Positional encoding is a vital component of the Transformer architecture, as emphasized in NVIDIA’s Generative AI and LLMs course. Transformers lack the inherent sequential processing of recurrent neural networks, so they rely on positional encoding to incorporate information about the order of tokens in the input sequence. This is typically achieved by adding fixed or learned vectors (e.g., sine and cosine functions) to the token embeddings, where each position in the sequence has a unique encoding. This allows the model to distinguish the relative or absolute positions of tokens, enabling it to understand word order in tasks like translation or text generation. For example, in the sentence "The cat sleeps," positional encoding ensures the model knows "cat" is the second token and "sleeps" is the third. Option A is incorrect, as positional encoding does not remove information but adds positional context. Option B is wrong because semantic meaning is captured by token embeddings, not positional encoding. Option D is also inaccurate, as the importance of tokens is determined by the attention mechanism, not positional encoding. The course notes: "Positional encodings are used in Transformers to provide information about the order of tokens in the input sequence, enabling the model to process sequences effectively."