Quiz about Certification Exam, NVIDIA, NVIDIA-Certified Associate: AI Infrastructure and Operations (NCA-AIIO)

Question 1

An enterprise is deploying a large-scale AI model for real-time image recognition. They face challenges with scalability and need to ensure high availability while minimizing latency. Which combination of
NVIDIA technologies would best address these needs?

Accepted Answer

NVIDIA TensorRT and NVLink

Answer

NVIDIA CUDA and NCCL

Answer

NVIDIA Triton Inference Server and GPUDirect RDMA

Answer

NVIDIA DeepStream and NGC Container Registry

Question 2

A company is using a multi-GPU server for training a deep learning model. The training process is extremely slow, and after investigation, it is found that the GPUs are not being utilized efficiently. The
system uses NVLink, and the software stack includes CUDA, cuDNN, and NCCL. Which of the following
actions is most likely to improve GPU utilization and overall training performance?

Accepted Answer

Increase the batch size

Answer

Update the CUDA version to the latest release

Answer

Disable NVLink and use PCIe for inter-GPU communication

Answer

Optimize the model's code to use mixed-precision training

Question 3

In an AI data center, you are responsible for monitoring the performance of a GPU cluster used for large- scale model training. Which of the following monitoring strategies would best help you identify and
address performance bottlenecks?

Accepted Answer

Track CPU, GPU, and network utilization simultaneously to identify any resource imbalances that could lead to bottlenecks.

Answer

Monitor only the GPU utilization metrics to ensure that all GPUs are being used at full capacity.

Answer

Focus on job completion times to ensure that the most critical jobs are being finished on schedule.

Answer

Use predictive analytics to forecast future GPU utilization, adjusting resources before bottlenecks occur.

Question 4

You are assisting a senior data scientist in analyzing a large dataset of customer transactions to identify potential fraud. The dataset contains several hundred features, but the senior team member advises you
to focus on feature selection before applying any machine learning models. Which approach should you
take under their supervision to ensure that only the most relevant features are used?

Accepted Answer

Use correlation analysis to identify and remove features that are highly correlated with each other.

Answer

Select features randomly to reduce the number of features while maintaining diversity.

Answer

Ignore the feature selection step and use all features in the initial model.

Answer

Use Principal Component Analysis (PCA) to reduce the dataset to a single feature.

Question 5

You are evaluating the performance of two AI models on a classification task. Model A has an accuracy of 85%, while Model B has an accuracy of 88%. However, Model A's F1 score is 0.90, and Model B's F1
score is 0.88. Which model would you choose based on the F1 score, and why?

Accepted Answer

Model A - The F1 score is higher, indicating better balance between precision and recall.

Answer

Model B - The higher accuracy indicates overall better performance.

Answer

Neither - The choice depends entirely on the specific use case.

Answer

Model B - The F1 score is lower but accuracy is more reliable.

Question 6

Which NVIDIA hardware and software combination is best suited for training large-scale deep learning models in a data center environment?

Accepted Answer

NVIDIA A100 Tensor Core GPUs with PyTorch and CUDA for model training.

Answer

NVIDIA Jetson Nano with TensorRT for training.

Answer

NVIDIA DGX Station with CUDA toolkit for model deployment.

Answer

NVIDIA Quadro GPUs with RAPIDS for real-time analytics.

Question 7

A healthcare company is looking to adopt AI for early diagnosis of diseases through medical imaging. They need to understand why AI has become so effective recently. Which factor should they consider as
most impactful in enabling AI to perform complex tasks like image recognition at scale?

Accepted Answer

Advances in GPU technology, enabling faster processing of large datasets required for AI tasks.

Answer

Development of new programming languages specifically for AI.

Answer

Increased availability of medical imaging data, allowing for better machine learning model training.

Answer

Reduction in data storage costs, allowing for more data to be collected and stored.

Question 8

Which of the following networking features is MOST critical when designing an AI environment to handle large-scale deep learning model training?

Accepted Answer

High network throughput with low latency between compute nodes.

Answer

Enabling network redundancy to prevent single points of failure.

Answer

Implementing network segmentation to isolate different parts of the AI environment.

Answer

Using Wi-Fi for flexibility in connecting compute nodes.

Question 9

Your AI data center is running multiple high-performance GPU workloads, and you notice that certain servers are being underutilized while others are consistently at full capacity, leading to inefficiencies.
Which of the following strategies would be most effective in balancing the workload across your AI data
center?

Accepted Answer

Implement NVIDIA GPU Operator with Kubernetes for Automatic Resource Scheduling

Answer

Use Horizontal Scaling to Add More Servers

Answer

Manually Reassign Workloads Based on Current Utilization

Answer

Increase Cooling Capacity in the Data Center

Question 10

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?

Accepted Answer

Continuously retrain the model using a streaming data pipeline

Answer

Run the model in parallel with rule-based systems to ensure redundancy

Answer

Deploy the model once and retrain it only when accuracy drops significantly

Answer

Use a static dataset to retrain the model periodically

Quiz NVIDIA-Certified Associate: AI Infrastructure and Operations (NCA-AIIO)

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