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NVIDIA NCA-GENM Exam Syllabus Topics:

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

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NVIDIA Generative AI Multimodal Sample Questions (Q18-Q23):

NEW QUESTION # 18
You are building a system that identifies objects in images based on spoken commands. You have trained a model but notice that it performs poorly when the spoken command contains synonyms or paraphrases of the training data. Which of the following techniques would BEST address this issue?

A. Employing a word embedding model (e.g., Word2Vec, GloVe) or contextual embeddings (e.g., BERT) to represent the spoken commands, allowing the model to generalize to semantically similar phrases.
B. Using data augmentation techniques such as rotating and scaling the images.
C. Increasing the size of the training dataset.
D. Reducing the learning rate of the model.
E. Simplifying the spoken commands to use only a limited vocabulary.

Answer: A

Explanation:
Option C is the most effective solution. Word embeddings or contextual embeddings capture the semantic meaning of words and phrases, enabling the model to understand synonyms and paraphrases. Options A, B, D, and E address different issues but are not directly related to the semantic understanding of spoken commands. Increasing data size (A) might help, but semantic understanding is key. Image augmentation (B) is irrelevant to the text understanding. Simplifying commands (D) limits the system's capabilities. Reducing learning rate (E) might improve training stability but doesn't address the semantic understanding.

NEW QUESTION # 19
When training a multimodal generative model for image captioning, you notice the model generates grammatically correct but generic and uninformative captions. Which technique is MOST likely to improve the in formativeness and specificity of the generated captions?

A. Decrese the size of the vocabulary.
B. Increase the size of the image encoder.
C. Decrease the learning rate during training.
D. Employ a diverse beam search or sampling strategy during inference to encourage exploration of different caption possibilities.
E. Use beam search during inference with a large beam size.

Answer: D

Explanation:
Diverse beam search or sampling strategies encourage the model to explore different caption possibilities during inference, leading to more diverse and informative captions. Standard beam search often converges to the most likely caption, which tends to be generic. Increasing the image encoder Size might improve image feature extraction but doesn't directly address the caption informativeness problem. Decreasing the learning rate is a general training technique that might improve convergence but doesn't specifically target caption informativeness.

NEW QUESTION # 20
You are tasked with building a multimodal A1 system that can generate video descriptions from video footage. You have experimented with several architectures, including combining CNNs for visual feature extraction and LSTMs for sequence generation. However, you are facing challenges with the model capturing long-range dependencies in the video. Which of the following architectural modifications or training techniques is MOST likely to address this issue?

A. Reducing the frame rate of the input video to reduce the temporal complexity.
B. Increasing the number of layers in the CNN to extract more detailed visual features.
C. Using a smaller batch size during training to reduce memory consumption.
D. Incorporating a Transformer-based architecture, such as a Vision Transformer (ViT) for visual feature extraction and a standard Transformer for sequence generation.

Answer: A

Explanation:
Transformers are known for their ability to capture long-range dependencies due to their self-attention mechanism. Replacing LSTMs with Transformers allows the model to attend to relevant parts of the video sequence regardless of their temporal distance. While CNNs can extract visual features, they don't inherently address long-range dependencies. RNNs are prone to vanishing gradients, making it difficult to learn long- range dependencies. Reducing the frame rate or batch size doesn't directly address the issue of capturing long-range dependencies within the video sequence.

NEW QUESTION # 21
You are fine-tuning a pre-trained multimodal model for a new task. You have limited computational resources. Which of the following fine-tuning strategies would be the MOST computationally efficient while still achieving good performance?

A. Fine-tune all the layers of the model.
B. Train a new random model from scratch for the task, which will avoid the need to load the pre-trained model.
C. Freeze all layers except the classification head and fine-tune only the classification head.
D. Randomize the model to train, if it improves the training rate.
E. Freeze the lower layers of the model and fine-tune the upper layers and the classification head.

Answer: E

Explanation:
Freezing the lower layers and fine-tuning the upper layers and classification head strikes a balance between computational efficiency and performance. The lower layers typically capture more general features that are less specific to the task, while the upper layers capture more task-specific features. Freezing the lower layers reduces the number of trainable parameters, making the fine-tuning process more computationally efficient. Fine-tuning all layers is computationally expensive, freezing all layers except the classification head might not be sufficient for adapting to the new task, and training from scratch does not leverage the knowledge learned during pre-training. Randomizing model is not a general practice.

NEW QUESTION # 22
You are deploying a multimodal Generative A1 model on a cloud platform. The model takes video and text as input to generate video descriptions. The model's performance needs to be monitored to ensure it meets certain performance SLAs. Which of the following metrics are MOST crucial to monitor in a production environment to ensure both computational efficiency and output quality? (Select TWO)

A. Inference latency (time per request).
B. GPU utilization.
C. Number of lines of code in the model.
D. BLEU score (or similar text generation metric) for generated descriptions.
E. Model size on disk.

Answer: A,D

Explanation:
Inference latency directly impacts the user experience and resource utilization. A high latency indicates potential bottlenecks. The BLEU score (or similar metric) measures the quality of the generated text, ensuring that the model is producing accurate and relevant descriptions. GPU utilization is important but secondary compared to latency and quality. Model size and lines of code are not direct indicators of runtime performance or output quality.

NEW QUESTION # 23
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