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| Section | Weight | Objectives |
|---|---|---|
| Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Performance Optimization | 10% | - Monitoring and improving system efficiency - Techniques for optimizing AI performance |
| Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
| Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
| Software Development & Engineering | 15% | - Integration and deployment of multimodal AI systems - Python libraries for multimodal AI |
| Experimentation | 25% | - Experimental design - A/B testing - Model evaluation and comparison - Hypothesis testing |
| Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
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NEW QUESTION # 37
Consider a scenario where you want to use a Transformer model for generating music. Which of the following modifications to the standard Transformer architecture would be most beneficial for capturing the long-range dependencies and musical structure inherent in music?
Answer: D
Explanation:
Relative positional embeddings are particularly effective for capturing relationships based on relative positions, which is crucial in music where the intervals and relationships between notes are significant. Standard positional embeddings only encode absolute position. Capturing those relationships greatly improves model accuracy While the other options might change behavior, they wouldn't specifically improve sequence modeling ability.
NEW QUESTION # 38
You're building a multimodal model that takes an image and a question as input and outputs an answer (Visual Question Answering - VQA). You find your model is heavily relying on the question type (e.g., 'What color is...' always predicts 'blue') and ignoring the image content. Select TWO of the following techniques that could help mitigate this 'language prior' problem.
Answer: B,D
Explanation:
B and D are the best answers. Using a question-only baseline (B) allows you to directly quantify the model's reliance on language priors and then penalize the model for over-relying on them during training, encouraging it to pay more attention to the image. Balancing the dataset (D) by ensuring an equal number of correct answers for each question type makes it harder for the model to simply predict based on the question type alone. The image encoder shouldn't be replaced as that is needed in the task. More images wouldn't necessarily fix the data imbalance.
NEW QUESTION # 39
Which framework is used for conversational AI models development?
Answer: A
Explanation:
NVIDIA NeMo is NVIDIA's open-source framework for building, training, and customizing conversational and generative AI models - spanning automatic speech recognition, natural language processing, text-to- speech, and large language models. It provides modular, reusable "neural modules" and pretrained checkpoints that developers fine-tune for domain-specific conversational applications (chatbots, voice assistants, transcription pipelines), and it integrates with NVIDIA's broader deployment stack (Triton, TensorRT) for production serving.
The distractors each target a different NVIDIA SDK's actual domain: NVIDIA Metropolis (A) is a platform for vision AI and intelligent video analytics (smart cities, retail analytics), not conversational AI. NVIDIA DeepStream (C) is a streaming analytics SDK for building GPU-accelerated video and audio processing pipelines, primarily targeting perception tasks rather than conversational model training. NVIDIA Clara (D) is a healthcare-specific application framework for medical imaging and genomics AI, unrelated to conversational AI development.
It's worth distinguishing NeMo from Riva: NeMo is the training/customization framework, while Riva is the corresponding deployment SDK optimized for low-latency, production speech and conversational AI inference. Exam questions sometimes probe this NeMo-versus-Riva distinction directly, so treat "build/train
/customize" as the NeMo signal and "deploy/production/low-latency" as the Riva signal.
Reference: Software Development and Engineering domain - NVIDIA NeMo framework; adjacent SDKs (Metropolis, DeepStream, Clara, Riva).
NEW QUESTION # 40
You are building a generative AI model that creates realistic product designs based on textual descriptions and a reference image depicting a similar, but not identical, product. You are using a Variational Autoencoder (VAE) architecture. However, the generated images lack the fine-grained details present in the reference image. Which of the following methods would be most suitable to incorporate fine-grained details from the reference image into the generated design?
Answer: A
Explanation:
Skip connections are designed to pass information from earlier layers to later layers, preserving fine-grained details. In this multimodal scenario, skip connections from the reference image encoder allow the generator to directly access and utilize those details. Increasing latent space dimensionality might capture more information, but doesn't guarantee fine-grained detail transfer. Larger kernels can blur details. Reducing batch size helps with generalization but doesn't directly address detail transfer. GANs might generate sharper images, but lack the explicit control over detail transfer that skip connections provide.
NEW QUESTION # 41
You are fine-tuning a large pre-trained language model for a specific downstream task using a limited amount of training dat a. Which of the following techniques is MOST likely to prevent overfitting and improve the model's generalization performance?
Answer: A
Explanation:
Overfitting occurs when a model learns the training data too well and fails to generalize to unseen data. Aggressive weight decay and dropout are regularization techniques that penalize complex models and prevent them from memorizing the training data. Training from scratch with limited data will almost certainly lead to overfitting. A large learning rate can also exacerbate overfitting. While a larger batch size can improve training efficiency, it doesn't directly address overfitting.
NEW QUESTION # 42
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