term has:
Function
Use case
Key commands or configs
3. Eliminate Wrong Choices Systematically
Each multiple-choice question usually has:
One obviously wrong option
One unrelated but tempting distractor
One technically correct but contextually wrong
One best answer
Strategy: Eliminate the easy wrong answers first. Then evaluate what fits the scenario best.
4. Use the 2-Pass Method for Time Management
First pass: Quickly answer questions you’re sure of.
Second pass: Return to harder questions and analyze them slowly.
5. Know Diagnostic Commands and Logs by Heart
You’ll be asked what command to use for troubleshooting. Know these cold:
Slurm:
squeue,sacct,sinfo, Slurm logsKubernetes:
kubectl describe,kubectl logs,kubectl get eventsGPU tools:
nvidia-smi,dcgmi,nsight,nvidia-smi topo -m
Create troubleshooting flowcharts, e.g.:
- Pod stuck in "Pending" → Check GPU request → Node label → Plugin DaemonSet → Scheduler logs
III. Final Advice
Understand, don’t memorize. Know how the system works, not just commands.
Practice often. Simulate deployments, errors, and recovery procedures.
Review regularly. Use spaced repetition to lock in difficult concepts.
Think like an operator. Always ask “what would I do in a real outage?”