1z0-1127-25 Free Practice Test

How does a presence penalty function in language model generation?

1. A. It penalizes all tokens equally, regardless of how often they have appeared.
2. B. It penalizes only tokens that have never appeared in the text before.
3. C. It applies a penalty only if the token has appeared more than twice.
4. D. It penalizes a token each time it appears after the first occurrence.

Correct Answer: D

Given the following code:
PromptTemplate(input_variables=["human_input", "city"], template=template) Which statement is true about PromptTemplate in relation to input_variables?

1. A. PromptTemplate requires a minimum of two variables to function properly.
2. B. PromptTemplate can support only a single variable at a time.
3. C. PromptTemplate supports any number of variables, including the possibility of having none.
4. D. PromptTemplate is unable to use any variables.

Correct Answer: C

Which is a cost-related benefit of using vector databases with Large Language Models
(LLMs)?

1. A. They require frequent manual updates, which increase operational costs.
2. B. They offer real-time updated knowledge bases and are cheaper than fine-tuned LLMs.
3. C. They increase the cost due to the need for real-time updates.
4. D. They are more expensive but provide higher quality data.

Correct Answer: B

In which scenario is soft prompting especially appropriate compared to other training styles?

1. A. When there is a significant amount of labeled, task-specific data available.
2. B. When the model needs to be adapted to perform well in a different domain it was not originally trained on.
3. C. When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training.
4. D. When the model requires continued pre-training on unlabeled data.

Correct Answer: C

What is the 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 selectively updates only a fraction of weights to reduce the number of parameters.
3. C. It selectively updates only a fraction of weights to reduce computational load and avoid overfitting.
4. D. It increases the training time as compared to Vanilla fine-tuning.

Correct Answer: C