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| Section | Objectives |
|---|---|
| Topic 1: Responsible and Trustworthy AI | - Ethical AI principles - Bias and safety considerations |
| Topic 2: Multimodal AI Systems | - Multimodal model design - Cross-modal learning
|
| Topic 3: Core AI and Machine Learning Fundamentals | - Machine learning basics
|
| Topic 4: Generative AI Concepts | - Generative models
|
| Topic 5: NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
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NEW QUESTION # 25
You are tasked with optimizing a multimodal A1 model that processes both image and text data for generating image captions. The model exhibits slow inference times, particularly when handling high-resolution images. Which of the following optimization strategies would be MOST effective in reducing inference latency, considering the NVIDIA ecosystem?
Answer: D
Explanation:
TensorRT is specifically designed for optimizing deep learning models for inference on NVIDIA GPUs. It performs graph optimization, quantization, and kernel fusion to reduce latency and increase throughput. Increasing batch size can sometimes help, but may also lead to memory issues or increased latency for small batch sizes. A larger model will generally increase latency. Simpler loss functions and removing dropout affect training and generalization, not necessarily inference speed.
NEW QUESTION # 26
You are tasked with evaluating the performance of a generative model that produces synthetic tabular dat a. This data will be used for downstream tasks such as training a fraud detection model. Which of the following evaluation metrics and strategies are MOST appropriate for assessing the quality and utility of the generated data in this scenario? Select all that apply.
Answer: A,C,E
Explanation:
JSD measures the similarity between probability distributions, making it suitable for comparing feature distributions in the real and synthetic datasets. Training a downstream model on synthetic data and evaluating it on real data directly assesses the utility of the synthetic data. PCA visualization helps compare the overall structure and distribution of the datasets. FID and SSIM are primarily used for evaluating image generation models, not tabular data. Thus A, B and C are the only correct answers.
NEW QUESTION # 27
You're building a virtual assistant using NVIDIAAvatar Cloud Engine (ACE). You want the avatar to respond to user queries with realistic facial expressions and lip synchronization. Which ACE components are essential for achieving this?
Answer: C
Explanation:
A complete ACE setup for realistic avatar interaction requires: Automatic Speech Recognition (ASR) to understand the user's query, Text-to-Speech (TTS) to generate the avatar's response, Audi02Emotion to infer emotional expressions from the text/audio, a 3D avatar model to represent the avatar visually, and an animation engine to drive facial expressions and lip synchronization. This combination ensures a lifelike and engaging user experience.
NEW QUESTION # 28
Consider the following code snippet which aims to create a custom prompt for a Stable Diffusion model using the 'diffusers* library. The goal is to generate an image of 'a cat wearing a hat sitting on a chair'. Which of the following modifications would MOST effectively improve the quality and coherence of the generated image?
Answer: B
Explanation:
Increasing the 'guidance_scales forces the generated image to adhere more closely to the prompt. Adding a negative prompt helps remove unwanted artifacts and improves image quality. Negative prompt is a well know prompt engginerring technique to remove unwanted objects or attributes.
NEW QUESTION # 29
How does the batch size influence VRAM consumption during inference with ML models on GPUs?
Answer: A
Explanation:
Batch size has a direct, proportional relationship with VRAM consumption during both training and inference: each sample in a batch requires its own memory allocation for input tensors, intermediate activations at every layer, and output tensors, all of which must reside in GPU memory simultaneously while the batch is being processed. Decreasing the batch size means fewer samples occupy memory concurrently, directly reducing peak VRAM consumption - this is precisely why reducing batch size is one of the first, most common remedies when a model run fails with an out-of-memory (OOM) error on a GPU with limited VRAM.
Option C states the inverse of the correct relationship and is a genuinely important misconception to correct:
increasing batch size increases VRAM consumption, not decreases it - parallelism across the batch means more simultaneous memory occupancy, not less. It's true that larger batches improve GPU compute
*utilization* and *throughput* (better amortizing fixed kernel-launch overhead and better exploiting parallel hardware) up to the point VRAM allows, but that throughput benefit is a separate effect from, and does not reduce, memory consumption. Options A and B both incorrectly claim batch size is memory-neutral, when it is in fact one of the most direct, easily controlled levers for managing VRAM usage - alongside model precision (quantization, mixed precision) and activation checkpointing, covered in the Performance Optimization domain elsewhere in this set.
Reference: Performance Optimization domain - batch size, VRAM/memory management, inference-time resource tradeoffs.
NEW QUESTION # 30
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