Question 1

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?

Options :

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

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

C : Continuously retrain the model using a streaming data pipeline

D : Use a static dataset to retrain the model periodically

Answer: C

Question 2

In an AI environment, the NVIDIA software stack plays a crucial role in ensuring seamless operations across different stages of the AI workflow. Which components of the NVIDIA software stack would you use to accelerate AI model training and deployment? (Select two)

Options :

A : NVIDIA cuDNN (CUDA Deep Neural Network library)

B : NVIDIA TensorRT

C : NVIDIA DGX-1

D : NVIDIA Nsight

E : NVIDIA DeepStream SDK

Answer: A,B

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 of the following statements best describes the difference between infrastructure requirements for training and inference in AI architectures?

Options :

A : Training typically runs on edge devices, while inference is run in centralized data centers

B : Inference always requires more GPUs than training

C : Training requires more computational resources and memory bandwidth compared to inference

D : Inference requires distributed computing clusters, while training can be done on a single server

Answer: C

Question 5

You are working under the supervision of a senior data scientist to analyze the results of a large-scale machine learning experiment. The experiment produced a vast amount of data, and your task is to create visualizations that effectively convey the key findings to stakeholders. The stakeholders are particularly interested in understanding the model's performance across different categories. Which type of visualization would be most appropriate for comparing the model's performance across multiple categories?

Options :

A : Line chart showing performance over time.

B : Scatter plot showing relationships between two variables.

C : Bar chart with error bars for each category.

D : Pie chart showing the distribution of categories.

Answer: C

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