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| Section | Objectives |
|---|---|
| NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
| Multimodal AI Systems | - Multimodal model design - Cross-modal learning
|
| Responsible and Trustworthy AI | - Ethical AI principles - Bias and safety considerations |
| Generative AI Concepts | - Generative models
|
| Core AI and Machine Learning Fundamentals | - Machine learning basics
|
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NEW QUESTION # 47
You're training a conditional GAN to generate images of birds based on text descriptions. The GAN generates images, but they lack fine- grained details and often have artifacts. Which of the following techniques are MOST likely to improve the quality and realism of the generated images? (Select TWO)
Answer: B,D
Explanation:
Spectral normalization helps stabilize training by limiting the Lipschitz constant of the discriminator and generator, preventing exploding gradients and improving image quality. A deeper and wider generator network can capture more complex image features and generate more detailed images. A simple MLP wouldn't be suitable for generating high-resolution images. Reducing the input noise vector size might limit the diversity of generated images. A more powerful discriminator helps in better distinguishing between real and fake images, which guides the generator to produce more realistic outputs. However, spectral normalization directly addresses stability issues that cause artifacts.
NEW QUESTION # 48
Which of the following is a disadvantage of the ReLU activation function?
Answer: B
Explanation:
Reviewer note: Marked answer (C) is factually incorrect - ReLU is well suited to deep networks and specifically helps mitigate vanishing gradients. The genuine, well-established disadvantage is the 'dying ReLU' problem (D).
I need to flag this one as well: the marked answer (C) does not hold up, and stating otherwise would misrepresent a fairly foundational deep learning fact. ReLU (Rectified Linear Unit, f(x) = max(0, x)) is, if anything, particularly well suited to deep neural networks - it was widely adopted specifically *because* it mitigates the vanishing gradient problem that plagued earlier activation functions like sigmoid and tanh in deep architectures: ReLU's gradient is a constant 1 for all positive inputs, rather than the saturating, near-zero gradients that sigmoid/tanh produce for large-magnitude inputs, which allows gradients to propagate more effectively through many layers.
The genuine, well-documented disadvantage of ReLU is option D: the "dying ReLU" problem. Because ReLU's gradient is exactly zero for any negative input, a neuron whose weighted input becomes consistently negative - often due to a large negative gradient update or an unfavorable initialization - will always output zero and will never receive a gradient large enough to recover, effectively "dying" and no longer contributing to learning. This is a real, practically significant issue that motivated variants like Leaky ReLU, Parametric ReLU (PReLU), and ELU, which allow a small non-zero gradient for negative inputs specifically to prevent neurons from dying.
Options A and B are also factually incorrect characterizations of ReLU - it is computationally cheap (a simple thresholding operation, part of its original appeal over sigmoid/tanh) and it specifically helps *avoid* vanishing gradients rather than causing them.
NEW QUESTION # 49
You are working on a project to classify images of different types of flowers. You have a relatively small dataset (around 500 images per class). Which of the following techniques would be the MOST effective to improve the performance of your image classifier, considering the limited data?
Answer: D
Explanation:
Transfer learning, specifically fine-tuning a pre-trained model, is highly effective when dealing with small datasets. Pre-trained models have already learned useful features from large datasets, and fine-tuning them allows the model to adapt to the specific characteristics of your flower dataset. Training a deep network from scratch with limited data will likely lead to overfitting. Data augmentation helps, but transfer learning is generally more impactful. Reducing image resolution might lose important details, and a linear classifier might be too simple to capture the complexity of image features.
NEW QUESTION # 50
You are tasked with deploying a generative A1 model for image inpainting using Triton Inference Server. The model requires significant GPU memory and you want to maximize throughput. Which Triton configuration parameters would be MOST important to tune, and why?
Answer: B
Explanation:
'instance_group' with 'KIND_GPIY assigns the model to specific GPUs. Increasing (B) leverages GPU parallelism. Enabling 'dynamic_batching' and setting (C) allows Triton to dynamically batch requests to maximize throughput. Model warmup reduces first request latency. (A) is incomplete (missing KIND_GPU). (D) is relevant for latency optimization but not as crucial for throughput in a memory-constrained scenario. Therefore both B and C are most crucial in optimizing throughput while dealing with memory constraint.
NEW QUESTION # 51
What role does 'late fusion' play in multimodal machine learning?
Answer: C
Explanation:
Late fusion trains separate, independent models for each modality - each producing its own prediction, score, or decision - and combines those outputs only at the final decision stage, typically via averaging, weighted voting, a learned meta-classifier (stacking), or simple rule-based aggregation. This is the direct counterpart to early fusion (combination at the raw/feature input level, tested elsewhere in this set) and to intermediate/hybrid fusion (combination at one or more mid-network representation levels).
Late fusion's key practical advantage is modularity and robustness: because each modality's model operates independently until the final combination step, a missing or corrupted modality at inference time degrades performance gracefully rather than catastrophically - the surviving modalities' models can still contribute a prediction. It also allows each modality-specific model to be trained, validated, and even updated independently, which simplifies engineering in production systems. Its main disadvantage is that it cannot capture fine-grained, low-level cross-modal interactions, since by the time information reaches the fusion point, each modality has already been reduced to a high-level decision.
Options C and D describe feature-level and preprocessing-level combination respectively - both inconsistent with "late" in the fusion terminology, which specifically denotes the decision/output stage. Option B describes a training-schedule concept unrelated to fusion architecture.
Reference: Multimodal Data domain - late fusion vs. early/hybrid fusion, decision-level combination.
NEW QUESTION # 52
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