Free NVIDIA NCA-GENL Exam Dumps Questions & Answers

In the context of language models, what does an autoregressive model predict?

• A.The next token solely using recurrent network or LSTM cells.
• B.The probability of the next token using a Monte Carlo sampling of past tokens.
• C.The probability of the next token in a text given the previous tokens.
• D.The probability of the next token by looking at the previous and future input tokens.

In Exploratory Data Analysis (EDA) for Natural Language Understanding (NLU), which method is essential for understanding the contextual relationship between words in textual data?

• A.Generating word clouds to visually represent word frequency and highlight key terms.
• B.Creating n-gram models to analyze patterns of word sequences like bigrams and trigrams.
• C.Applying sentiment analysis to gauge the overall sentiment expressed in a text.
• D.Computing the frequency of individual words to identify the most common terms in a text.

In the context of a natural language processing (NLP) application, which approach is most effective for implementing zero-shot learning to classify text data into categories that were not seen during training?

• A.Train the new model from scratch for each new category encountered.
• B.Use rule-based systems to manually define the characteristics of each category.
• C.Use a large, labeled dataset for each possible category.
• D.Use a pre-trained language model with semantic embeddings.

What distinguishes BLEU scores from ROUGE scores when evaluating natural language processing models?

• A.BLEU scores analyze syntactic structures, while ROUGE scores evaluate semantic accuracy.
• B.BLEU scores measure model efficiency, whereas ROUGE scores assess computational complexity.
• C.BLEU scores determine the fluency of text generation, while ROUGE scores rate the uniqueness of generated text.
• D.BLEU scores evaluate the 'precision' of translations, while ROUGE scores focus on the 'recall' of summarized text.

Which technique is used in prompt engineering to guide LLMs in generating more accurate and contextually appropriate responses?

• A.Increasing the model's parameter count.
• B.Leveraging the system message.
• C.Training the model with additional data.
• D.Choosing another model architecture.