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| Section | Objectives |
|---|---|
| Core AI and Machine Learning Fundamentals | - Machine learning basics
|
| NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
| Multimodal AI Systems | - Multimodal model design - Cross-modal learning
|
| Responsible and Trustworthy AI | - Bias and safety considerations - Ethical AI principles |
| Generative AI Concepts | - Generative models
|
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NEW QUESTION # 17
Which metric is commonly used for evaluating Automatic Speech Recognition (ASR) models?
Answer: B
Explanation:
Word Error Rate is the standard evaluation metric for ASR systems. It measures the edit distance between the model's transcription and a human reference transcript, computed as (Substitutions + Deletions + Insertions) / Number of reference words, expressed as a percentage. Lower WER indicates better transcription accuracy.
Its character-level analogue, Character Error Rate (CER), is used for languages without clear word boundaries or for morphologically complex languages.
The distractors target common confusions: CTC (Connectionist Temporal Classification) Loss (A) is a
*training* objective used to align variable-length audio input with variable-length text output in ASR models like DeepSpeech - it optimizes the model but is not itself a post-hoc evaluation metric on held-out accuracy.
F1 Score (B) evaluates classification tasks with defined positive/negative classes, such as keyword spotting or wake-word detection, not full transcription. Mean Opinion Score (C) is a subjective, human-rated metric used to evaluate speech *synthesis* quality (TTS) or perceived audio naturalness - the inverse task of ASR - not transcription accuracy.
On NVIDIA's Riva and NeMo ASR pipelines, WER is the benchmark reported against datasets like LibriSpeech, and it remains the figure typically referenced in the exam's Multimodal Data and Experimentation domains when discussing speech model evaluation.
Reference: Multimodal Data domain - ASR evaluation metrics (WER, CER) vs. TTS metrics (MOS).
NEW QUESTION # 18
You are training a text-to-image diffusion model and observe that the generated images often exhibit a 'washed-out' or overly smooth appearance. Which of the following adjustments to the training process would likely improve the image quality and detail?
Answer: C
Explanation:
A perceptual loss function encourages the generated images to have more realistic features and details, as it compares the high- level representations of the generated images to the real images. Increasing its weight in the training objective would incentivize the model to produce more detailed and visually appealing results. Decreasing diffusion steps leads to faster but often lower-quality results. Reducing batch size can affect training stability but doesn't directly address the 'washed-out' appearance. Data augmentation and learning rate adjustments may have some impact, but are less directly targeted at improving image detail.
NEW QUESTION # 19
Which of the following NVIDIA SDKs is most suitable for deploying a real-time, low-latency speech-to-text service as part of a multimodal AI application?
Answer: C
Explanation:
NVIDIA Riva is specifically designed for building and deploying real-time conversational A1 services, including speech-to-text, text-to- speech, and natural language understanding. Its focus is on low latency and high accuracy for conversational applications.
NEW QUESTION # 20
You're training a conditional GAN (cGAN) to generate images of handwritten digits conditioned on the digit label. You notice that the generated images are blurry and lack fine details, even after extensive training. Which of the following techniques could you implement to improve the sharpness and realism of the generated images?
Answer: C
Explanation:
Perceptual loss functions, which compare high-level features extracted by a pre-trained network, encourage the generator to create images that are more perceptually realistic and detailed. Spectral normalization helps stabilize training and can prevent the discriminator from overpowering the generator, but it doesn't directly address the lack of detail. Increasing latent space dimensionality and adding batch normalization are general techniques that might help, but perceptual loss is more targeted for improving image quality. Increasing the learning rate might destabilize training.
NEW QUESTION # 21
You are training a Variational Autoencoder (VAE) and notice that the generated samples are blurry and lack detail. Which of the following adjustments could help improve the quality and sharpness of the generated images2 Select all that apply.
Answer: A,C,D,E
Explanation:
Increasing network capacity allows the model to learn more complex representations. Decreasing the KL divergence allows the decoder to focus more on reconstruction, potentially sacrificing some disentanglement. Increasing the latent space provides more room for capturing variations in the data. Using a more powerful decoder helps in generating sharper images
NEW QUESTION # 22
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