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| Section | Weight | Objectives |
|---|---|---|
| Multimodal Data | 15% | - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation - Multimodal model architectures and integration |
| Performance Optimization | 10% | - Scalability and deployment considerations - Hardware acceleration with NVIDIA platforms - Model efficiency and inference optimization |
| Software Development and Engineering | 15% | - Libraries, frameworks, and tools for multimodal AI - Development workflows for generative AI applications - Best practices for building and maintaining systems |
| Trustworthy AI | 5% | - Reliability, fairness, and safety in generative systems - Ethical considerations and responsible use - Robustness and error mitigation |
| Core Machine Learning and AI Knowledge | 20% | - Fundamental concepts of machine learning and deep learning - Generative AI principles and techniques - Neural network architectures relevant to multimodal systems |
| Data Analysis and Visualization | 10% | - Interpretation of generative AI outputs - Visualization techniques for model behavior and results - Analyzing multimodal datasets and outputs |
| Experimentation | 25% | - Metrics and validation strategies for generative models - Model training, fine-tuning, and evaluation - Experiment design and methodology |
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NEW QUESTION # 21
What advantage does multimodal learning have over unimodal learning?
Answer: B
Explanation:
Multimodal learning's principal advantage is access to complementary and, at times, redundant information across modalities that a single modality alone cannot provide - enabling the model to capture richer, more nuanced patterns and relationships. A sentiment analysis system that sees only text misses tone-of-voice cues available in audio and facial expression cues available in video; combining all three lets the model resolve ambiguity that any single modality would leave unresolved (sarcasm detected via mismatched text sentiment and vocal tone, for instance). This complementarity is the substantive, well-evidenced advantage of multimodal approaches in the research literature.
The other options overstate or misstate multimodal learning's properties: it does not inherently require fewer data samples (A) - in fact, multimodal models often require more data to learn reliable cross-modal correspondences, and can be more data-hungry in practice, particularly during pretraining. Reliability (C) is not an inherent, guaranteed property; multimodal systems introduce new failure modes, such as sensitivity to missing or corrupted modalities and to modality imbalance, that must be explicitly engineered against - reliability is not automatic. Multimodal data is also not inherently easier to collect (D); acquiring synchronized, aligned data across multiple modalities (e.g., paired audio-video-text with accurate timestamps) is typically harder and more resource-intensive than collecting a single modality.
Reference: Multimodal Data domain - complementarity of modalities, richer pattern capture.
NEW QUESTION # 22
You're training a model to generate code snippets from natural language descriptions. You are using a Transformer architecture and a large dataset of code examples. You notice the model frequently generates syntactically correct code, but the code doesn't accurately implement the described functionality (i.e., it's semantically incorrect). Select TWO methods which could improve the semantic correctness of the generated code.
Answer: A,E
Explanation:
Fine-tuning with unit tests (B) and using reinforcement learning with unit test rewards (D) directly address semantic correctness- The model learns to generate code that not only compiles but also behaves as expected- Increasing Transformer layers (A) or vocabulary size (E) might improve syntactic correctness or fluency, but don't guarantee semantic accuracy Beam search (C) generates more diverse outputs, but doesn't explicitly guide the model towards semantic correctness.
NEW QUESTION # 23
Which of the following techniques can be used to reduce the computational cost and memory footprint of large language models (LLMs) during inference?
Answer: B,C,E
Explanation:
Quantization reduces the precision of the model's weights and activations, which can significantly reduce memory footprint and speed up inference. Knowledge distillation involves training a smaller 'student' model to mimic the behavior of a larger 'teacher' model, reducing the computational cost. Pruning removes unimportant connections (weights) from the model, leading to a sparser network and lower computational requirements. Increasing the model size or adding more layers would increase the computational cost.
NEW QUESTION # 24
Consider the following code snippet used within a U-Net architecture. What is its purpose?
torch.cat ([up, skip], dim=1)
Answer: D
Explanation:
The 'torch.cat([up, skip], dim=1) function concatenates two tensors, 'up' and 'skip' , along the channel dimension (dim=1) In the context of a U-Net, 'up' represents the upsampled feature map from the decoder path, and 'skip' represents the corresponding feature map from the encoder path. Concatenating them allows the decoder to combine both coarse-grained and fine-grained information for better image reconstruction.
NEW QUESTION # 25
Which framework is used for conversational AI models development?
Answer: D
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 # 26
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