Quiz NVIDIA - NCA-AIIO–Reliable Hot Questions

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Free PDF Quiz NVIDIA NCA-AIIO - NVIDIA-Certified Associate AI Infrastructure and Operations Marvelous Hot Questions

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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q141-Q146):

NEW QUESTION # 141
A financial services company is developing a machine learning model to detect fraudulent transactions in real- time. They need to manage the entire AI lifecycle, from data preprocessing to model deployment and monitoring. Which combination of NVIDIA software components should they integrate to ensure an efficient and scalable AI development and deployment process?

A. NVIDIA DeepStream for data processing, CUDA for model training, and NGC for deployment.
B. NVIDIA Clara for model training, TensorRT for data processing, and Jetson for deployment.
C. NVIDIA RAPIDS for data processing, TensorRT for model optimization, and Triton Inference Server for deployment.
D. NVIDIA Metropolis for data collection, DIGITS for training, and Triton Inference Server for deployment.

Answer: C

Explanation:
The AI lifecycle for real-time fraud detection needs efficient data preprocessing, model optimization, and deployment. NVIDIA RAPIDS accelerates data processing on GPUs, TensorRToptimizes models for low- latency inference, and Triton Inference Server scales deployment across platforms-perfect for financial use cases in NVIDIA DGX or cloud environments.
Clara (Option A) is healthcare-focused, not fraud. DeepStream (Option C) is video-centric, and CUDA isn't a full training solution. Metropolis (Option D) targets smart cities, and DIGITS is outdated. Option B aligns with NVIDIA's lifecycle strategy.

NEW QUESTION # 142
You are tasked with contributing to the operations of an AI data center that requires high availability and minimal downtime. Which strategy would most effectively help maintain continuous AI operations in collaboration with the data center administrator?

A. Use GPUs in active-passive clusters, with DPUs handling real-time network failover and security
B. Implement a failover system where DPUs manage the AI model inference during GPU downtime
C. Schedule regular maintenance during peak hours to ensure that GPUs and DPUs are always operational
D. Deploy a redundant set of CPUs to take over GPU workloads in case of failure

Answer: A

Explanation:
UsingGPUs in active-passive clusters, with DPUs handling real-time network failover and security(C) is the most effective strategy for maintaining continuous AI operations with high availability and minimal downtime. Let's explore this in depth:
* Active-Passive GPU Clusters: In this setup, active GPUs handle the primary workload (e.g., training or inference), while passive GPUs remain on standby, ready to take over if an active node fails. This redundancy ensures that AI operations continue seamlessly during hardware failures, a common high- availability design in data centers. NVIDIA's GPU clusters (e.g., DGX systems) support such configurations, often managed via orchestration tools like Kubernetes with the NVIDIA GPU Operator.
* Role of DPUs: NVIDIA's Data Processing Units (e.g., BlueField DPUs) offload network, storage, and security tasks from CPUs and GPUs, enhancing system resilience. In this strategy, DPUs manage real- time network failover (e.g., rerouting traffic to passive GPUs) and security (e.g., encryption, isolation), ensuring uninterrupted data flow and protection during failover events. This reduces latency and downtime compared to CPU-managed failover.
* Why it works: The combination leverages GPU redundancy for compute continuity and DPU intelligence for network reliability, aligning with NVIDIA's vision of integrated AI infrastructure.
Monitoring tools (e.g., nvidia-smi, DPU metrics) enable proactive failover triggers, minimizing disruption.
Why not the other options?
* A (DPU-managed inference during GPU downtime): DPUs accelerate networking/storage, not inference, which requires GPU compute power-making this impractical.
* B (CPU redundancy): CPUs can't match GPU performance for AI workloads, leading to degraded operation, not continuity.
* D (Peak-hour maintenance): Scheduling maintenance during peak hours increases downtime, contradicting the goal.
NVIDIA's DPU and GPU cluster documentation supports this high-availability approach (C).

NEW QUESTION # 143
In a large-scale AI training environment, a data scientist needs to schedule multiple AI model training jobs with varying dependencies and priorities. Which orchestration strategy would be most effective to ensure optimal resource utilization and job execution order?

A. FIFO (First-In-First-Out) Queue
B. DAG-Based Workflow Orchestration
C. Manual Scheduling
D. Round-Robin Scheduling

Answer: B

Explanation:
DAG-Based Workflow Orchestration (A) (Directed Acyclic Graph) is the most effective strategy for scheduling multiple AI training jobs with varying dependencies and priorities. A DAG defines a workflow where tasks (e.g., data preprocessing, model training, validation) are represented as nodes, and edges indicate dependencies and execution order. Tools like Apache Airflow or Kubeflow Pipelines, which integrate with NVIDIA GPU clusters, use DAGs to optimize resource utilization by scheduling jobs based on their dependencies and priority levels, ensuring that high-priority tasks access GPUs when needed while respecting inter-task relationships. This approach is scalable and automated, critical for large-scale environments.
* Manual Scheduling(B) is error-prone, time-consuming, and impractical for complex, dependency- driven workloads.
* FIFO Queue(C) executes jobs in arrival order, ignoring dependencies or priorities, leading to inefficient GPU use.
* Round-Robin Scheduling(D) distributes jobs evenly but doesn't account for dependencies, risking delays or resource contention.
NVIDIA's AI infrastructure supports orchestration tools like Kubeflow, which leverage DAGs for optimal job management (A).

NEW QUESTION # 144
Your AI data center is experiencing fluctuating workloads where some AI models require significant computational resources at specific times, while others have a steady demand. Which of the following resource management strategies would be most effective in ensuring efficient use of GPU resources across varying workloads?

A. Manually Schedule Workloads Based on Expected Demand
B. Upgrade All GPUs to the Latest Model
C. Use Round-Robin Scheduling for Workloads
D. Implement NVIDIA MIG (Multi-Instance GPU) for Resource Partitioning

Answer: D

Explanation:
Implementing NVIDIA MIG (Multi-Instance GPU) for resource partitioning is the most effective strategy for ensuring efficient GPU resource use across fluctuating AI workloads. MIG, available on NVIDIA A100 GPUs, allows a single GPU to be divided into isolated instances with dedicated memory and compute resources. This enables dynamic allocation tailored to workload demands-assigning larger instances to resource-intensive tasks and smaller ones to steady tasks-maximizing utilization and flexibility. NVIDIA's
"MIG User Guide" and "AI Infrastructure and OperationsFundamentals" emphasize MIG's role in optimizing GPU efficiency in data centers with variable workloads.
Round-robin scheduling (A) lacks resource awareness, leading to inefficiency. Manual scheduling (C) is impractical for dynamic workloads. Upgrading GPUs (D) increases capacity but doesn't address allocation efficiency. MIG is NVIDIA's recommended solution for this scenario.

NEW QUESTION # 145
In your multi-tenant AI cluster, multiple workloads are running concurrently, leading to some jobs experiencing performance degradation. Which GPU monitoring metric is most critical for identifying resource contention between jobs?

A. GPU Temperature
B. Network Latency
C. GPU Utilization Across Jobs
D. Memory Bandwidth Utilization

Answer: C

Explanation:
GPU Utilization Across Jobs is the most critical metric for identifying resource contention in a multi-tenant cluster. It shows how GPU resources are divided among workloads, revealing overuse or starvation via tools like nvidia-smi. Option B (temperature) indicates thermal issues, not contention. Option C (network latency) affects distributed tasks. Option D (memory bandwidth) is secondary. NVIDIA's DCGM supports this metric for contention analysis.

NEW QUESTION # 146
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