NCA-GENL Reliable Test Topics - Certification NCA-GENL Test Answers
NCA-GENL Reliable Test Topics - Certification NCA-GENL Test Answers
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NVIDIA Generative AI LLMs Sample Questions (Q36-Q41):
NEW QUESTION # 36
In the Transformer architecture, which of the following statements about the Q (query), K (key), and V (value) matrices is correct?
- A. V is used to calculate the positional embeddings for each token in the input sequence.
- B. K is responsible for computing the attention scores between the query and key vectors.
- 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 # 37
In the context of fine-tuning LLMs, which of the following metrics is most commonly used to assess the performance of a fine-tuned model?
- A. Number of layers
- B. Model size
- C. Training duration
- D. Accuracy on a validation set
Answer: D
Explanation:
When fine-tuning large language models (LLMs), the primary goal is to improve the model's performance on a specific task. The most common metric for assessing this performance is accuracy on a validation set, as it directly measures how well the model generalizes to unseen data. NVIDIA's NeMo framework documentation for fine-tuning LLMs emphasizes the use of validation metrics such as accuracy, F1 score, or task-specific metrics (e.g., BLEU for translation) to evaluate model performance during and after fine-tuning.
These metrics provide a quantitative measure of the model's effectiveness on the target task. Options A, C, and D (model size, training duration, and number of layers) are not performance metrics; they are either architectural characteristics or training parameters that do not directly reflect the model's effectiveness.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/model_finetuning.html
NEW QUESTION # 38
Which calculation is most commonly used to measure the semantic closeness of two text passages?
- A. Jaccard similarity
- B. Cosine similarity
- C. Euclidean distance
- D. Hamming distance
Answer: B
Explanation:
Cosine similarity is the most commonly used metric to measure the semantic closeness of two text passages in NLP. It calculates the cosine of the angle between two vectors (e.g., word embeddings or sentence embeddings) in a high-dimensional space, focusing on the direction rather than magnitude, which makes it robust for comparing semantic similarity. NVIDIA's documentation on NLP tasks, particularly in NeMo and embedding models, highlights cosine similarity as the standard metric for tasks like semantic search or text similarity, often using embeddings from models like BERT or Sentence-BERT. Option A (Hamming distance) is for binary data, not text embeddings. Option B (Jaccard similarity) is for set-based comparisons, not semantic content. Option D (Euclidean distance) is less common for text due to its sensitivity to vector magnitude.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html
NEW QUESTION # 39
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 uncover patterns and anomalies in the dataset
- C. To determine the optimum number of layers in the neural network
- D. To assess the computing resources required for fine-tuning
Answer: B
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 # 40
When comparing and contrasting the ReLU and sigmoid activation functions, which statement is true?
- A. ReLU is a linear function while sigmoid is non-linear.
- B. ReLU is less computationally efficient than sigmoid, but it is more accurate than sigmoid.
- C. ReLU is more computationally efficient, but sigmoid is better for predicting probabilities.
- D. ReLU and sigmoid both have a range of 0 to 1.
Answer: C
Explanation:
ReLU (Rectified Linear Unit) and sigmoid are activation functions used in neural networks. According to NVIDIA's deep learning documentation (e.g., cuDNN and TensorRT), ReLU, defined as f(x) = max(0, x), is computationally efficient because it involves simple thresholding, avoiding expensive exponential calculations required by sigmoid, f(x) = 1/(1 + e