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NEW QUESTION # 115
Which of the following statements is true about GPUs and CPUs?
Answer: A
Explanation:
GPUs and CPUs are architecturally distinct due to their optimization goals. GPUs feature thousands of simpler cores designed for massive parallelism, excelling at executing many lightweight threads concurrently--ideal for tasks like matrix operations in AI. CPUs, conversely, have fewer, more complex cores optimized for sequential processing and handling intricate control flows, making them suited for serial tasks. This divergence in design means GPUs outperform CPUs in parallel workloads, while CPUs excel in single-threaded performance, contradicting claims of identical architectures or interchangeable use.
NEW QUESTION # 116
Which feature of RDMA reduces CPU utilization and lowers latency?
Answer: B
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 # 117
Your AI data center is running multiple high-performance GPU workloads, and you notice that certain servers are being underutilized while others are consistently at full capacity, leading to inefficiencies. Which of the following strategies would be most effective in balancing the workload across your AI data center?
Answer: D
Explanation:
The NVIDIA GPU Operator with Kubernetes (C) automates resource scheduling and workload balancing across GPU clusters. It integrates GPU awareness into Kubernetes, dynamically allocating workloads to underutilized servers based on real-time utilization, priority, and resource demands. This ensures efficient use of all GPUs, reducing inefficiencies without manual intervention.
* Horizontal scaling(A) adds more servers, increasing capacity but not addressing the imbalance- underutilized servers would remain inefficient.
* Manual reassignment(B) is impractical for large-scale, dynamic workloads and lacks scalability.
* Increasing cooling capacity(D) improves hardware reliability but doesn't balanceworkloads.
The GPU Operator's automation and integration with Kubernetes make it the most effective solution (C).
NEW QUESTION # 118
Why use NVIDIA GPUDirect Storage in an AI cluster?
Answer: B
Explanation:
NVIDIA GPUDirect Storage is used to create a direct data path between storage and GPU memory. NVIDIA' s GPUDirect Storage Overview Guide states: "GPUDirect Storage (GDS) enables a direct data path for direct memory access (DMA) transfers between GPU memory and storage, which avoids a bounce buffer through the CPU." It also says this direct path can relieve system bandwidth bottlenecks and reduce CPU latency and utilization load.
NVIDIA's GPUDirect Storage technical blog further explains that GPUDirect Storage enables "a direct data path between local or remote storage, like NVMe or NVMe over Fabric (NVMe-oF), and GPU memory." Therefore, the correct answer is C: it enables peer-to-peer memory transfers between GPUs and NVMe storage.
Why the other options are incorrect: GPUDirect Storage is not primarily about TCP/IP transmission between GPUs and CPUs. It does simplify and bypass parts of the traditional CPU-centric storage path, but the more precise answer is direct GPU-memory-to-storage transfer. It does not increase GPU clock rates.
Reference: NVIDIA GPUDirect Storage Overview Guide; NVIDIA Developer Blog - A Direct Path Between Storage and GPU Memory.
NEW QUESTION # 119
You are assisting a senior researcher in analyzing the results of several AI model experiments conducted with different training datasets and hyperparameter configurations. The goal is to understand how these variables influence model overfitting and generalization. Which method would best help in identifying trends and relationships between dataset characteristics, hyperparameters, and the risk of overfitting?
Answer: B
Explanation:
Conducting a decision tree analysis (D) best identifies trends and relationships between datasetcharacteristics (e.g., size, diversity), hyperparameters (e.g., learning rate, batch size), and overfitting risk. Decision trees model complex, non-linear interactions, revealing which variables most influence generalization (e.g., high learning rate causing overfitting). Tools like NVIDIA RAPIDS cuML support such analysis on GPUs, handling large experiment datasets efficiently.
* Time series analysis(A) tracks accuracy over epochs but doesn't link to dataset/hyperparameter effects.
* Scatter plot(B) visualizes overfitting (training vs. validation gap) but lacks explanatory depth for multiple variables.
* Histogram(C) shows overfitting frequency but not causal relationships.
Decision trees provide actionable insights for this research goal (D).
NEW QUESTION # 120
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