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NEW QUESTION # 119
Why do attention-based models (Transformers) scale better than RNNs for long sequences?
Answer: D
Explanation:
Attention allows parallel processing of sequence elements, avoiding sequential bottlenecks in RNNs.
NEW QUESTION # 120
Which of the following is a best practice for addressing model drift in AI operations?
Answer: C
Explanation:
The correct answer is B because model drift is an operational issue where production model performance changes as data, user behavior, or business conditions change. NVIDIA's recommender systems best- practices documentation states that production modules should be continuously monitored: "Modules are continuously monitored so that the quality of the recommendation can be measured in real time through a range of KPIs." It further explains that these modules "trigger full retraining should model drift occur, such as when certain KPIs fall below known established baselines." NVIDIA's TAO Toolkit guidance also supports retraining as the correct response to drift: "To avoid model drift or to accommodate changing business requirements, retrain your model regularly." Why the other options are incorrect: Increasing hardware resources may improve throughput or latency, but it does not fix degraded model accuracy caused by drift. Permitting input distributions to change without controls is a cause of drift, not a mitigation. Assuming a model will generalize to any data is not a valid AI operations practice. The verified best practice is to monitor deployed models and retrain or update them with fresh, representative data.
Reference: NVIDIA Best Practices for Building and Deploying Recommender Systems; NVIDIA TAO Toolkit guidance on model drift and retraining.
NEW QUESTION # 121
Why do convolutional neural networks outperform fully connected networks in vision tasks?
Answer: D
Explanation:
CNNs leverage spatial locality and shared weights, significantly reducing parameters and improving generalization on image data.
NEW QUESTION # 122
When designing a data center specifically for AI workloads, which of the following factors is most critical to optimize for training large-scale neural networks?
Answer: C
Explanation:
High-speed, low-latency networking between compute nodes is the most critical factor to optimize when designing a data center for training large-scale neural networks. AI workloads, especially distributed training on NVIDIA GPUs (e.g., DGX systems), require rapid communication between nodes to exchange gradients, weights, and other data. Technologies like NVIDIA NVLink (intra-node) and InfiniBand or RDMA (inter- node) minimize communication overhead, ensuringscalability and reduced training time. NVIDIA's "DGX SuperPOD Reference Architecture" highlights that networking performance is a bottleneck in large-scale AI training, making it more critical than storage or CPU capacity.
Maximizing storage arrays (A) is important for data availability but less critical than networking for training performance. CPU cores (B) play a secondary role to GPUs in AI training. Virtualization (D) enhances flexibility but is not the primary optimization focus for training throughput. NVIDIA's AI infrastructure guidelines prioritize networking for such workloads.
NEW QUESTION # 123
You are leading a project to implement a real-time fraud detection system for a financial institution. The system needs to analyze transactions in real-time using a deep learning model that has been trained on large datasets. The inference workload must be highly scalable and capable of processing thousands of transactions per second with minimal latency. Your deployment environment includes NVIDIA A100 GPUs in a Kubernetes-managed cluster. Which approach would be most suitable to deploy and manage your deep learning inference workload?
Answer: C
Explanation:
NVIDIA Triton Inference Server with Kubernetes is the most suitable approach for deploying and managing a real-time fraud detection system on NVIDIA A100 GPUs. Triton provides a scalable, low-latency inference platform with features like dynamic batching and model management, ideal for processing thousands of transactions per second. Integration with Kubernetes (via NVIDIA GPU Operator) ensures high availability, scalability, and orchestration in a cluster, as outlined in NVIDIA's "Triton Inference Server Documentation" and "DeepOps" resources. This meets the financial institution's needs for real-time, high-throughput inference.
TensorRT standalone (A) optimizes models but lacks deployment scalability. Kafka with GPUs (C) is a messaging system, not an inference solution. CUDA with Docker (D) is a development tool, not a production deployment platform. Triton with Kubernetes is NVIDIA's recommended approach.
NEW QUESTION # 124
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