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| Topic |
Details |
| Topic 1 |
- AI Operations: This section of the exam measures the skills of data center operators and encompasses the management of AI environments. It requires describing essentials for AI data center management, monitoring, and cluster orchestration. Key topics include articulating measures for monitoring GPUs, understanding job scheduling, and identifying considerations for virtualizing accelerated infrastructure. The operational knowledge also covers tools for orchestration and the principles of MLOps.
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| Topic 2 |
- Essential AI knowledge: Exam Weight: This section of the exam measures the skills of IT professionals and covers foundational AI concepts. It includes understanding the NVIDIA software stack, differentiating between AI, machine learning, and deep learning, and comparing training versus inference. Key topics also involve explaining the factors behind AI's rapid adoption, identifying major AI use cases across industries, and describing the purpose of various NVIDIA solutions. The section requires knowledge of the software components in the AI development lifecycle and an ability to contrast GPU and CPU architectures.
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| Topic 3 |
- AI Infrastructure: This section of the exam measures the skills of IT professionals and focuses on the physical and architectural components needed for AI. It involves understanding the process of extracting insights from large datasets through data mining and visualization. Candidates must be able to compare models using statistical metrics and identify data trends. The infrastructure knowledge extends to data center platforms, energy-efficient computing, networking for AI, and the role of technologies like NVIDIA DPUs in transforming data centers.
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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q22-Q27):
NEW QUESTION # 22
What is an advantage of InfiniBand over Ethernet?
- A. InfiniBand offers lower latency than Ethernet.
- B. InfiniBand supports RDMA while Ethernet does not.
- C. InfiniBand always provides higher bandwidth than Ethernet.
Answer: A
Explanation:
InfiniBand's advantage over Ethernet lies in its lower latency, achieved through a streamlined protocol and hardware offloads, delivering microsecond-scale communication critical for AI clusters. While InfiniBand often offers high bandwidth, Ethernet can match or exceed it (e.g., 400 GbE), and Ethernet supports RDMA via RoCE, making latency the standout differentiator.
(Reference: NVIDIA Networking Documentation, Section on InfiniBand vs. Ethernet)
NEW QUESTION # 23
Your company is running a distributed AI application that involves real-time data ingestion from IoT devices spread across multiple locations. The AI model processing this data requires high throughput and low latency to deliver actionable insights in near real-time. Recently, the application has been experiencing intermittent delays and data loss, leading to decreased accuracy in the AI model's predictions. Which action would best improve the performance and reliability of the AI application in this scenario?
- A. Implementing a dedicated, high-bandwidth network link between IoT devices and the data processing system.
- B. Switching to a batch processing model to reduce the frequency of data transfers.
- C. Deploying a Content Delivery Network (CDN) to cache data closer to the IoT devices.
- D. Upgrading the IoT devices to more powerful hardware.
Answer: A
Explanation:
Real-time AI applications, especially those involving IoT devices, depend on rapid and reliable data ingestion to maintain low latency and high throughput. Intermittent delays and data loss suggest a bottleneck in the network connecting the IoT devices to the processing system. Implementing a dedicated, high-bandwidth network link (e.g., using NVIDIA's InfiniBand or high-speed Ethernet solutions) ensures that data flows seamlessly from distributed IoT devices to the AI cluster, reducing latency and preventing packet loss. This aligns with NVIDIA's focus on high-performance networking for distributed AI, as seen in DGX systems and NVIDIA BlueField DPUs, which offload and accelerate network traffic.
Switching to batch processing (Option B) sacrifices real-time performance, which is critical for this use case, making it unsuitable. A CDN (Option C) is designed for static content delivery, not dynamic IoT data streams, and wouldn't address the core issue of real-time ingestion. Upgrading IoT hardware (Option D) might improve local processing but doesn't solve network-related delays or data loss between devices and the AI system. A robust network infrastructure is the most effective solution here.
NEW QUESTION # 24
In which industry has AI most significantly improved operational efficiency through predictive maintenance, leading to reduced downtime and maintenance costs?
- A. Healthcare
- B. Retail
- C. Manufacturing
- D. Finance
Answer: C
Explanation:
Manufacturing has seen the most significant improvements in operational efficiency through AI-driven predictive maintenance, leveraging NVIDIA's GPU-accelerated solutions like NVIDIA DGX systems and AI software stacks. Predictive maintenance uses machine learning models to analyze sensor data (e.g., vibration, temperature) from equipment, predicting failures before they occur, thus reducing downtime and maintenance costs. NVIDIA's documentation highlights manufacturing use cases, such as those in industrial IoT, where AI optimizes production lines (e.g., automotiveassembly). While finance (Option A) benefits from AI in fraud detection, retail (Option B) in supply chain optimization, and healthcare (Option D) in diagnostics, manufacturing stands out for tangible cost savings via predictive maintenance, as evidenced by NVIDIA's industry-specific success stories.
NEW QUESTION # 25
You are part of a team that is setting up an AI infrastructure using NVIDIA's DGX systems. The infrastructure is intended to support multiple AI workloads, including training, inference, and dataanalysis.
You have been tasked with analyzing system logs to identify performance bottlenecks under the supervision of a senior engineer. Which log file would be most useful to analyze when diagnosing GPU performance issues in this scenario?
- A. Application error logs
- B. NVIDIA GPU utilization logs (nvidia-smi)
- C. System kernel logs (dmesg)
- D. Network traffic logs
Answer: B
Explanation:
NVIDIA GPU utilization logs from nvidia-smi are most useful for diagnosing GPU performance issues on DGX systems. These logs provide real-time metrics (e.g., utilization, memory usage, processes), pinpointing bottlenecks like underutilization or contention. Option A (network logs) aids distributed issues, not GPU- specific ones. Option C (kernel logs) tracks system events, not GPU performance. Option D (application logs) focuses on software, not hardware. NVIDIA's DGX troubleshooting guides prioritize nvidia-smi for GPU diagnostics.
NEW QUESTION # 26
Your team is tasked with deploying a new AI-driven application that needs to perform real-time video processing and analytics on high-resolution video streams. The application must analyze multiple video feeds simultaneously to detect and classify objects with minimal latency. Considering the processing demands, which hardware architecture would be the most suitable for this scenario?
- A. Use CPUs for video analytics and GPUs for managing network traffic
- B. Deploy CPUs exclusively for all video processing tasks
- C. Deploy GPUs to handle the video processing and analytics
- D. Deploy a combination of CPUs and FPGAs for video processing
Answer: C
Explanation:
Real-time video processing and analytics on high-resolution streams require massive parallel computation, which NVIDIA GPUs excel at. GPUs handle tasks like object detection and classification (e.g., via CNNs) efficiently, minimizing latency for multiple feeds. NVIDIA's DeepStream SDK and TensorRT optimize this pipeline on GPUs, making them the ideal architecture for such workloads, as seen in DGX and Jetson deployments.
CPUs alone (Option A) lack the parallelism for real-time video analytics, causing delays. Using CPUs for analytics and GPUs for traffic (Option C) misaligns strengths-GPUs should handle compute-intensive analytics. CPUs with FPGAs (Option D) offer flexibility but lack the optimized software ecosystem (e.g., CUDA) that NVIDIA GPUs provide for AI. Option B is the most suitable, per NVIDIA's video analytics focus.
NEW QUESTION # 27
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