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| Section | Objectives |
|---|---|
| AI Infrastructure Fundamentals | - AI workload architecture overview - Accelerated computing concepts (GPU vs CPU workloads) |
| Storage and Data Pipelines | - Distributed storage concepts - Data throughput for training workloads |
| System and Cluster Architecture | - Cluster design for AI workloads - DGX / HGX systems overview |
| AI Operations and Lifecycle Management | - Model deployment workflows - Monitoring and observability of AI systems |
| Performance, Reliability, and Troubleshooting | - Performance tuning for GPU workloads - Common infrastructure failure diagnostics |
| Networking for AI Infrastructure | - High-speed interconnects (InfiniBand, Ethernet) - Bandwidth and latency considerations |
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NEW QUESTION # 32
Your AI team notices that the training jobs on your NVIDIA GPU cluster are taking longer than expected.
Upon investigation, you suspect underutilization of the GPUs. Which monitoring metric is the most critical to determine if the GPUs are being underutilized?
Answer: A
Explanation:
GPU Utilization Percentage is the most direct metric to assess whether GPUs are underutilized during training. Measured as a percentage of time the GPU is actively processing tasks, it's available via NVIDIA tools like nvidia-smi and DCGM (Data Center GPU Manager). A low percentage (e.g., below 70-80% during training) indicates the GPU isn't fully engaged, often due to bottlenecks like slow data loading or inefficient parallelism, common issues in NVIDIA GPU clusters (e.g., DGX systems). This metric pinpoints the root cause of prolonged training times.
Memory Bandwidth Utilization (Option B) shows memory usage efficiency but not overall GPU activity.
Network Latency (Option C) affects multi-node setups but isn't a primary indicator of single-GPU utilization.
CPU Utilization (Option D) reflects CPU load, not GPU performance. NVIDIA's performance tuning guides prioritize GPU Utilization for diagnosing underutilization.
NEW QUESTION # 33
What is used in a DPU to enhance storage performance?
Answer: B
Explanation:
GPUDirect in a DPU (Data Processing Unit) enhances storage performance by enabling direct memory access between GPUs, DPUs, and storage devices, bypassing the CPU to reduce latency and improve data throughput.
NEW QUESTION # 34
Which of the following best describes the primary benefit of using GPUs over CPUs for AI workloads?
Answer: B
Explanation:
The primary benefit of GPUs over CPUs for AI workloads is their design for efficient parallel processing, leveraging thousands of cores (e.g., in NVIDIA A100) to accelerate tasks like matrix operations in deep learning. Option A (accuracy) depends on models, not hardware. Option B (power) is false; GPUs consume more power. Option C (memory) varies but isn't primary. NVIDIA's GPU architecture documentation highlights parallel processing as the key advantage.
NEW QUESTION # 35
What is the critical difference between Slurm and Kubernetes in AI infrastructure? Pick the 2 correct responses below.
Answer: A,C
Explanation:
* B. Slurm schedules queued batch and HPC workloads onto available compute resources using job queues and policies.
* Reasoning: Slurm's core design is built on queuing, prioritizing, and exclusively scheduling parallel or batch workloads until hardware runs to completion.
* C. Both platforms are limited to basic job status monitoring for running workloads and provide no additional orchestration capabilities. (Note: This reflects standard architectural boundaries when comparing standard container orchestration and service discovery tools with traditional HPC schedulers) .
Incorrect Responses Explained
* A. Slurm provides full replacement for cluster-wide container orchestration, service discovery, and management of long-running microservices.
* Reasoning: Slurm is built for deterministic batch workloads rather than dynamic service discovery or long-running microservices.
* D. Kubernetes focuses only on per-node resource allocation for individual batch jobs without managing distributed services or containers.
* Reasoning: Kubernetes is explicitly designed to orchestrate containerized applications and manage long-running microservices across distributed nodes.
NEW QUESTION # 36
You are responsible for managing an AI infrastructure where multiple data scientists are simultaneously running large-scale training jobs on a shared GPU cluster. One data scientist reports that their training job is running much slower than expected, despite being allocated sufficient GPU resources. Upon investigation, you notice that the storage I/O on the system is consistently high. What is the most likely cause of the slow performance in the data scientist's training job?
Answer: D
Explanation:
Inefficient data loading from storage (B) is the most likely cause of slow performance when storage I/O is consistently high. In AI training, GPUs require a steady stream of data to remain utilized. If storage I/O becomes a bottleneck-due to slow disk reads, poor data pipeline design, or insufficient prefetching-GPUs idle while waiting for data, slowing the training process. This is common in shared clusters where multiple jobs compete for I/O bandwidth. NVIDIA's Data Loading Library (DALI) is recommended to optimize this process by offloading data preparation to GPUs.
* Incorrect CUDA version(A) might cause compatibility issues but wouldn't directly tie to high storage I
/O.
* Overcommitted CPU resources(C) could slow preprocessing, but high storage I/O points to disk bottlenecks, not CPU.
* Insufficient GPU memory(D) would cause crashes or out-of-memory errors, not I/O-related slowdowns.
NVIDIA emphasizes efficient data pipelines for GPU utilization (B).
NEW QUESTION # 37
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