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| Section | Objectives |
|---|---|
| Multimodal AI Systems | - Cross-modal learning
|
| Core AI and Machine Learning Fundamentals | - Machine learning basics
|
| Responsible and Trustworthy AI | - Ethical AI principles - Bias and safety considerations |
| NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
| Generative AI Concepts | - Generative models
|
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NEW QUESTION # 55
You are working on a project that involves generating realistic images from text descriptions using a diffusion model. You want to reduce the inference time of the model, which currently takes several minutes to generate a single image. Which of the following techniques would be MOST effective for accelerating inference without significantly compromising image quality?
Answer: A
Explanation:
DDIM and progressive distillation are specifically designed to reduce the number of sampling steps needed in diffusion models, leading to faster inference. Increasing diffusion steps (A) would increase inference time. Smaller batch size (B) might reduce memory usage, but not significantly affect overall inference time. Training with a larger dataset (D) improves quality, but not inference speed. Switching to CPU (E) would dramatically slow down inference.
NEW QUESTION # 56
You are working on a Generative A1 project that involves analyzing text dat a. You've noticed that certain words are appearing much more frequently than others, potentially skewing your results. Which of the following techniques would be MOST effective in addressing this issue?
Answer: B
Explanation:
Removing stop words eliminates common words like 'the,' 'a,' 'is,' which often dominate text data. TF-IDF then weights words based on their frequency in a document relative to their frequency across all documents, reducing the impact of common words and highlighting important terms. Options B and C are helpful but not sufficient. Option D can actually worsen the issue. Option E addresses semantic meaning, not frequency imbalance.
NEW QUESTION # 57
You are tasked with building a Generative A1 model to generate realistic images of outdoor scenes. The training dataset contains a large number of images with varying lighting conditions, weather conditions, and object compositions. Which data augmentation techniques would be MOST effective in improving the model's robustness and generalization ability?
Answer: E
Explanation:
Color jittering, adding Gaussian noise, and random perspective transformations simulate variations in lighting, noise, and camera angles, making the model more robust to different real-world scenarios. Random cropping and resizing are also helpful, but less impactful than changes in the color space and noise. Horizontal flipping can be useful but is limited. A fixed rotation or Vertical flipping is not a general augmentation and might harm the model's performance.
NEW QUESTION # 58
What characteristic of autoencoders makes them suitable for anomaly detection?
Answer: B
Explanation:
An autoencoder learns to compress input data into a lower-dimensional latent (bottleneck) representation via its encoder, then reconstruct the original input from that representation via its decoder, trained by minimizing reconstruction error on normal data. Because the model is optimized specifically to reconstruct patterns it has seen frequently during training, it becomes proficient at compressing and reconstructing "normal" instances but performs poorly - producing high reconstruction error - on inputs that deviate structurally from the training distribution, i.e., anomalies. Thresholding reconstruction error thus provides a natural, unsupervised anomaly score without requiring labeled anomalous examples, which are often scarce or unavailable in real- world settings.
This mechanism is the operative characteristic tested here, not classification accuracy (B, which describes a supervised discriminative task the autoencoder is not directly trained for), image enhancement (C, a description closer to denoising autoencoders' side effect rather than the core anomaly-detection mechanism), or forecasting (D, which describes sequence models like RNNs/LSTMs applied to time series, a different architecture family and objective).
Variants such as variational autoencoders (VAEs) extend this idea probabilistically, and in multimodal settings, cross-modal autoencoders can flag anomalies where reconstruction fails to reconcile one modality given another.
Reference: Core Machine Learning and AI Knowledge domain - autoencoders, latent representations, reconstruction-error-based anomaly detection.
NEW QUESTION # 59
You are working on a sequence-to-sequence model for neural machine translation. You've implemented an attention mechanism, but the model is still struggling with long sentences, often losing context in the later parts of the translation. Which type of attention mechanism is most likely to alleviate this issue effectively?
Answer: D
Explanation:
Multi-Head Attention allows the model to attend to different parts of the input sequence with different learned linear projections, capturing richer relationships and improving performance on long sequences compared to single-head attention mechanisms- While other attention mechanisms are valuable, multi- head attention offers the most robust solution for long-range dependencies.
NEW QUESTION # 60
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