Question 1

While monitoring your AI data center, you observe that one of your GPU clusters is experiencing frequent GPU memory errors. These errors are causing job failures and system instability. What is the most likely cause of these memory errors?

Options :

A : A bug in the deep learning model code.

B : Using outdated versions of GPU drivers.

C : Overheating of the GPUs due to insufficient cooling.

D : Insufficient power supply to the GPU nodes.

Answer: C

Question 2

Your organization is setting up an AI model deployment pipeline that requires frequent updates. The team needs to ensure minimal downtime during model updates, version control, and monitoring of the models in production. Which software component would be most suitable to handle these requirements?

Options :

A : NVIDIA NGC Catalog

B : NVIDIA TensorRT

C : NVIDIA Triton Inference Server

D : NVIDIA DIGITS

Answer: C

Question 3

You are working on a regression task to predict car prices. Model Gamma has a Mean Absolute Error (MAE) of $1,200, while Model Delta has a Mean Absolute Error (MAE) of $1,500. Which model should be preferred based on the Mean Absolute Error (MAE), and what does this metric indicate?

Options :

A : Model Gamma is worse because lower MAE can indicate overfitting.

B : Model Gamma is better because it has a lower MAE.

C : Neither model is better because MAE is not suitable for comparing regression models.

D : Model Delta is better because it has a higher MAE, which means it-s more flexible.

Answer: B

Question 4

Which two features of GPU architecture make it more suitable than CPU architecture for training large-scale AI models? (Select two)

Options :

A : CPUs have more versatile instruction sets, making them better suited for AI model training.

B : GPUs are optimized for floating-point operations, which are heavily used in AI model training.

C : GPUs have a higher core density, allowing them to perform many operations in parallel.

D : GPUs have higher clock speeds than CPUs, leading to faster processing of individual tasks.

E : GPUs consume less power per operation, making them more efficient for AI training tasks.

Answer: B,C

Question 5

You are tasked with creating a real-time dashboard for monitoring the performance of a large-scale AI system processing social media data. The dashboard should provide insights into trends, anomalies, and performance metrics using NVIDIA GPUs for data processing and visualization. Which tool or technique would most effectively leverage the GPU resources to visualize real-time insights from this high-volume social media data?

Options :

A : Relying solely on a relational database to handle the data and generate visualizations.

B : Implementing a GPU-accelerated deep learning model to generate insights and feeding results into a CPU-based visualization tool.

C : Using a standard CPU-based ETL (Extract, Transform, Load) process to prepare the data for visualization.

D : Employing a GPU-accelerated time-series database for real-time data ingestion and visualization.

Answer: D

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