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

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NVIDIA Generative AI Multimodal Sample Questions (Q12-Q17):

NEW QUESTION # 12
A multimodal A1 model is designed to translate sign language videos into text. The model performs well on videos with clear hand gestures and lighting conditions but struggles with videos recorded in low light or with partial hand occlusions. Which of the following strategies would be MOST effective in improving the model's robustness to these challenging conditions?

A. Increasing the size of the text vocabulary.
B. Training the model on a smaller dataset.
C. Using a simpler text encoder.
D. Reducing the frame rate of the input videos.
E. Applying image enhancement techniques (e.g., contrast adjustment, noise reduction) to the video frames.

Answer: E

Explanation:
Applying image enhancement techniques to the video frames can improve the visibility of hand gestures in low-light conditions and reduce the impact of noise, making the model more robust. Reducing the frame rate or training on a smaller dataset would likely decrease performance. Increasing the text vocabulary or using a simpler text encoder would not directly address the issue of poor video quality.

NEW QUESTION # 13
Consider a multimodal A1 system that generates recipes based on images of ingredients. The system uses attention maps to highlight the relevant ingredients in the image. You observe that the attention maps are often noisy and highlight irrelevant parts of the image, leading to incorrect recipes. Which of the following strategies could BEST improve the quality and interpretability of the attention maps?

A. Increase the size of the convolutional filters in the image encoder.
B. Add more layers to the attention module.
C. Use a stronger image encoder, such as a larger ResNet or a Vision Transformer.
D. Apply L1 regularization to the attention weights to encourage sparsity.
E. All of the above can improve the quality and interpretability of the attention maps.

Answer: C,D

Explanation:
Applying L1 regularization to the attention weights encourages sparsity, meaning that the model will focus on only the most relevant regions of the image, leading to cleaner and more interpretable attention maps. Option D is another possible answer as it will help create an image with more precise objects detection. Option A is unlikely to improve the quality and interpretability of the attention maps. The size of the convolutional filters is more related to the receptive field of the image encoder. Adding more layers (C) may not directly address the noisiness of the attention maps.

NEW QUESTION # 14
Which of the following are valid techniques for fusing multimodal data?

A. All of the above except D.
B. Just Fusion
C. Late Fusion
D. Intermediate Fusion
E. Early Fusion

Answer: A

Explanation:
Early fusion combines data at the input level, late fusion combines outputs from individual modality models, and intermediate fusion combines data at some intermediate layer within the network. 'Just Fusion' isn't a standard term. therefore all the answer should be A, B and C.

NEW QUESTION # 15
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. BLEU score (or similar text generation metric) for generated descriptions.
C. Number of lines of code in the model.
D. GPU utilization.
E. Model size on disk.

Answer: A,B

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 # 16
You are tasked with deploying a generative A1 model using NVIDIA Triton Inference Server. Which configuration parameter within Triton is MOST crucial for optimizing throughput and minimizing latency when serving a large number of concurrent requests?

A. Max Queue Size
B. Input Data Type
C. Instance Group Count
D. Batching Preferences
E. Default Model Filename

Answer: C

Explanation:
The 'Instance Group Count' parameter in Triton determines how many instances of the model are loaded onto the GPU(s) and/or CPU(s). Increasing the number of instances (up to the hardware's capacity) allows Triton to handle more concurrent requests in parallel, thereby improving throughput and reducing latency. While batching and max queue size can also help, the instance count is the most fundamental for parallelism. The default model filename is irrelevent to performance and input data type is a requirement not a performance consideration.

NEW QUESTION # 17
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