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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Trustworthy AI | 5% | - Robustness and error mitigation - Ethical considerations and responsible use - Reliability, fairness, and safety in generative systems |
| Topic 2: Experimentation | 25% | - Metrics and validation strategies for generative models - Experiment design and methodology - Model training, fine-tuning, and evaluation |
| Topic 3: Data Analysis and Visualization | 10% | - Interpretation of generative AI outputs - Visualization techniques for model behavior and results - Analyzing multimodal datasets and outputs |
| Topic 4: Core Machine Learning and AI Knowledge | 20% | - Neural network architectures relevant to multimodal systems - Generative AI principles and techniques - Fundamental concepts of machine learning and deep learning |
| Topic 5: Multimodal Data | 15% | - Multimodal model architectures and integration - Data preprocessing, fusion, and representation - Characteristics of text, image, and audio data |
| Topic 6: Performance Optimization | 10% | - Model efficiency and inference optimization - Hardware acceleration with NVIDIA platforms - Scalability and deployment considerations |
| Topic 7: Software Development and Engineering | 15% | - Best practices for building and maintaining systems - Libraries, frameworks, and tools for multimodal AI - Development workflows for generative AI applications |
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NEW QUESTION # 31
Which of the following NVIDIA SDKs is most suitable for deploying a real-time, low-latency speech-to-text service as part of a multimodal AI application?
Answer: A
Explanation:
NVIDIA Riva is specifically designed for building and deploying real-time conversational A1 services, including speech-to-text, text-to- speech, and natural language understanding. Its focus is on low latency and high accuracy for conversational applications.
NEW QUESTION # 32
You are fine-tuning a pre-trained large language model (LLM) for a specific text generation task. During training, you observe that the model is overfitting to the training data and not generalizing well to unseen examples. Which of the following techniques could be MOST effective in mitigating overfitting in this scenario?
Answer: A,B,D
Explanation:
Dropout regularization prevents the model from relying too heavily on specific neurons. Decreasing the learning rate reduces the step size during training, preventing the model from memorizing the training data. Early stopping prevents the model from training for too long and overfitting to the training data. While increasing the training dataset can help, it might not always be feasible. Smaller batch sizes can sometimes increase generalization, but it's less direct than the other techniques.
NEW QUESTION # 33
You are working with a multimodal model that combines text and video data for action recognition. The text data consists of descriptions of the actions, and the video data consists of sequences of frames. You want to fuse these modalities at a late fusion stage. Which of the following approaches BEST describes late fusion?
Answer: B
Explanation:
Late fusion involves processing each modality separately to obtain feature representations and then combining these representations at a later stage, typically by concatenation or averaging, before making a final prediction. Averaging predictions (option B) is a specific type of late fusion. Concatenating raw pixel values and word embeddings (option A) is an example of early fusion. Training a single model with a shared embedding space (option C) is also closer to early or intermediate fusion. Attention mechanisms can be used in various fusion strategies but do not define late fusion specifically.
NEW QUESTION # 34
You are working on a project that involves generating music from video. The approach uses a pre-trained video encoder and a pre- trained music decoder. You find that the generated music often lacks a clear connection to the visual content of the video. To improve the coherence between the video and the generated music, which of the following steps would be the MOST effective? (Select TWO)
Answer: C,E
Explanation:
Cross-modal attention (B) allows the model to explicitly learn relationships between visual and musical features. End-to-end fine- tuning with a suitable loss function (D) encourages the model to learn a coherent mapping between the two modalities. Training the encoders and decoders separately won't necessarily improve coherence. Generating from random noise defeats the purpose. Limiting video length may reduce complexity but doesn't fundamentally address the alignment problem.
NEW QUESTION # 35
What is a main application of Triton Inference Server?
Answer: A
Explanation:
NVIDIA Triton Inference Server is an open-source model-serving platform designed to standardize production deployment across heterogeneous model formats and hardware backends. Its defining capability is multi-framework support: a single Triton instance can concurrently serve models trained in TensorFlow, PyTorch, ONNX Runtime, TensorRT, OpenVINO, and custom Python/C++ backends, exposing them through unified HTTP/REST and gRPC inference APIs. This eliminates the need for framework-specific serving stacks and lets teams standardize deployment infrastructure independent of how a given model was trained.
Option B is a common but incorrect assumption - Triton explicitly supports both GPU and CPU inference, which matters for cost-sensitive or edge deployments where GPU availability is limited. Option C confuses Triton with cuGraph, a separate RAPIDS library for GPU-accelerated graph analytics (unrelated to model serving), and option A describes a generative task (denoising diffusion), which is a *model capability*, not a Triton *server* function - Triton can host such a model, but "generating images from noise" is not what the server itself does.
Triton also provides dynamic batching, concurrent model execution, model ensembling (chaining pre/post- processing with inference), and metrics export - features tested elsewhere in the Software Development and Engineering and Performance Optimization domains.
Reference: Software Development and Engineering domain - Triton Inference Server, multi-framework model deployment.
NEW QUESTION # 36
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