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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA-Certified Associate AI Infrastructure and Operations |
| Exam Number: | NCA-AIIO |
| Certificate Validity Period: | 2 years |
| Exam Format: | Multiple-response, Multiple-choice |
| Passing Score: | 70% |
| Available Languages: | English |
| Exam Duration: | 60 minutes |
| Related Certifications: | NVIDIA-Certified Professional AI Infrastructure NVIDIA-Certified Professional AI Operations |
| Real Exam Qty: | 50 |
| Exam Price: | USD 125 |
| Recommended Training: | NVIDIA AI Infrastructure Fundamentals Course |
| Exam Registration: | NVIDIA Certification Portal |
| Sample Questions: | NVIDIA NCA-AIIO Sample Questions |
| Exam Way: | Online remote proctored exam |
| Pre Condition: | Basic understanding of data center infrastructure; no mandatory prior certification required |
| Official Syllabus URL: | https://www.nvidia.com/en-us/learn/certification/ai-infrastructure-operations-associate/ |
>> Real NCA-AIIO Exam Questions <<
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NEW QUESTION # 69
Which feature of RDMA reduces CPU utilization and lowers latency?
Answer: C
Explanation:
Remote Direct Memory Access (RDMA) reduces CPU utilization and latency through network adapters with hardware offloading. These adapters handle data transfers directly between memory locations, bypassing CPU-intensive operations like memory copies and protocol processing. Larger buffers and software like Magnum I/O may enhance performance, but hardware offloading is the core RDMA feature delivering these benefits.
NEW QUESTION # 70
What aspect of AI infrastructure design is MOST critical for ensuring high availability of production AI services during hardware or node failures?
Answer: D
Explanation:
The correct answer is A because high availability depends on redundancy, failover, orchestration, and scaling rather than manual recovery. NVIDIA Run:ai documentation says the goal of high availability is "to reduce downtime and eliminate single points of failure" by using Kubernetes best practices with NVIDIA Run:ai configuration. It identifies two fundamental strategies: using multiple system nodes for critical services and replicating core and third-party services so workloads are distributed and single points of failure are reduced.
NVIDIA also states that if a component fails on one node, requests can be routed to another instance.
This aligns exactly with automated failover orchestration and elastic scaling across redundant nodes. NVIDIA' s NIM autoscaling guidance also describes using Kubernetes Horizontal Pod Autoscaling with NVIDIA NIM microservices to automatically scale services up and down based on metrics, which supports production availability and capacity management.
Why the other options are incorrect: Custom GPU driver builds do not ensure service continuity during node failure. Dataset expansion is related to model training quality, not production high availability. Manual GPU restarts and ad hoc redeployment are reactive and unreliable, while production AI services require automated orchestration and redundancy.
Reference: NVIDIA Run:ai High Availability documentation; NVIDIA Technical Blog on Horizontal Autoscaling of NVIDIA NIM Microservices on Kubernetes.
NEW QUESTION # 71
What factor has led to the rapid increase and adoption of AI?
Answer: A
Explanation:
The efficient use of large amounts of data to train models has driven the rapid growth of AI, enabling more accurate and capable machine learning and deep learning systems across industries.
NEW QUESTION # 72
What is a key advantage of dynamic, priority-based job scheduling in an AI cluster?
Answer: B
Explanation:
The correct answer is C because priority-based scheduling is specifically intended to allocate scarce GPU resources according to workload importance. NVIDIA Run:ai documentation states: "Optimized workload scheduling - Ensure high-priority jobs get GPU resources. Workloads dynamically receive resources based on demand." NVIDIA also explains that the Run:ai Scheduler "allows the prioritization of workloads across different departments and projects within the organization at large scales, based on the resource distribution set by the system administrator." NVIDIA's scheduler concepts further confirm that "Workload's priority sets the scheduling precedence within a project," and that high-priority workloads can preempt lower-priority preemptible workloads in the same scheduling queue. Therefore, the key advantage is that urgent or high-priority workloads can receive timely access to constrained compute resources when resource contention occurs.
Reference: NVIDIA Run:ai Documentation - Overview; Introduction to Workloads; Scheduler Concepts and Principles.
NEW QUESTION # 73
In the field of Artificial Intelligence, there is a hierarchical structure of subsets that delineates the relationship between different areas of study and application within AI. What is the hierarchical structure of subsets?
Answer: C
Explanation:
The correct hierarchy is Machine Learning # Deep Learning # Generative AI. NVIDIA explains that "Deep learning is a subset of AI and machine learning that uses multi-layered artificial neural networks." NVIDIA's deep learning glossary also states that "Deep learning is a subset of artificial intelligence (AI) and machine learning (ML)" and describes the progression of AI, machine learning, and deep learning.
For this exam question, the closest correct subset sequence among the options is B: Machine Learning, Deep Learning, Generative AI. Generative AI is commonly built from deep learning models that generate new content, so it is the most specific category in the listed hierarchy. Option A reverses the hierarchy, and option C incorrectly places Generative AI before Deep Learning.
Reference: NVIDIA Deep Learning Developer Page; NVIDIA Deep Learning Glossary.
NEW QUESTION # 74
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