NVIDIA Generative AI LLMs - NCA-GENL무료 덤프문제 풀어보기

A. To provide a curated collection of GPU-optimized AI and data science software.

B. To provide a platform for testing and debugging software applications.

C. To provide a platform for developers to collaborate and share software development projects.

D. To provide a marketplace for buying and selling software development tools and resources.

A. Encoder model

B. Siamese model

C. SVM model

D. Auto-encoder model

A. A branch of artificial intelligence that focuses on auto generation of models for classification.

B. A branch of artificial intelligence that focuses on creating models that can generate new and original data.

C. A branch of artificial intelligence that focuses on improving the efficiency of existing models.

D. A branch of artificial intelligence that focuses on analyzing and interpreting existing data.

A. The probability of the next token by looking at the previous and future input tokens.

B. The probability of the next token using a Monte Carlo sampling of past tokens.

C. The next token solely using recurrent network or LSTM cells.

D. The probability of the next token in a text given the previous tokens.

A. To automate the collection of training data for fine-tuning.

B. To optimize the model's inference speed for production deployment.

C. To increase the model's parameter count for better performance.

D. To identify and mitigate potential biases, safety risks, and harmful outputs.

A. Q, K, and V are randomly initialized weight matrices used for positional encoding.

B. Q represents the query vector used to retrieve relevant information from the input sequence.

C. K is responsible for computing the attention scores between the query and key vectors.

D. V is used to calculate the positional embeddings for each token in the input sequence.

A. Accuracy

B. Recall

C. Precision

D. Perplexity

A. RAG is an architecture used to optimize the output of an LLM by retraining the model with domain- specific data.

B. RAG is a methodology that combines an information retrieval component with a response generator.

C. RAG is a method for manipulating and generating text-based data using Transformer-based LLMs.

D. RAG is a technique used to fine-tune pre-trained LLMs for improved performance.

A. Euclidean distance

B. Hamming distance

C. Jaccard similarity

D. Cosine similarity

A. Computing the frequency of individual words to identify the most common terms in a text.

B. Generating word clouds to visually represent word frequency and highlight key terms.

C. Creating n-gram models to analyze patterns of word sequences like bigrams and trigrams.

D. Applying sentiment analysis to gauge the overall sentiment expressed in a text.