NCA-GENL Practice Test

NCA-GENL Practice Test practice questions, page 3 of 4, with answer review and explanations.

Question 11

In ML applications, which machine learning algorithm is commonly used for creating new data based on existing data?

  1. Decision tree
  2. Support vector machine (SVM)
  3. Generative adversarial network (GAN)
  4. K-means clustering

Question 12

What statement best describes the diffusion models in generative AI?

  1. Diffusion models are probabilistic generative models that progressively inject noise into data, then learn to reverse this process for sample generation.
  2. Diffusion models are discriminative models that use gradient-based optimization algorithms to classify data points.
  3. Diffusion models are unsupervised models that use clustering algorithms to group similar data points together.
  4. Diffusion models are generative models that use a transformer architecture to learn the underlying probability distribution of the data.

Question 13

What is the main consequence of the scaling law in deep learning for real-world applications?

  1. With more data, it is possible to exceed the irreducible error region.
  2. The best performing model can be established even in the small data region.
  3. Small and medium error regions can approach the results of the big data region.
  4. In the power-law region, with more data it is possible to achieve better results.

Question 14

In Natural Language Processing, there are a group of steps in problem formulation collectively known as word representations (also word embeddings). Which of the following are Deep Learning models that can be used to produce these representations for NLP tasks? (Choose two.)

  1. Word2vec
  2. WordNet
  3. Kubernetes
  4. TensorRT
  5. BERT

Question 15

What is the purpose of the NVIDIA NEMO Toolkit?

  1. NeMo focuses on the morphology of a language by studying its words, and how they are formed.
  2. NeMo helps researchers to develop models that trade-off size with minimum loss impact.
  3. NeMo facilitates the creation of models for speech recognition and natural language understanding.
  4. NeMo helps researchers develop state-of-the-art models for computer vision based on convolutions.