NCA-AIIO Practice Questions 2025: Master Hardware & System Architecture
Boost your NCA-AIIO exam prep with expert-crafted practice questions focused on the Hardware & System Architecture domain. Get detailed explanations, real-world scenarios, and proven strategies to master key concepts and maximize your exam score. Perfect for IT professionals and aspiring AI infrastructure specialists.
The NVIDIA Certified Associate – AI Infrastructure & Operations (NCA-AIIO) is an entry-level certification designed for IT professionals, data center staff, DevOps engineers, and system administrators looking to validate their foundational knowledge of AI infrastructure, NVIDIA GPU and DPU architectures, and operational best practices for AI workloads.
Exam Details: 50 multiple-choice questions | 90 minutes | Passing score: 70% | Validity: 2-3 years | No prerequisites
Who should take it? Anyone involved in deploying, managing, or supporting AI and accelerated computing environments.
Full exam guide & breakdown
Why Hardware & System Architecture Matters
The Hardware & System Architecture domain is the backbone of the NCA-AIIO exam, accounting for roughly 40% of your score. Mastering this area means you can design, deploy, and optimize AI infrastructure for real-world data center environments. Topics include NVIDIA GPU architectures, multi-instance GPU (MIG), NVLink, data center networking, storage, power, and virtualization. Start with AI Infrastructure Fundamentals if you’re new to these concepts.
Exam Success Tips
- Understand the purpose of each hardware component in an AI stack (GPU, CPU, DPU, storage, networking).
- Be able to compare NVIDIA GPU architectures and identify when to use MIG, vGPU, or NVLink.
- Know best practices for power, cooling, and redundancy in high-density GPU environments.
- Practice scenario-based questions—these are common on the real exam.
- Review industry use cases (e.g., automotive, healthcare, finance) to understand how AI infrastructure is applied in real-world settings.
Hardware & System Architecture Practice Questions
1. GPU Architecture Fundamentals
Which NVIDIA GPU architecture feature allows partitioning a single GPU into multiple, isolated instances for better resource utilization in multi-tenant environments?
Show Answer & Explanation
Correct Answer: B
Explanation: Multi-Instance GPU (MIG) technology enables partitioning a single NVIDIA A100 or H100 GPU into up to seven independent GPU instances, each with dedicated memory, cache, and compute cores. This is vital for cloud service providers, research labs, and enterprises running multiple AI workloads on shared hardware—improving both resource efficiency and workload isolation.
Tip: Understand the difference between MIG (hardware-level partitioning) and vGPU (virtualization for VMs).
2. Data Center Infrastructure
In a high-performance AI data center, what is the primary advantage of InfiniBand over traditional Ethernet for inter-node communication?
Show Answer & Explanation
Correct Answer: B
Explanation: InfiniBand delivers bandwidth up to 400 Gbps and ultra-low latency, making it the preferred interconnect for distributed AI training and HPC clusters. This minimizes communication bottlenecks during large-scale model training, directly impacting overall training speed and efficiency.
Real-world note: Many top AI supercomputers and hyperscale data centers use InfiniBand for GPU-to-GPU and node-to-node communication.
3. Storage Architecture
Which storage configuration best balances performance and capacity for large AI training datasets?
Show Answer & Explanation
Correct Answer: B
Explanation: RAID 0 NVMe SSD arrays provide extremely high sequential read/write speeds—essential for feeding data-hungry AI training jobs—while scaling capacity by adding more drives. This configuration is common in AI labs and data centers where rapid data access is critical.
Scenario: If storage I/O is a bottleneck in your AI training pipeline, upgrading to a RAID 0 NVMe setup can yield immediate performance gains.
4. Power and Cooling
What’s the recommended power delivery setup for a high-density GPU server rack to ensure stability and minimize voltage fluctuations?
Show Answer & Explanation
Correct Answer: B
Explanation: Redundant power supplies connected to different electrical phases provide both failover protection and load balancing, which is essential for mission-critical AI workloads. This approach greatly reduces the risk of downtime and hardware failure due to power issues.
Best practice: Always pair power redundancy with robust cooling strategies, such as hot/cold aisle containment, to protect sensitive GPU hardware.
5. Virtualization Technology
In a virtualized environment, which NVIDIA technology lets multiple virtual machines share GPU resources securely?
Show Answer & Explanation
Correct Answer: B
Explanation: NVIDIA vGPU technology allows multiple virtual machines to share a single physical GPU, with hardware-level isolation and security. This is crucial for organizations running AI workloads in virtualized, multi-tenant environments such as VDI or cloud platforms.
Tip: Know the difference between vGPU (virtualization for VMs) and MIG (hardware partitioning for containers or processes).
Continue Your Exam Mastery
Advance your preparation with these related practice sets and resources:
- Next: Performance Optimization & Monitoring Practice Questions
- Related: Data Management & Storage Practice Questions
- Foundation: AI Infrastructure Fundamentals Practice Questions
- Overview: Return to Complete Study Guide
Pro Study Tip: Use a mix of practice questions, cheat sheets, and scenario-based mock exams to maximize retention and exam readiness. Review explanations for both correct and incorrect answers to deepen your understanding.
For a full domain-by-domain breakdown and more sample questions, see the Comprehensive NCA-AIIO Guide.
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