Practice Questions
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In the context of transformer architecture, what is the main purpose of fine-tuning a pre-trained model for a specific business application?
Fine‑tuning lets the model learn patterns that are unique to the business data. Other options are incorrect because The goal is not to shrink the mode...
In the context of Transformer architecture used in business applications, how does the encoder-decoder structure utilize positional encoding to enhance data processing?
Both the encoder and decoder add positional encoding to every token. Other options are incorrect because The idea that only the encoder needs position...
In the context of Transformer architecture, how does self-attention enhance the process of transfer learning?
Self‑attention lets each token in the input see every other token and decide how much it should listen to each one. Other options are incorrect becaus...
How does the concept of Multi-Head Attention in Transformer Architecture enhance the capabilities of Deep Learning Models in the context of Transfer Learning?
Multi‑head attention lets the transformer look at several pieces of the input at the same time. Other options are incorrect because Some think the mod...
How can transfer learning in transformer architecture improve sequence-to-sequence learning, and what ethical considerations should businesses keep in mind when implementing these AI technologies?
Transfer learning (using a model trained on a large dataset to start a new task) begins with a model that already knows patterns. Other options are in...
What is the primary reason that the Transformer architecture has revolutionized natural language processing compared to earlier models?
Transformers use an attention mechanism that lets every word look at all others at the same time. Other options are incorrect because Some think Trans...
A team of developers is working on a new language translation application. They are debating whether to use traditional RNNs or the Transformer architecture for their model. Based on the principles of the Transformer architecture, which of the following reasons should they prioritize when making their decision?
Transformers use attention to see all words at once. Other options are incorrect because Some think RNNs are better for long text, but they often forg...
How does the Transformer architecture enhance parallelization compared to traditional RNNs?
Transformers use attention, a method that lets every word in a sentence talk to every other word at the same time. Other options are incorrect because...
Order the steps of how the Transformer architecture processes input data from initial encoding to final output generation.
First the raw words turn into numeric vectors in the input embedding stage. Other options are incorrect because This option puts attention before the ...
What distinguishes the Transformer architecture from previous models in handling sequential data?
Transformers use attention, a method that looks at all parts of the input at once. Other options are incorrect because Some think Transformers need ma...
Attention:Encoder :: Decoder:?
The decoder receives the context produced by the encoder. Other options are incorrect because Attention is a method, not the decoder’s purpose; Contex...
Which of the following statements correctly describe the advantages of the Transformer architecture? Select all that apply.
Transformers replace recurrent layers with self‑attention, so many parts of the input can be processed together. Other options are incorrect because S...
Which of the following statements best categorizes the advantages of the Transformer architecture compared to traditional RNNs in natural language processing tasks?
Transformers can look at all words at the same time. Other options are incorrect because It sounds like Transformers use the same stepping‑by‑stepping...
In the Transformer architecture, the primary mechanism that connects the encoder and decoder is called ____. This mechanism allows for parallelization and has improved the efficiency of training models compared to traditional methods.
Attention lets the model examine all parts of the input at the same time. Other options are incorrect because Convolution slides a small filter across...
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