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| Section | Objectives |
|---|---|
| NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
| Responsible and Trustworthy AI | - Ethical AI principles - Bias and safety considerations |
| Multimodal AI Systems | - Multimodal model design - Cross-modal learning
|
| Generative AI Concepts | - Generative models
|
| Core AI and Machine Learning Fundamentals | - Machine learning basics
|
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NEW QUESTION # 37
In machine learning, what is the purpose of data normalization?
Answer: C
Explanation:
Normalization rescales numeric features onto a common, well-defined range or distribution - for example, min-max scaling to [0,1], or standardization to zero mean and unit variance (z-score) - so that features measured on different scales contribute comparably to model training. Among the options given, "converting data into a specific format for easier analysis" is the closest description of this rescaling purpose, though the more precise technical framing is: normalization standardizes the scale of feature values to stabilize and accelerate optimization.
This matters mechanically because many algorithms are scale-sensitive: gradient descent converges faster and more stably when input features share a comparable range (large-scale features would otherwise dominate the loss gradient), distance-based methods (k-NN, k-means, SVMs with RBF kernels) require comparable scales for distance calculations to be meaningful, and regularization terms penalize weight magnitude uniformly, which only makes sense if inputs are on comparable scales.
It is important to distinguish normalization from the other three options: it does not remove data (A, which is cleansing/filtering), does not increase complexity (B, the opposite of its intent), and does not reduce dimensionality (D, which describes techniques like PCA or feature selection - an entirely separate preprocessing goal focused on the number of features, not their scale).
Reference: Core Machine Learning and AI Knowledge domain - feature scaling (normalization, standardization) vs. dimensionality reduction.
NEW QUESTION # 38
You are building a real-time multimodal system that processes live video and audio streams to detect potentially dangerous situations. Latency is a critical constraint. Which of the following strategies is MOST important to minimize latency in this system?
Answer: A
Explanation:
Minimizing latency requires optimizing the model for efficient computation. Techniques like model quantization (reducing the precision of the weights), knowledge distillation (transferring knowledge from a larger model to a smaller one), and reducing the number of layers can significantly reduce the computational cost and inference time. Large batch sizes increase latency, and deep networks generally have higher latency due to increased computations.
NEW QUESTION # 39
You are evaluating a multimodal model that generates descriptions for video clips. You have human ratings for the relevance, fluency, and coherence of the generated descriptions. Which statistical test is MOST appropriate for determining if there is a statistically significant difference in the median ratings for each of these criteria (relevance, fluency, coherence) between two different versions of your model?
Answer: D
Explanation:
Since you're interested in comparing the medians of the ratings and not assuming a normal distribution (which is often the case with subjective human ratings), a non-parametric test is more appropriate than a t-test or ANOVA. The Mann-Whitney U test (also known as the Wilcoxon rank-sum test) is used to compare the medians of two independent groups. The Kruskal-Wallis test is used when you have more than two groups.
NEW QUESTION # 40
You are building a system to generate captions for images. You want to evaluate how well the generated captions describe the content of the images. Which of the following metrics are most suitable for evaluating the quality of image captions?
Answer: A,E
Explanation:
BLEU and ROUGE are standard metrics for evaluating the quality of generated text, especially in the context of machine translation and text summarization. BLEU measures the precision of n-grams in the generated text compared to reference texts, while ROUGE measures the recall. Pixel accuracy and Inception Score are more relevant for image classification and image generation tasks, respectively. F1-Score could be used if you manually labeled different image aspects of the caption.
NEW QUESTION # 41
You are building a multimodal emotion recognition system that takes both facial expressions (images) and speech audio as input. During development, you observe that the model is heavily biased towards the audio modality, effectively ignoring the visual input. Which technique would be the LEAST effective in mitigating this modality bias?
Answer: C
Explanation:
Increasing the audio branch's complexity while simplifying the image branch would actually exacerbate the modality bias towards audio. The other techniques (modality dropout, gradient blending, loss reweighting, and adversarial training) are all strategies designed to encourage the model to utilize both modalities more evenly. Increasing the model parameters of one mode leads to over-representation of that mode.
NEW QUESTION # 42
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