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NEW QUESTION # 104
Which two components are included in GPU Operator? (Choose two.)
Answer: C,D
Explanation:
The NVIDIA GPU Operator is a tool for automating GPU resource management in Kubernetes environments.
It includes two key components: GPU drivers, which provide the necessary software to interface with NVIDIA GPUs, and the NVIDIA Data Center GPU Manager (DCGM), which offers health monitoring, telemetry, and diagnostics for GPU clusters. Frameworks like PyTorch and TensorFlow are separate AI development tools, not part of the GPU Operator, which focuses on infrastructure rather than application layers.
(Reference: NVIDIA GPU Operator Documentation, Components Section)
NEW QUESTION # 105
You are working on a project that involves monitoring the performance of an AI model deployed in production. The model's accuracy and latency metrics are being tracked over time. Your task, under the guidance of a senior engineer, is to create visualizations that help the team understand trends in these metrics and identify any potential issues. Which visualization would be most effective for showing trends in both accuracy and latency metrics over time?
Answer: D
Explanation:
Tracking accuracy and latency trends over time requires a visualization that shows both metrics' evolution clearly. A dual-axis line chart, with accuracy on one axis and latency on the other, plots each as a line against time, revealing correlations (e.g., latency spikes reducing accuracy) and trends. NVIDIA RAPIDS supports such visualizations on GPUs, enhancing real-time monitoring in production environments like DGX or Triton deployments.
Pie charts (Option A) show distributions, not trends. Box plots (Option B) summarize static data, not time- based changes. Stacked area charts (Option C) imply cumulative values, confusing for independent metrics.
Dual-axis is NVIDIA-aligned for performance analysis.
NEW QUESTION # 106
What is the critical difference between Slurm and Kubernetes in AI infrastructure? Pick the 2 correct responses below.
Answer: A,D
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 # 107
When deploying high-density workloads in a data center, what are the three main resource constraints that need to be considered?
Answer: C
Explanation:
High-density workloads (e.g., GPU clusters for AI) strain data center resources, primarily power (to supply dense servers), cooling (to dissipate heat from tightly packed hardware), and physical space (to house equipment). While processing speed, bandwidth, and other factors matter, power, cooling, and space are the physical constraints most critical to deployment feasibility.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Data Center Resource Constraints)
NEW QUESTION # 108
You are assisting a professional administrator in ensuring data integrity during AI model training in an AI data center. Which of the following strategies would best contribute to maintaining data integrity across distributed GPU nodes?
Answer: C
Explanation:
Implementing a distributed file system with replication (e.g., GPFS, Lustre) is the best strategy to maintain data integrity across distributed GPU nodes during AI model training. This ensures allnodes access a consistent, replicated dataset, preventing corruption or discrepancies that could skew training results.
NVIDIA's "DGX SuperPOD Reference Architecture" and "AI Infrastructure and Operations Fundamentals" recommend distributed file systems for data consistency in multi-node GPU clusters, supporting scalability and fault tolerance.
A single master node (A) risks bottlenecks and single-point failures. DPUs for verification (B) offload networking, not data integrity tasks. Redundant processing (C) is inefficient and post-hoc. NVIDIA's guidance favors distributed file systems for integrity.
NEW QUESTION # 109
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