Question 1

Which statement best describes the role of encoder and decoder models in natural language processing?

Options :

A : Encoder models and decoder models both convert sequences of words into vector representations without generating new text.

B : Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.

C : Encoder models convert a sequence of words into a vector representation, and decoder models take this vector representation to generate a sequence of words.

D : Encoder models take a sequence of words and predict the next word in the sequence, whereas decoder models convert a sequence of words into a numerical representation.

Answer: C

Question 2

Which of the following statements is NOT a characteristic of vector databases used in LLM applications?

Options :

A : Efficient storage of high-dimensional data representing text or code.

B : Ability to perform fast similarity searches based on semantic relationships.

C : Use of SQL for querying and retrieving data.

D : Scalability to handle large volumes of textual or code-based information.

Answer: C

Question 3

In the context of fine-tuning with OCI Generative AI Service, what is the role of a custom dataset?

Options :

A : It defines the new tasks the model should be able to perform.

B : It provides additional training data specific to your domain.

C : It acts as a validation set to evaluate the fine-tuned model-s performance.

D : It replaces the pre-trained weights of the base model entirely.

Answer: B

Question 4

Which of the following statements accurately describes the relationship between chain depth and complexity in LangChain models?

Options :

A : A deeper chain always leads to more complex and nuanced outputs.

B : Increasing chain depth can inadvertently introduce errors through output propagation.

C : A shallow chain is ideal for tasks requiring high levels of creative exploration.

D : The optimal chain depth depends on the specific task and desired output complexity.

Answer: D

Question 5

: During LLM fine-tuning, what part of the model typically undergoes the most significant adjustments?

Options :

A : The input layer responsible for processing raw text data.

B : The final layers responsible for generating the desired output.

C : All layers of the LLM architecture are adjusted equally.

D : Only the pre-trained word embeddings are updated.

Answer: B

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