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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q37-Q42):

NEW QUESTION # 37
In your AI data center, you've observed that some GPUs are underutilized while others are frequently maxed out, leading to uneven performance across workloads. Which monitoring tool or technique would be most effective in identifying and resolving these GPU utilization imbalances?

A. Set Up Alerts for Disk I/O Performance Issues
B. Perform Manual Daily Checks of GPU Temperatures
C. Monitor CPU Utilization Using Standard System Monitoring Tools
D. Use NVIDIA DCGM to Monitor and Report GPU Utilization

Answer: D

Explanation:
Identifying and resolving GPU utilization imbalances requires detailed, real-time monitoring. NVIDIA DCGM (Data Center GPU Manager) tracks GPU Utilization Percentage across a cluster (e.g., DGX systems), pinpointing underutilized and overloaded GPUs. It provides actionable data to adjust workload distribution, optimizing performance via integration with schedulers like Kubernetes.
Disk I/O alerts (Option A) address storage, not GPU use. Manual temperature checks (Option B) are unscalable and unrelated to utilization. CPU monitoring (Option C) misses GPU-specific issues. DCGM is NVIDIA's go-to tool for this task.

NEW QUESTION # 38
You are responsible for managing an AI infrastructure that runs a critical deep learning application. The application experiences intermittent performance drops, especially when processing large datasets. Upon investigation, you find that some of the GPUs are not being fully utilized while others are overloaded, causing the overall system to underperform. What would be the most effective solution to address the uneven GPU utilization and optimize the performance of the deep learning application?

A. Increase the clock speed of the GPUs.
B. Add more GPUs to the system.
C. Reduce the size of the datasets being processed.
D. Implement dynamic load balancing for the GPUs.

Answer: D

Explanation:
Intermittent performance drops due to uneven GPU utilization stem from workload imbalance. Dynamic load balancing, enabled by NVIDIA tools like Triton Inference Server or Kubernetes with GPU Operator, redistributes tasks based on GPU utilization, ensuring even processing of large datasets. This optimizes performance in DGX or multi-GPU setups by preventing overload and underuse, directly addressing the root cause.
Reducing dataset size (Option A) compromises model quality and doesn't fix distribution. Increasing clock speed (Option B) may help overloaded GPUs but not underutilized ones. Adding GPUs (Option C) increases capacity but not balance. NVIDIA's infrastructure solutions favor dynamic balancing for critical applications.

NEW QUESTION # 39
A financial services company is using an AI model for fraud detection, deployed on NVIDIA GPUs. After deployment, the company notices a significant delay in processing transactions, which impacts their operations. Upon investigation, it's discovered that the AI model is being heavily used during peak business hours, leading to resource contention on the GPUs. What is the best approach to address this issue?

A. Disable GPU monitoring to free up resources
B. Implement GPU load balancing across multiple instances
C. Increase the batch size of input data for the AI model
D. Switch to using CPU resources instead of GPUs for processing

Answer: B

Explanation:
Implementing GPU load balancing across multiple instances is the best approach to address resource contention and delays in a fraud detection system during peak hours. Load balancing distributes inference workloads across multiple NVIDIA GPUs (e.g., in a DGX cluster or Kubernetes setup with Triton Inference Server), ensuring no single GPU is overwhelmed. This maintains low latency and high throughput, as recommended in NVIDIA's "AI Infrastructure and Operations Fundamentals" and "Triton Inference Server Documentation" for production environments.
Switching to CPUs (A) sacrifices GPU performance advantages. Disabling monitoring (B) doesn't address contention and hinders diagnostics. Increasing batch size (C) may worsen delays by overloading GPUs. Load balancing is NVIDIA's standard solution for peak load management.

NEW QUESTION # 40
Your AI-driven data center experiences occasional GPU failures, leading to significant downtime for critical AI applications. To prevent future issues, you decide to implement a comprehensive GPU health monitoring system. You need to determine which metrics are essential for predicting and preventing GPU failures. Which of the following metrics should be prioritized to predict potential GPU failures and maintain GPU health?

A. GPU Clock Speed
B. Error Rates (e.g., ECC errors)
C. CPU Utilization
D. GPU Temperature

Answer: B

Explanation:
Predicting GPU failures requires monitoring metrics that signal hardware degradation or faults. Error Rates, such as ECC (Error-Correcting Code) errors, are critical because they indicate memory corruption or hardware issues in NVIDIA GPUs (e.g., A100, H100). ECC errors, tracked via NVIDIA DCGM (Data Center GPU Manager) or nvidia-smi, can predict impending failures if they increase over time, allowing proactive maintenance to prevent downtime in AI data centers like DGX deployments.
GPU Clock Speed (Option A) reflects performance but not health. GPU Temperature (Option B) is important for thermal management but less predictive of failure unless extreme. CPU Utilization (Option C) is unrelated to GPU health. NVIDIA's focus on reliability in enterprise settings prioritizes Error Rates for failure prediction.

NEW QUESTION # 41
Which of the following is a primary challenge when integrating AI into existing IT infrastructure?

A. Scalability of the AI workloads
B. Ensuring AI models have a user-friendly interface
C. Selecting the right cloud service provider
D. Finding AI tools that are compatible with existing hardware

Answer: A

Explanation:
Scalability of AI workloads is a primary challenge when integrating AI into existing IT infrastructure. AI tasks, especially training and inference on NVIDIA GPUs, demand significant compute, memory, and networking resources, which legacy systems may not handle efficiently. Scaling these workloads across clusters or hybrid environments requires careful planning, as noted in NVIDIA's "AI Infrastructure and Operations Fundamentals" and "AI Adoption Guide." User-friendly interfaces (A) are secondary to technical integration. Hardware compatibility (C) is less challenging with NVIDIA's broad support. Cloud provider selection (D) is a decision, not a core challenge.
NVIDIA identifies scalability as a key integration hurdle.

NEW QUESTION # 42
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