NCA-GENL Practice Test

NCA-GENL Practice Test practice questions, page 2 of 4, with answer review and explanations.

Question 6

What is a foundation model in the context of Large Language Models (LLMs)?

  1. A model that sets the state-of-the-art results for any of the tasks that compose the General Language Understanding Evaluation (GLUE) benchmark.
  2. Any model trained on vast quantities of data at scale whose goal is to serve as a starter that can be adapted to a variety of downstream tasks.
  3. Any model validated by the artificial intelligence safety institute as the foundation for building transformed-based applications.
  4. Any model based on the foundation paper “Attention is all you need”, that uses recurrent neural networks and convolution layers.

Question 7

Why is layer normalization important in transformer architectures?

  1. To enhance the model’s ability to generalize to new data.
  2. To compress the model size for efficient storage.
  3. To stabilize the learning process by adjusting the inputs across the features.
  4. To encode positional information within the sequence.

Question 8

Why do we need positional encoding in transformer-based models?

  1. To increase the throughput of the model.
  2. To represent the order of elements in a sequence.
  3. To prevent overfitting of the model.
  4. To reduce the dimensionality of the input data.

Question 9

What do we usually refer to as generative AI?

  1. A branch of artificial intelligence that focuses on creating models that can generate new and original data.
  2. A branch of artificial intelligence that focuses on auto generation of models for classification.
  3. A branch of artificial intelligence that focuses on improving the efficiency of existing models.
  4. A branch of artificial intelligence that focuses on analyzing and interpreting existing data.

Question 10

Which of the following is an activation function used in neural networks?

  1. Sigmoid function
  2. K-means clustering function
  3. Mean Squared Error function
  4. Diffusion function