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NVIDIA NCA-GENM Exam Syllabus Topics:

Section	Objectives
Topic 1: Core AI and Machine Learning Fundamentals	- Machine learning basics 1. Neural networks fundamentals 2. Supervised and unsupervised learning
Topic 2: Multimodal AI Systems	- Multimodal model design - Cross-modal learning 1. Text-image integration 2. Audio-visual understanding
Topic 3: NVIDIA AI Ecosystem	- NVIDIA tools and frameworks 1. NeMo framework usage 2. GPU-accelerated AI workflows
Topic 4: Generative AI Concepts	- Generative models 1. Transformers and LLM basics 2. Diffusion models
Topic 5: Responsible and Trustworthy AI	- Ethical AI principles - Bias and safety considerations

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NVIDIA Generative AI Multimodal Sample Questions (Q52-Q57):

NEW QUESTION # 52
Consider a scenario where you are evaluating the performance of a multimodal A1 model that generates descriptions for images. However, the generated descriptions tend to be repetitive and lack diversity. Which of the following techniques can be employed to address this issue and encourage more diverse and creative outputs from the model? (Select TWO)

A. Employing a nucleus sampling (top-p sampling) decoding strategy.
B. Using a greedy decoding strategy.
C. Utilizing a temperature scaling parameter during decoding and increasing its value.
D. Increasing the model's training data size.
E. Using a beam search decoding strategy with a small beam width.

Answer: A,C

Explanation:
Nucleus sampling (top-p sampling) randomly samples from the smallest set of words whose cumulative probability mass exceeds a threshold p, encouraging more diverse outputs. Increasing the temperature scaling parameter makes the probability distribution flatter, leading to more exploration and less predictable (more creative) outputs. Beam search with a small beam width might still result in repetitive outputs. Increasing the training data size can help, but it might not directly address the lack of diversity. Greedy decoding always selects the most probable word, leading to repetitive and predictable outputs.

NEW QUESTION # 53
Consider a multimodal generative model trained on a dataset of images and corresponding captions. After training, you observe that the model generates captions that are grammatically correct but often lack specific details and relevance to the input image. Which of the following regularization techniques is MOST likely to improve the faithfulness and informativeness of the generated captions?

A. KL divergence regularization to encourage the generated caption distribution to be similar to the prior caption distribution.
B. L1 regularization on the model weights.
C. Attention regularization to encourage the model to attend to relevant regions in the image when generating the caption.
D. Adding Gaussian noise to the input images.
E. Dropout during training.

Answer: C

Explanation:
Attention regularization is the most directly relevant technique. By penalizing the model when it fails to attend to relevant image regions, it encourages the model to focus on the important visual cues when generating the caption. This leads to more informative and faithful captions that are grounded in the image content. L1 regularization (A) promotes sparsity, Dropout (B) reduces overfitting, KL divergence regularization (C) encourages similarity to the prior distribution (which may not improve faithfulness), and adding Gaussian noise (E) improves robustness, but none of these directly address the issue of attention to relevant image regions.

NEW QUESTION # 54
You are building a multimodal model to translate spoken language into sign language animations. You have audio recordings of spoken words and corresponding sign language video sequences. Which architecture would be most suitable for this task?

A. A support vector machine (SVM) to classify audio and video features.
B. A recurrent neural network (RNN) or Transformer network with attention mechanisms to capture the temporal dependencies in both the audio and video sequences, and a connectionist temporal classification (CTC) loss function for alignment.
C. A convolutional neural network (CNN) for audio and another CNN for video, followed by a fully connected layer.
D. A generative adversarial network (GAN) to generate realistic sign language animations.
E. A simple feedforward neural network.

Answer: B

Explanation:
RNNs or Transformer networks with attention mechanisms are best suited for sequence-to-sequence tasks like translation, as they can capture temporal dependencies. CTC loss helps align the audio and video sequences, even if they are not perfectly synchronized. CNNs are good for feature extraction but don't handle sequential data as well as RNNs/Transformers.

NEW QUESTION # 55
You are working with a large dataset of images to train a Generative A1 model. You suspect that some images are corrupted or of poor quality, which could negatively impact training. Which of the following methods would be the MOST effective in identifying and removing these problematic images?

A. Calculate the average pixel intensity for each image and remove those with very low or very high average intensity.
B. Manually inspect each image and remove those that appear to be corrupted or low quality.
C. Compute the image sharpness (e.g., using Laplacian variance) and remove images with low sharpness values.
D. Check for file corruption errors during image loading and remove those files.
E. Use a pre-trained image quality assessment model (e.g., BRISQUE, NIQE) to score each image and remove those with low scores.

Answer: C,D,E

Explanation:
Checking file integrity to remove corruption images is an important first step. Computing Image Sharpness is an effective way to programmatically identify and filter blur or out-of-focus Images. Using pre-trained image assessment models is another advanced and automativ approach to identifying and removing images of low quality, even if they are not overtly corrupted. Manually checking would take too long. Average pixel intensity is often useful to filter.

NEW QUESTION # 56
You are evaluating a generative A1 model for image captioning. The model produces captions that are grammatically correct but often miss key objects in the image. Which of the following evaluation metrics would be MOST suitable to identify this deficiency?

A. BLEU (Bilingual Evaluation Understudy)
B. Perplexity
C. Inception Score
D. ROUGE (Recall-Oriented Understudy for Gisting Evaluation)
E. CIDEr (Consensus-based Image Description Evaluation)

Answer: E

Explanation:
CIDEr is designed to evaluate image captions by comparing them to a set of reference captions, emphasizing consensus and relevance to the image content. It is more sensitive to the presence of key objects than BLEU or ROUGE, which primarily focus on n-gram overlap and grammatical correctness. Perplexity and Inception Score are not relevant for evaluating image captioning.

NEW QUESTION # 57
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