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Question No 1

[Fundamentals of Machine Learning and Neural Networks] Why do we need positional encoding in transformer - based models?

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Question No 2

[LLM Integration and Deployment] What is Retrieval Augmented Generation (RAG)?

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RAG is an architecture used to optimize the output of an LLM by retraining the model with domain - specific data.

RAG is a methodology that combines an information retrieval component with a response generator.

RAG is a method for manipulating and generating text - based data using Transformer - based LLMs.

RAG is a technique used to fine - tune pre - trained LLMs for improved performance.

Question No 3

[Experimentation] In the context of fine - tuning LLMs, which of the following metrics is most commonly used to assess the performance of a fine - tuned model?

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Question No 4

[Fundamentals of Machine Learning and Neural Networks] Which of the following claims is correct about quantization in the context of Deep Learning? (Pick the 2 correct responses)

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Quantization might help in saving power and reducing heat production.

It consists of removing a quantity of weights whose values are zero.

It leads to a substantial loss of model accuracy.

Helps reduce memory requirements and achieve better cache utilization.

It only involves reducing the number of bits of the parameters.

Question No 5

[Data Preprocessing and Feature Engineering] What is the primary purpose of applying various image transformation techniques (e.g., flipping, rotation, zooming) to a dataset?

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To simplify the model's architecture, making it easier to interpret the results.

To artificially expand the dataset's size and improve the model's ability to generalize.

To ensure perfect alignment and uniformity across all images in the dataset.

To reduce the computational resources required for training deep learning models.

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