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| Section | Objectives |
|---|---|
| Topic 1: Multimodal AI Systems | - Multimodal model design - Cross-modal learning
|
| Topic 2: Responsible and Trustworthy AI | - Ethical AI principles - Bias and safety considerations |
| 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 # 38
Which of the following tasks can be performed using the transformer LLM encoder model?
Answer: A
Explanation:
Encoder-only transformer architectures (BERT and its derivatives being the canonical examples) process an entire input sequence bidirectionally, attending to both left and right context simultaneously, and output contextualized representations for each token - well suited to understanding tasks that require a rich representation of input meaning without generating new sequential output. Semantic analysis - including sentiment classification, named entity recognition, semantic similarity, and text classification more broadly - is a natural fit, typically implemented by taking the encoder's output representation (often a special [CLS] token embedding) and feeding it to a lightweight classification head.
The other options require architectures the encoder-only design is not built for: generating code (B) and open- ended text generation generally require autoregressive, causal (decoder-only or encoder-decoder) architectures that predict tokens sequentially, since encoder-only models produce a fixed set of contextualized representations rather than generating novel token sequences left-to-right. Image generation (C) requires an entirely different model family (diffusion models, GANs, or autoregressive image models), unrelated to text- encoder architectures. Speech recognition (D) requires an audio-processing pipeline - typically a CTC-based or encoder-decoder acoustic model operating on spectrogram or waveform input - not a text-only transformer encoder.
Reference: Core Machine Learning and AI Knowledge domain - transformer encoder vs. decoder vs.
encoder-decoder architectures and their respective task fits.
NEW QUESTION # 39
In the transformer architecture, what is the purpose of positional encoding?
Answer: D
Explanation:
Unlike recurrent architectures, which process tokens sequentially and thereby inherently encode order through the sequence of computation, the transformer's self-attention mechanism processes all tokens in parallel and is permutation-invariant by construction - attention scores between tokens do not inherently depend on their position in the sequence. Positional encoding solves this by injecting explicit information about each token's position into its input representation, typically by adding a positional vector (computed via fixed sinusoidal functions in the original "Attention Is All You Need" formulation, or learned as trainable embeddings in many modern variants) to the token's embedding before it enters the attention layers. Without this, "the cat sat on the mat" and "the mat sat on the cat" would be indistinguishable to the self-attention mechanism, since the same set of token embeddings would be processed identically regardless of order.
Semantic meaning (option A) is the role of the token embeddings themselves, learned separately from positional information - the two are combined (typically summed) but serve distinct purposes. Positional encoding does not remove information (C); it adds it. And while attention weights do effectively encode a learned notion of token importance relative to a query (option D), that importance-weighting mechanism is a separate, downstream function of the attention layers, not the role of positional encoding itself, which only supplies order information as an input feature.
Reference: Core Machine Learning and AI Knowledge domain - transformer architecture, self-attention, positional encoding.
NEW QUESTION # 40
Which of the following best describes the purpose of GAN (Generative Adversarial Networks)?
Answer: C
Explanation:
A GAN consists of two networks trained in an adversarial minimax game: a generator that learns to produce synthetic samples from random noise, and a discriminator that learns to distinguish those generated samples from real training data. As training progresses, the generator improves at producing increasingly realistic samples in an attempt to fool the discriminator, while the discriminator improves at detecting fakes - at convergence (ideally), the generator produces samples statistically indistinguishable from the real training distribution. This generative objective - producing new, realistic data resembling the training distribution - is GAN's defining purpose, applied to images, audio, tabular data, and other domains.
The remaining options describe fundamentally different task categories that GANs are not designed for:
decision optimization based on historical data (B) describes reinforcement learning or classical decision- theoretic optimization, not generative modeling. Classification and categorization (C) is a discriminative task
- GANs' discriminator component performs a real-vs-fake discrimination internally as a training mechanism, but the discriminator is not GAN's end product or purpose; the generator is. Search algorithm optimization (D) is an information-retrieval concern entirely outside generative modeling's scope.
GANs are one of several generative model families tested in this domain alongside diffusion models (which power the U-Net-based denoising questions elsewhere in this set) and variational autoencoders, each with different training dynamics and stability characteristics.
Reference: Core Machine Learning and AI Knowledge domain - GANs, generative modeling.
NEW QUESTION # 41
You are fine-tuning a large pre-trained language model for a specific downstream task. During training, you observe that the model performs well on the training data but generalizes poorly to the validation dat a. Which of the following strategies could help improve the model's generalization performance?
Answer: A,C,D,E
Explanation:
Decreasing the learning rate can help the model to converge to a better solution and avoid overfitting. Increasing the training data size provides the model with more examples to learn from, improving generalization. Early stopping prevents the model from training for too long and overfitting the training data. Increasing weight decay adds more regularization, preventing the model from learning overly complex patterns. Increasing the learning rate might worsen overfitting.
NEW QUESTION # 42
You're building a system that generates images from text descriptions, incorporating spatial relationships. For instance, the text 'A red ball is to the left of a blue cube' should result in an image where the red ball is actually positioned to the left of the blue cube. Which of the following approaches would be MOST suitable for encoding and utilizing spatial information in this text-to-image generation process?
Answer: D
Explanation:
Explicitly encoding spatial relations in the text encoder is the most direct way to ensure the model understands and utilizes this information during image generation. Standard Transformers might learn some spatial relationships implicitly, but augmenting them with spatial embeddings makes the process more robust and controllable. Relying solely on the decoder or using bag-of-words ignores crucial spatial information. Penalizing the model after generation is inefficient; we want to guide the generation process itself.
NEW QUESTION # 43
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