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NCA-GENL Practice Exam — NCA-GENL: Generative AI LLM

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NCA-GENL · Q1
Topic 1 Question #1 Which of the following best describes Word2vec?
  • A.
    A programming language used to build artificial intelligence models.
  • B.
    A statistical technique used to analyze word frequency in a text corpus.
  • C.
    A deep learning algorithm used to generate word embeddings from text data.
  • D.
    A database management system designed for storing and querying word data.

Answer: C

Option Analysis: A. This option is incorrect because Word2vec is not a programming language, it is an algorithm for natural language processing tasks. B. This option is incorrect because while word frequency analysis is a simple text processing technique, Word2vec learns vector representations of words rather than just counting frequency. C. This option is correct because Word2vec is a shallow neural network deep learning framework developed to generate dense, context-aware word embeddings that capture semantic similarity between words from a text corpus. D. This option is incorrect because Word2vec is not a database management system, it is a natural language processing algorithm. Key Concept: Word2vec is a deep learning-based word embedding technique that maps words from a text corpus to dense numerical vectors that capture semantic meaning. References: Introduction to Word Embeddings and Word2Vec, Google Developers, Word2Vec Explained, Microsoft Learn
NCA-GENL · Q2
Topic 1 Question #2 In the context of language models, what does an autoregressive model predict?
  • A.
    The probability of the next token in a text given the previous 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 LTSM cells.
  • D.
    The probability of the next token by looking at the previous and future input tokens.

Answer: A

Option Analysis: A. This option is correct because the core defining property of an autoregressive language model is to model the probability distribution of the next token conditioned only on all preceding tokens in the sequence. B. This option is incorrect because Monte Carlo sampling is an inference method for selecting tokens, not the fundamental prediction task that defines an autoregressive model. C. This option is incorrect because autoregressive modeling is a prediction formulation that is architecture agnostic, and can be implemented with transformers as well as RNNs/LSTMs, so this statement is both incorrect and incomplete. D. This option is incorrect because looking at both previous and future tokens describes bidirectional models like BERT, not autoregressive models which only use preceding context. Key Concept: Autoregressive language modeling is defined by sequential prediction of each next token conditioned only on prior tokens in the sequence. References: Introduction to Autoregressive and Bidirectional Language Models, Hugging Face Docs, https://huggingface.co/docs/transformers/en/model_summary Google AI Blog: Attention Is All You Need, https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html
NCA-GENL · Q3
Topic 1 Question #3 In large-language models, what is the purpose of the attention mechanism?
  • A.
    To measure the importance of the words in the output sequence.
  • B.
    To determine the order in which words are generated.
  • C.
    To capture the order of the words in the input sequence.
  • D.
    To assign weights to each word in the input sequence.

Answer: D

Option Analysis: A. Option A is incorrect because attention operates on the input sequence when computing context for each output step, not to measure importance only in the output sequence as stated. B. Option B is incorrect because the attention mechanism does not determine the order of word generation during inference, which is handled by the autoregressive decoding process. C. Option C is incorrect because token positional embeddings, not the attention mechanism itself, are designed to capture word order information in the input sequence. D. Option D is correct because the core purpose of the attention mechanism is to assign learnable, context-aware weights to each token (word) in the input sequence to reflect relevance for the current prediction step. Key Concept: The attention mechanism in large language models enables context-aware weighting of input tokens to prioritize relevant information when generating each output token. References: Attention Is All You Need, https://arxiv.org/abs/1706.03762 Google AI Developer Documentation: Transformer Model Architecture
NCA-GENL · Q4
Topic 1 Question #4 In the transformer architecture, what is the purpose of positional encoding?
  • A.
    To remove redundant information from the input sequence.
  • B.
    To encode the semantic meaning of each token in the input sequence.
  • C.
    To add information about the order of each token in the input sequence.
  • D.
    To encode the importance of each token in the input sequence.

Answer: C

Option Analysis: A. Option A is incorrect because positional encoding does not remove redundant information from input sequences, this is not its intended purpose. B. Option B is incorrect because semantic meaning of tokens is encoded by token embeddings, not positional encoding. C. Option C is correct because self-attention in transformers is order-agnostic, so positional encoding explicitly adds sequence order information for each input token. D. Option D is incorrect because token importance is calculated by attention weights after positional encoding is added, not encoded by positional encoding itself. Key Concept: In Transformer architecture, positional encoding injects sequence order information into order-agnostic self-attention processing to enable the model to understand token position. References: Attention Is All You Need, arXiv.org. https://arxiv.org/abs/1706.03762 Google AI Blog: Transformer Neural Models for Language Understanding, Google AI. https://ai.googleblog.com/2018/08/transformer-neural-models-for-language.html
NCA-GENL · Q5
Topic 1 Question #5 Which technique is designed to train a deep learning model by adjusting the weights of the neural network based on the error between the predicted and actual outputs?
  • A.
    Gradient Boosting
  • B.
    Principal Component Analysis
  • C.
    K-means Clustering
  • D.
    Backpropagation

Answer: D

Option Analysis: A. Gradient Boosting is an ensemble tree-based machine learning algorithm that builds sequential models to correct residual errors, not a technique for adjusting neural network weights during deep learning training, so it is incorrect. B. Principal Component Analysis is an unsupervised dimensionality reduction technique, not a weight adjustment method for training deep neural networks, so it is incorrect. C. K-means Clustering is an unsupervised clustering algorithm that groups similar data points, it is not used for training deep learning models by adjusting neural network weights, so it is incorrect. D. Backpropagation is the core standard technique for training deep neural networks that calculates prediction error and propagates it backwards through the network to adjust layer weights to minimize overall prediction error, so it is correct. Key Concept: Backpropagation is the foundational weight optimization technique used to train deep learning neural networks by adjusting weights based on prediction error between actual and predicted outputs. References: Introduction to Backpropagation, DeepLearning.AI on Coursera, https://www.coursera.org/lecture/neural-networks-deep-learning/backpropagation-intuition-yV0oK Backpropagation, Microsoft Learn

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This question bank includes 50 NCA-GENL practice questions covering single and multiple choice, each with answers and explanations.

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