NCA-GENL RELIABLE TEST SIMS - NEW NCA-GENL TEST TEST

NCA-GENL Reliable Test Sims - New NCA-GENL Test Test

NCA-GENL Reliable Test Sims - New NCA-GENL Test Test

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NVIDIA Generative AI LLMs Sample Questions (Q29-Q34):

NEW QUESTION # 29
Which metric is commonly used to evaluate machine-translation models?

  • A. BLEU score
  • B. Perplexity
  • C. ROUGE score
  • D. F1 Score

Answer: A

Explanation:
The BLEU (Bilingual Evaluation Understudy) score is the most commonly used metric for evaluating machine-translation models. It measures the precision of n-gram overlaps between the generated translation and reference translations, providing a quantitative measure of translation quality. NVIDIA's NeMo documentation on NLP tasks, particularly machine translation, highlights BLEU as the standard metric for assessing translation performance due to its focus on precision and fluency. Option A (F1 Score) is used for classification tasks, not translation. Option C (ROUGE) is primarily for summarization, focusing on recall.
Option D (Perplexity) measures language model quality but is less specific to translation evaluation.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html
Papineni, K., et al. (2002). "BLEU: A Method for Automatic Evaluation of Machine Translation."


NEW QUESTION # 30
In the Transformer architecture, which of the following statements about the Q (query), K (key), and V (value) matrices is correct?

  • A. K is responsible for computing the attention scores between the query and key vectors.
  • B. V is used to calculate the positional embeddings for each token in the input sequence.
  • C. Q represents the query vector used to retrieve relevant information from the input sequence.
  • D. Q, K, and V are randomly initialized weight matrices used for positional encoding.

Answer: C

Explanation:
In the transformer architecture, the Q (query), K (key), and V (value) matrices are used in the self-attention mechanism to compute relationships between tokens in a sequence. According to "Attention is All You Need" (Vaswani et al., 2017) and NVIDIA's NeMo documentation, the query vector (Q) represents the token seeking relevant information, the key vector (K) is used to compute compatibility with other tokens, and the value vector (V) provides the information to be retrieved. The attention score is calculated as a scaled dot- product of Q and K, and the output is a weighted sum of V. Option C is correct, as Q retrieves relevant information. Option A is incorrect, as Q, K, and V are not used for positional encoding. Option B is wrong, as attention scores are computed using both Q and K, not K alone. Option D is false, as positional embeddings are separate from V.
References:
Vaswani, A., et al. (2017). "Attention is All You Need."
NVIDIA NeMo Documentation:https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html


NEW QUESTION # 31
What is the purpose of few-shot learning in prompt engineering?

  • A. To optimize hyperparameters
  • B. To give a model some examples
  • C. To train a model from scratch
  • D. To fine-tune a model on a massive dataset

Answer: B

Explanation:
Few-shot learning in prompt engineering involves providing a small number of examples (demonstrations) within the prompt to guide a large language model (LLM) to perform a specific task without modifying its weights. NVIDIA's NeMo documentation on prompt-based learning explains that few-shot prompting leverages the model's pre-trained knowledge by showing it a few input-output pairs, enabling it to generalize to new tasks. For example, providing two examples of sentiment classification in a prompt helps the model understand the task. Option B is incorrect, as few-shot learning does not involve training from scratch. Option C is wrong, as hyperparameter optimization is a separate process. Option D is false, as few-shot learning avoids large-scale fine-tuning.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html
Brown, T., et al. (2020). "Language Models are Few-Shot Learners."


NEW QUESTION # 32
When fine-tuning an LLM for a specific application, why is it essential to perform exploratory data analysis (EDA) on the new training dataset?

  • A. To select the appropriate learning rate for the model
  • B. To assess the computing resources required for fine-tuning
  • C. To uncover patterns and anomalies in the dataset
  • D. To determine the optimum number of layers in the neural network

Answer: C

Explanation:
Exploratory Data Analysis (EDA) is a critical step in fine-tuning large language models (LLMs) to understand the characteristics of the new training dataset. NVIDIA's NeMo documentation on data preprocessing for NLP tasks emphasizes that EDA helps uncover patterns (e.g., class distributions, word frequencies) and anomalies (e.g., outliers, missing values) that can affect model performance. For example, EDA might reveal imbalanced classes or noisy data, prompting preprocessing steps like data cleaning or augmentation. Option B is incorrect, as learning rate selection is part of model training, not EDA. Option C is unrelated, as EDA does not assess computational resources. Option D is false, as the number of layers is a model architecture decision, not derived from EDA.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html


NEW QUESTION # 33
When designing prompts for a large language model to perform a complex reasoning task, such as solving a multi-step mathematical problem, which advanced prompt engineering technique is most effective in ensuring robust performance across diverse inputs?

  • A. Zero-shot prompting with a generic task description.
  • B. Retrieval-augmented generation with external mathematical databases.
  • C. Few-shot prompting with randomly selected examples.
  • D. Chain-of-thought prompting with step-by-step reasoning examples.

Answer: D

Explanation:
Chain-of-thought (CoT) prompting is an advanced prompt engineering technique that significantly enhances a large language model's (LLM) performance on complex reasoning tasks, such as multi-step mathematical problems. By including examples that explicitly demonstrate step-by-step reasoning in the prompt, CoT guides the model to break down the problem into intermediate steps, improving accuracy and robustness.
NVIDIA's NeMo documentation on prompt engineering highlights CoT as a powerful method for tasks requiring logical or sequential reasoning, as it leverages the model's ability to mimic structured problem- solving. Research by Wei et al. (2022) demonstrates that CoT outperforms other methods for mathematical reasoning. Option A (zero-shot) is less effective for complex tasks due to lack of guidance. Option B (few- shot with random examples) is suboptimal without structured reasoning. Option D (RAG) is useful for factual queries but less relevant for pure reasoning tasks.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html Wei, J., et al. (2022). "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models."


NEW QUESTION # 34
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