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  1. Home
  2. NVIDIA Certification
  3. NCA-AIIO Exam
  4. NVIDIA.NCA-AIIO.v2026-09-18.q123 Dumps
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Question 91

How is the architecture different in a GPU versus a CPU?

Correct Answer: B
A GPU's architecture is designed for massive parallelism, featuring thousands of lightweight cores that execute simple instructions across vast data elements simultaneously-ideal for tasks like AI training. In contrast, a CPU has fewer, complex cores optimized for sequential execution and branching logic. GPUs don't function as PCIe controllers (a hardware role), nor are they single-core designs, making the parallel execution focus the key differentiator. (Reference:
NVIDIA GPU Architecture Whitepaper, Section on GPU Design Principles)
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Question 92

What is the benefit of NGC?

Correct Answer: C
NGC (NVIDIA GPU Cloud) provides a curated set of GPU-optimized software, including pre- trained AI models, containers, and SDKs, which accelerates deployment and ensures compatibility with NVIDIA GPUs.
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Question 93

How is the architecture different in a GPU versus a CPU?

Correct Answer: B
A GPU's architecture is designed for massive parallelism, featuring thousands of lightweight cores that execute simple instructions across vast data elements simultaneously-ideal for tasks like AI training. In contrast, a CPU has fewer, complex cores optimized for sequential execution and branching logic. GPUs don' t function as PCIe controllers (a hardware role), nor are they single-core designs, making the parallel execution focus the key differentiator.
(Reference: NVIDIA GPU Architecture Whitepaper, Section on GPU Design Principles)
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Question 94

Which NVIDIA software component is specifically designed to accelerate the end-to-end data science workflow by leveraging GPU acceleration?

Correct Answer: D
NVIDIA RAPIDS is a suite of GPU-accelerated libraries (e.g., cuDF, cuML) designed to speed up the end-to- end data science workflow, from data preparation to machine learning, on NVIDIA GPUs. It integrates with tools like Pandas and Scikit-learn, providing dramatic performance boosts for tasks like ETL, feature engineering, and model training, as used in DGX systems and cloud environments.
The CUDA Toolkit (Option A) is a general-purpose GPU programming platform, not data science-specific.
DeepStream SDK (Option B) targets video analytics, not broad data science. TensorRT (Option C) optimizes inference, not the full workflow. RAPIDS is NVIDIA's dedicated data science accelerator.
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Question 95

During AI model deployment, your team notices significant performance degradation in inference workloads.
The model is deployed on an NVIDIA GPU cluster with Kubernetes. Which of the following could be the most likely cause of the degradation?

Correct Answer: D
Insufficient GPU memory allocation is the most likely cause of inference degradation in a Kubernetes- managed NVIDIA GPU cluster. Memory shortages lead to swapping or failures, slowing performance. Option A (outdated CUDA) may cause compatibility issues, not direct degradation. Option B (CPU bottlenecks) affects preprocessing, not inference. Option C (disk I/O) impacts data loading, not GPU tasks. NVIDIA's Kubernetes GPU Operator docs stress memory allocation.
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