1z0-1127-25 Practice Questions

A.  

Because text generation does not require complex models

B.  

Because text is not categorical

C.  

Because text representation is categorical unlike images

D.  

Because diffusion models can only produce images

A.  

To increase the accuracy of the most likely word in the vocabulary

B.  

To determine the number of words to generate in a single decoding step

C.  

To decide to which part of speech the next word should belong

D.  

To adjust the sharpness of probability distribution over vocabulary when selecting the next word

A.  

Fine-tuning and continuous pretraining both modify all parameters and use labeled, task-specific data.

B.  

Parameter Efficient Fine-Tuning and Soft Prompting modify all parameters of the model using unlabeled data.

C.  

Fine-tuning modifies all parameters using labeled, task-specific data, whereas Parameter Efficient Fine-Tuning updates a few, new parameters also with labeled, task-specific data.

D.  

Soft Prompting and continuous pretraining are both methods that require no modification to the original parameters of the model.

A.  

Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance.

B.  

Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.

C.  

Dot Product measures the magnitude and direction of vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude.

D.  

Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.

A.  

Increasing the temperature removes the impact of the most likely word.

B.  

Decreasing the temperature broadens the distribution, making less likely words more probable.

C.  

Increasing the temperature flattens the distribution, allowing for more varied word choices.

D.  

Temperature has no effect on probability distribution; it only changes the speed of decoding.

A.  

To increase the complexity and size of text data

B.  

To translate text into a different language

C.  

To create numerical representations of text that capture the meaning and relationships between words or phrases

D.  

To compress text data into smaller files for storage

A.  

The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model

B.  

The percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation

C.  

The improvement in accuracy achieved by the model during training on the user-uploaded dataset

D.  

The level of incorrectness in the model’s predictions, with lower values indicating better performance

A.  

PromptTemplate requires a minimum of two variables to function properly.

B.  

PromptTemplate can support only a single variable at a time.

C.  

PromptTemplate supports any number of variables, including the possibility of having none.

D.  

PromptTemplate is unable to use any variables.

A.  

ConversationImageMemory

B.  

ConversationBufferMemory

C.  

ConversationSummaryMemory

D.  

ConversationTokenBufferMemory

A.  

Pretraining the model on a specific domain

B.  

Training the model using reinforcement learning

C.  

Conditioning the model with task-specific instructions or demonstrations

D.  

Adding more layers to the model