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NVIDIA NCA-GENL Exam Syllabus Topics:

NVIDIA Generative AI LLMs Sample Questions (Q43-Q48):

NEW QUESTION # 43
What is the primary purpose of applying various image transformation techniques (e.g., flipping, rotation, zooming) to a dataset?

• A. To artificially expand the dataset's size and improve the model's ability to generalize.
• B. To reduce the computational resources required for training deep learning models.
• C. To simplify the model's architecture, making it easier to interpret the results.
• D. To ensure perfect alignment and uniformity across all images in the dataset.

Answer: A

Explanation:
Image transformation techniques such as flipping, rotation, and zooming are forms of data augmentation used to artificially increase the size and diversity of a dataset. NVIDIA's Deep Learning AI documentation, particularly for computer vision tasks using frameworks like DALI (Data Loading Library), explains that data augmentation improves a model's ability to generalize by exposing it to varied versions of the training data, thus reducing overfitting. For example, flipping an image horizontally creates a new training sample that helps the model learn invariance to certain transformations. Option A is incorrect because transformations do not simplify the model architecture. Option C is wrong, as augmentation introduces variability, not uniformity. Option D is also incorrect, as augmentation typically increases computational requirements due to additional data processing.
References:
NVIDIA DALI Documentation: https://docs.nvidia.com/deeplearning/dali/user-guide/docs/index.html

NEW QUESTION # 44
Which aspect in the development of ethical AI systems ensures they align with societal values and norms?

• A. Achieving the highest possible level of prediction accuracy in AI models.
• B. Ensuring AI systems have explicable decision-making processes.
• C. Developing AI systems with autonomy from human decision-making.
• D. Implementing complex algorithms to enhance AI's problem-solving capabilities.

Answer: B

Explanation:
Ensuring explicable decision-making processes, often referred to as explainability or interpretability, is critical for aligning AI systems with societal values and norms. NVIDIA's Trustworthy AI framework emphasizes that explainable AI allows stakeholders to understand how decisions are made, fostering trust and ensuring compliance with ethical standards. This is particularly important for addressing biases and ensuring fairness. Option A (prediction accuracy) is important but does not guarantee ethical alignment. Option B (complex algorithms) may improve performance but not societal alignment. Option C (autonomy) can conflict with ethical oversight, making it less desirable.
References:
NVIDIA Trustworthy AI:https://www.nvidia.com/en-us/ai-data-science/trustworthy-ai/

NEW QUESTION # 45
Which metric is commonly used to evaluate machine-translation models?

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

Answer: C

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 # 46
When comparing and contrasting the ReLU and sigmoid activation functions, which statement is true?

• A. ReLU is less computationally efficient than sigmoid, but it is more accurate than sigmoid.
• B. ReLU and sigmoid both have a range of 0 to 1.
• C. ReLU is more computationally efficient, but sigmoid is better for predicting probabilities.
• D. ReLU is a linear function while sigmoid is non-linear.

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