Question 1

You are tasked with managing an AI training environment where multiple deep learning models are being trained simultaneously on a shared GPU cluster. Some models require more GPU resources and longer training times than others. Which orchestration strategy would best ensure that all models are trained efficiently without causing delays for high-priority workloads?

Options :

A : Implement a priority-based scheduling system that allocates more GPUs to high-priority models while throttling lower-priority ones.

B : Assign equal GPU resources to all models regardless of their requirements.

C : Use a first-come, first-served (FCFS) scheduling policy for all models.

D : Randomly assign GPU resources to each model training job.

Answer: A

Question 2

You have completed an analysis of resource utilization during the training of a deep learning model on an NVIDIA GPU cluster. The senior engineer requests that you create a visualization that clearly conveys the relationship between GPU memory usage and model training time across different training sessions. Which visualization would be most effective in conveying the relationship between GPU memory usage and model training time?

Options :

A : Bar chart showing average memory usage for each training session

B : Histogram of training times

C : Line chart showing training time over sessions

D : Scatter plot with GPU memory usage on one axis and training time on the other

Answer: D

Question 3

During an AI project, a data scientist uses a combination of data mining and data visualization techniques to extract insights from a large dataset containing millions of transaction records. The scientist notices that the data is highly imbalanced, with only a small fraction of transactions being fraudulent. Which approach would be most effective in ensuring that the insights extracted are meaningful and accurate?

Options :

A : Ignore the minority class and focus analysis on the majority class since it represents the bulk of the data.

B : Use oversampling techniques on the minority class to balance the dataset before applying data mining techniques.

C : Apply principal component analysis (PCA) to reduce the dimensionality of the dataset, focusing only on the majority class.

D : Visualize the dataset using scatter plots and histograms without addressing the class imbalance.

Answer: B

Question 4

You have deployed an AI training job on a GPU cluster, but the training time has not decreased as expected after adding more GPUs. Upon further investigation, you observe that the GPU utilization is low, and the CPU utilization is very high. What is the most likely cause of this issue?

Options :

A : The AI model is not compatible with multi-GPU training.

B : The GPUs are not properly connected in the cluster.

C : Incorrect software version installed on the GPUs.

D :

The data preprocessing is being bottlenecked by the CPU.

Answer: D

Question 5

Which of the following factors has most significantly contributed to the recent rapid improvements and widespread adoption of AI?

Options :

A : The invention of new AI programming languages.

B : The rise of social media, increasing the need for AI.

C : Increased computational power, especially with the advent of modern GPUs and specialized AI hardware.

D : The global standardization of AI ethics guidelines.

Answer: C