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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Essential AI Knowledge | 38% | - Purpose and benefits of DPU in data center - Accelerated computing use cases and industry applications - AI, Machine Learning, and Deep Learning concepts and differences - GPU vs CPU architecture and characteristics - NVIDIA software stack and components in AI environment |
| Topic 2: AI Operations | 22% | - Cluster orchestration and job scheduling concepts - Scaling and maintenance considerations - AI infrastructure monitoring and management basics - Operational best practices for NVIDIA solutions |
| Topic 3: AI Infrastructure | 40% | - Data center power, cooling and physical requirements - On-premises vs cloud infrastructure comparison - AI networking fundamentals and considerations - GPU cluster design and configuration principles - Hardware requirements for training and inference workloads |
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NEW QUESTION # 47
What is a key value of using NVIDIA NIMs?
Answer: C
Explanation:
NVIDIA NIMs (NVIDIA Inference Microservices) are pre-built, GPU-accelerated microservices with standardized APIs, designed to simplify and accelerate AI model deployment across diverse environments- clouds, data centers, and edge devices. Their key value lies in enabling fast, turnkey inference without requiring custom deployment pipelines, reducing setup time and complexity. While community support and SDK deployment may be tangential benefits, they are not the primary focus of NIMs.
(Reference: NVIDIA NIMs Documentation, Overview Section)
NEW QUESTION # 48
What aspect of AI infrastructure design is MOST critical for ensuring high availability of production AI services during hardware or node failures?
Answer: A
Explanation:
The correct answer is A because high availability depends on redundancy, failover, orchestration, and scaling rather than manual recovery. NVIDIA Run:ai documentation says the goal of high availability is "to reduce downtime and eliminate single points of failure" by using Kubernetes best practices with NVIDIA Run:ai configuration. It identifies two fundamental strategies: using multiple system nodes for critical services and replicating core and third-party services so workloads are distributed and single points of failure are reduced.
NVIDIA also states that if a component fails on one node, requests can be routed to another instance.
This aligns exactly with automated failover orchestration and elastic scaling across redundant nodes. NVIDIA' s NIM autoscaling guidance also describes using Kubernetes Horizontal Pod Autoscaling with NVIDIA NIM microservices to automatically scale services up and down based on metrics, which supports production availability and capacity management.
Why the other options are incorrect: Custom GPU driver builds do not ensure service continuity during node failure. Dataset expansion is related to model training quality, not production high availability. Manual GPU restarts and ad hoc redeployment are reactive and unreliable, while production AI services require automated orchestration and redundancy.
Reference: NVIDIA Run:ai High Availability documentation; NVIDIA Technical Blog on Horizontal Autoscaling of NVIDIA NIM Microservices on Kubernetes.
NEW QUESTION # 49
Which NVIDIA tool aids data center monitoring and management?
Answer: D
Explanation:
NVIDIA Data Center GPU Manager (DCGM) aids data center monitoring and management by providing detailed GPU telemetry, health diagnostics, and performance tracking at scale. Clara targets healthcare, TensorRT optimizes inference, and Mellanox Insight isn't a standard NVIDIA tool, making DCGM the go-to solution.
NEW QUESTION # 50
What is the name of NVIDIA's SDK that accelerates machine learning?
Answer: B
Explanation:
The CUDA Deep Neural Network library (cuDNN) is NVIDIA's SDK specifically designed to accelerate machine learning, particularly deep learning tasks. It provides highly optimized implementations of neural network primitives-such as convolutions, pooling, normalization, and activation functions-leveraging GPU parallelism. Clara focuses on healthcare applications, and RAPIDS accelerates data science workflows, but cuDNN is the core SDK for machine learning acceleration.
NEW QUESTION # 51
In the field of Artificial Intelligence, there is a hierarchical structure of subsets that delineates the relationship between different areas of study and application within AI. What is the hierarchical structure of subsets?
Answer: A
Explanation:
The correct hierarchy is Machine Learning # Deep Learning # Generative AI. NVIDIA explains that "Deep learning is a subset of AI and machine learning that uses multi-layered artificial neural networks." NVIDIA's deep learning glossary also states that "Deep learning is a subset of artificial intelligence (AI) and machine learning (ML)" and describes the progression of AI, machine learning, and deep learning.
For this exam question, the closest correct subset sequence among the options is B: Machine Learning, Deep Learning, Generative AI. Generative AI is commonly built from deep learning models that generate new content, so it is the most specific category in the listed hierarchy. Option A reverses the hierarchy, and option C incorrectly places Generative AI before Deep Learning.
Reference: NVIDIA Deep Learning Developer Page; NVIDIA Deep Learning Glossary.
NEW QUESTION # 52
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