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NCA-AIIO Gap Analysis
Source: NVIDIA Official Study Guide (NCA-AIIO, Feb 2026)
Question bank: 163 questions (50 original + 95 from NVIDIA practice tests 1-4 + 18 gap coverage)
13
Well-covered subtopics
6
Thin coverage (1 question)
10
Missing technologies (now added)
Updated 2026-03-14: 18 gap-coverage questions (Q145-Q162) added addressing all priority areas. Total question bank: 163 questions.
Coverage Map
Domain 1: Essential AI Knowledge (38%) — 8 subtopics
| # | Official Subtopic | Coverage | Questions |
|---|
| 1.1 | NVIDIA software stack | Good | Q0,Q3,Q5,Q8,Q9,Q11,Q12,Q13,Q16 |
| 1.2 | Training vs inference requirements | Good | Q7,Q14,Q17 |
| 1.3 | AI vs ML vs DL | Thin (1q) | Q2 |
| 1.4 | AI adoption factors | Thin (1q) | Q4 |
| 1.5 | AI use cases & industries | Thin (1q) | Q15 (automotive only) |
| 1.6 | NVIDIA solutions purpose/use case | Good | Multiple |
| 1.7 | AI lifecycle software components | OK (2q) | Q6,Q18 |
| 1.8 | GPU vs CPU architecture | OK (2q) | Q1,Q10 |
Domain 2: AI Infrastructure (40%) — 10 subtopics
| # | Official Subtopic | Coverage | Questions |
|---|
| 2.1 | Hardware requirements for AI tasks | Partial | Q23,Q26,Q31 |
| 2.2 | Scale GPU infrastructure | OK (2q) | Q29,Q37 |
| 2.3 | Power & cooling in data centers | Good | Q26,Q27,Q35 |
| 2.4 | On-prem vs cloud | Thin (1q) | Q28 |
| 2.5 | Cluster components | Partial | Q29 |
| 2.6 | Facility requirements | NONE | — |
| 2.7 | Networking requirements for AI | Good | Q19,Q20,Q21,Q37 |
| 2.8 | DC networking protocols & concepts | Good | Q22,Q25,Q30 |
| 2.9 | High-speed network options | Good | Q32,Q33,Q38 |
| 2.10 | DPU purpose & benefits | Thin (1q) | Q24 |
Domain 3: AI Operations (22%) — 4 subtopics
| # | Official Subtopic | Coverage | Questions |
|---|
| 3.1 | DC management & monitoring | OK (3q) | Q39,Q46,Q48 |
| 3.2 | Orchestration & job scheduling | OK (3q) | Q41,Q42,Q47 |
| 3.3 | GPU monitoring metrics | Partial (2q) | Q43,Q45 |
| 3.4 | Virtualization for AI | Good (3q) | Q40,Q44,Q49 |
Topics with Zero Coverage (Now Addressed)
1. Facility Requirements (subtopic 2.6)
- Physical space, floor loading, weight per rack
- Fire suppression systems for high-density compute
- UPS and power redundancy (N+1, 2N)
- Electrical distribution and PDU considerations
- Physical security and access control
2. BMC (Baseboard Management Controller)
- Out-of-band remote hardware management
- Lights-out management (power cycle, BIOS, console access remotely)
3. GPUDirect Storage
- Direct path between storage devices and GPU memory (bypasses CPU and system memory)
- Distinct from GPUDirect RDMA (which is GPU-to-NIC)
4. NVIDIA Container Toolkit
- Enables Docker/Kubernetes containers to access host GPUs
- Lighter than VMs, preferred for AI workloads
- Deployed by GPU Operator in Kubernetes but also standalone
5. Containers vs VMs for AI
- Containers: lighter overhead, faster startup, preferred for AI
- VMs: stronger isolation, better multi-tenancy, more overhead
6. NVIDIA Dynamo
- Next-generation inference engine
7. Run:ai (now part of NVIDIA)
- GPU pooling across clusters
- Fractional GPU allocation
- Intelligent scheduling for AI workloads
8. Grace Hopper Superchip
- Combined Grace CPU + H100 GPU in a single module
- For workloads requiring tight CPU-GPU coupling
9. GPU Generation Comparisons
- A100: 80GB HBM2e, Ampere architecture
- H100: 80GB HBM3, Hopper architecture
- H200: 141GB HBM3e (memory-enhanced H100)
- B100/B200: Blackwell architecture (latest generation)
10. Omniverse / Digital Twins
- Manufacturing use case: predictive maintenance, quality inspection, digital twins
- NVIDIA Omniverse platform
Topics with Thin Coverage
1.3 AI vs ML vs DL 1 question
- Supervised vs unsupervised vs reinforcement learning
- Key architectures: CNNs, RNNs, Transformers
- When to use ML vs DL (data size, complexity trade-offs)
1.4 AI Adoption Factors 1 question
- Transfer learning and pre-trained models lowering barrier
- Cloud GPU-as-a-Service democratizing access
- Transformer architecture breakthrough
1.5 AI Use Cases & Industries 1 question
- Healthcare: drug discovery, medical imaging, genomics
- Finance: fraud detection, algorithmic trading, risk assessment
- Manufacturing: predictive maintenance, digital twins (Omniverse)
- Retail: recommendation engines, demand forecasting
- Telecom: network optimization, 5G
- Energy: grid optimization, exploration
2.4 On-prem vs Cloud 1 question
- CAPEX vs OPEX trade-off
- Hybrid approach (cloud for experimentation, on-prem for production)
- Time-to-deploy differences
- "Noisy neighbor" in cloud
2.10 DPU Benefits 1 question
- BlueField DPU runs its own OS
- "Third pillar" alongside CPU and GPU
- Secure multi-tenancy and workload isolation
- NVMe-oF storage offload
- Zero-trust security model
3.3 GPU Monitoring Metrics 2 questions
- Thermal throttling thresholds (~83-90C)
- PCIe / NVLink throughput monitoring
- Clock speed (base vs boost, throttling indicators)
- GPU utilization vs memory utilization interpretation
- Power draw relative to TDP
Gap analysis for NCA-AIIO Study Suite · Back to Study Suite