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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q85-Q90):

NEW QUESTION # 85
A retail company is considering using AI to enhance its operations. They want to improve customer experience, optimize inventory management, and personalize marketing campaigns. Which AI use case would be most impactful in achieving these goals?

A. Image recognition for automatic labeling of products in warehouses
B. Natural language processing for automated customer support chatbots
C. AI-powered recommendation systems, which personalize product suggestions for customers based on their behavior
D. AI-driven fraud detection to prevent unauthorized transactions

Answer: C

Explanation:
AI-powered recommendation systems are the most impactful use case for improving customer experience, optimizing inventory, and personalizing marketing in retail. These systems, accelerated by NVIDIA GPUs and deployed via Triton Inference Server, analyze customer behavior to deliver tailored suggestions, driving sales, reducing overstock, and enhancing campaigns. NVIDIA's "State of AI in Retail and CPG" report highlights recommendation systems as a top retail AI application.
NLP chatbots (B) improve support but don't address inventory or marketing directly. Fraud detection (C) is security-focused, not operational. Image recognition (D) aids warehousing but lacks broad impact. NVIDIA prioritizes recommendations for retail goals.

NEW QUESTION # 86
You are managing a high-performance AI cluster where multiple deep learning jobs are scheduled to run concurrently. To maximize resource efficiency, which of the following strategies should youuse to allocate GPU resources across the cluster?

A. Assign jobs to GPUs based on their geographic proximity to reduce data transfer times.
B. Allocate all GPUs to the largest job to ensure its rapid completion, then proceed with smaller jobs.
C. Allocate GPUs to jobs based on their compute intensity, reserving the most powerful GPUs for the most demanding tasks.
D. Use a priority queue to assign GPUs to jobs based on their deadline, ensuring the most time-sensitive jobs complete first.

Answer: C

Explanation:
Maximizing resource efficiency in a high-performance AI cluster requires matching GPU capabilities to job requirements. Allocating GPUs based on compute intensity ensures that resource-intensive tasks (e.g., large models or datasets) run on high-performance GPUs (e.g., NVIDIA A100 or H100), while lighter tasks use less powerful ones (e.g., V100). NVIDIA's Multi-Instance GPU (MIG) and GPU Operator in Kubernetes support this strategy by allowing dynamic partitioning and allocation, optimizing utilization and throughput across the cluster.
A priority queue (Option A) focuses on deadlines but may underutilize GPUs if low-priority jobs are resource- heavy. Allocating all GPUs to one job (Option B) wastes resources when smaller jobs could run concurrently.
Geographic proximity (Option D) reduces latency in distributed setups but doesn't address compute efficiency within a cluster. NVIDIA's emphasis on workload-aware scheduling in DGX and cloud environments supports Option C as the best approach.

NEW QUESTION # 87
You are assisting a senior data scientist in a project aimed at improving the efficiency of a deep learning model. The team is analyzing how different data preprocessing techniques impact the model's accuracy and training time. Your task is to identify which preprocessing techniques have the most significant effect on these metrics. Which method would be most effective in identifying the preprocessing techniques that significantly affect model accuracy and training time?

A. Perform a multivariate regression analysis with preprocessing techniques as independent variables and accuracy/training time as dependent variables.
B. Use a line chart to plot training time for different preprocessing techniques.
C. Conduct a t-test between different preprocessing techniques.
D. Create a pie chart showing the distribution of preprocessing techniques used.

Answer: A

Explanation:
Performing a multivariate regression analysis with preprocessing techniques as independent variables and accuracy/training time as dependent variables is the most effective method. This statistical approach quantifies the impact of each technique (e.g., normalization, augmentation) on both metrics, identifying significant contributors while accounting for interactions. NVIDIA's Deep Learning Performance Guide suggests such analyses for optimizing training pipelines on GPUs. Option A (line chart) visualizes trends but lacks statistical rigor. Option B (t-test) compares pairs, not multiple factors. Option D (pie chart) shows usage distribution, not impact. Regression aligns with NVIDIA's data-driven optimization strategies.

NEW QUESTION # 88
Your team is deploying an AI model that involves a real-time recommendation system for a high-traffic e- commerce platform. The model must analyze user behavior and suggest products instantly as the user interacts with the platform. Which type of AI workload best describes this use case?

A. Streaming analytics
B. Reinforcement learning
C. Batch processing
D. Offline training

Answer: A

Explanation:
Streaming analytics best describes the workload for a real-time recommendation system on a high-traffic e- commerce platform. This workload involves continuous processing of incoming data (user behavior) to deliver instant product suggestions, requiring low-latency inference on NVIDIA GPUs, often with tools like NVIDIA TensorRT or Triton Inference Server. Option A (batch processing) handles data in fixed chunks, unsuitable for real-time needs. Option B (reinforcement learning) focuses on decision-making through trial and error, not immediate recommendations. Option D (offline training) is for model development, not deployment. NVIDIA's AI infrastructure documentation emphasizes streaming analytics for real-time applications like e-commerce personalization.

NEW QUESTION # 89
Your AI development team is working on a project that involves processing large datasets and training multiple deep learning models. These models need to be optimized for deployment on different hardware platforms, including GPUs, CPUs, and edge devices. Which NVIDIA software component would best facilitate the optimization and deployment of these models across different platforms?

A. NVIDIA Triton Inference Server
B. NVIDIA RAPIDS
C. NVIDIA TensorRT
D. NVIDIA DIGITS

Answer: C

Explanation:
NVIDIA TensorRT is a high-performance deep learning inference library designed to optimize and deploy models across diverse hardware platforms, including NVIDIA GPUs, CPUs (via TensorRT's CPU fallback), and edge devices (e.g., Jetson). It supports model optimization techniques like layer fusion, precision calibration (e.g., FP32 to INT8), and dynamic tensor memory management, ensuring efficient execution tailored to each platform's capabilities. This makes it ideal for the team's need to process large datasets and deploy models universally, a key component in NVIDIA's inference ecosystem (e.g., DGX, Jetson, cloud deployments).
DIGITS (Option B) is a training tool, not focused on deployment optimization. Triton Inference Server (Option C) manages inference serving but doesn't optimize models for diverse hardware like TensorRT does.
RAPIDS (Option D) accelerates data science workflows, not model deployment. TensorRT's cross-platform optimization is the best fit, per NVIDIA's inference strategy.

NEW QUESTION # 90
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Exam NCA-AIIO Learning & Latest Test NCA-AIIO Simulations

The best NCA-AIIO Practice Exam Preparation Materials are high pass rate - ITPassLeader

NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q85-Q90):

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