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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions:
1. You are managing an AI data center platform that runs a mix of compute-intensive training jobs and low- latency inference tasks. Recently, the system has been experiencing unexpected slowdowns during inference tasks, even though there are sufficient GPU resources available. What is the most likely cause of this issue, and how can it be resolved?
A) The GPUs are overheating, leading to thermal throttling during inference.
B) The training jobs are consuming too much network bandwidth, leaving insufficient bandwidth for inference data transfer.
C) The inference jobs are running at the same priority level as the training jobs, causing contention for resources.
D) The inference tasks are not optimized for the GPU architecture, leading to inefficient use of resources.
2. What is a key consideration when virtualizing accelerated infrastructure to support AI workloads on a hypervisor-based environment?
A) Maximize the number of VMs per physical server
B) Ensure GPU passthrough is configured correctly
C) Enable vCPU pinning to specific cores
D) Disable GPU overcommitment in the hypervisor
3. You are managing an AI data center where energy consumption has become a critical concern due to rising costs and sustainability goals. The data center supports various AI workloads, including model training, inference, and data preprocessing. Which strategy would most effectively reduce energy consumption without significantly impacting performance?
A) Implement dynamic voltage and frequency scaling (DVFS) to adjust GPU power usage based on workload demands.
B) Consolidate all AI workloads onto a single GPU to reduce overall power usage.
C) Schedule all AI workloads during nighttime to take advantage of lower electricity rates.
D) Reduce the clock speed of all GPUs to lower power consumption.
4. You are supporting a senior engineer in troubleshooting an AI workload that involves real-time data processing on an NVIDIA GPU cluster. The system experiences occasional slowdowns during data ingestion, affecting the overall performance of the AI model. Which approach would be most effective in diagnosing the cause of the data ingestion slowdown?
A) Optimize the AI model's inference code
B) Profile the I/O operations on the storage system
C) Switch to a different data preprocessing framework
D) Increase the number of GPUs used for data processing
5. You are tasked with deploying a machine learning model into a production environment for real-time fraud detection in financial transactions. The model needs to continuously learn from new data and adapt to emerging patterns of fraudulent behavior. Which of the following approaches should you implement to ensure the model's accuracy and relevance over time?
A) Run the model in parallel with rule-based systems to ensure redundancy
B) Continuously retrain the model using a streaming data pipeline
C) Use a static dataset to retrain the model periodically
D) Deploy the model once and retrain it only when accuracy drops significantly
Solutions:
Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: A | Question # 4 Answer: B | Question # 5 Answer: B |