1z0-1127-25 Free Practice Test

Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

1. A. It updates all the weights of the model uniformly.
2. B. It does not update any weights but restructures the model architecture.
3. C. It selectively updates only a fraction of the model??s weights.
4. D. It increases the training time as compared to Vanilla fine-tuning.

Correct Answer: C

What do embeddings in Large Language Models (LLMs) represent?

1. A. The color and size of the font in textual data
2. B. The frequency of each word or pixel in the data
3. C. The semantic content of data in high-dimensional vectors
4. D. The grammatical structure of sentences in the data

Correct Answer: C

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

1. A. Encoder models and decoder models both convert sequences of words into vector representations without generating new text.
2. B. 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.
3. 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.
4. D. Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.

Correct Answer: C

What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?

1. A. The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model
2. B. The percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation
3. C. The improvement in accuracy achieved by the model during training on the user- uploaded dataset
4. D. The level of incorrectness in the model??s predictions, with lower values indicating better performance

Correct Answer: D

Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?

1. A. Step-Back Prompting
2. B. Chain-of-Thought
3. C. Least-to-Most Prompting
4. D. In-Context Learning

Correct Answer: B