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| Section | Objectives |
|---|---|
| Topic 1: Core AI and Machine Learning Fundamentals | - Machine learning basics
|
| Topic 2: NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
| Topic 3: Generative AI Concepts | - Generative models
|
| Topic 4: Multimodal AI Systems | - Cross-modal learning
|
| Topic 5: Responsible and Trustworthy AI | - Bias and safety considerations - Ethical AI principles |
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NEW QUESTION # 20
Which of the following statements are TRUE regarding the challenges of training multimodal machine learning models? (Select TWO)
Answer: B,D
Explanation:
Multimodal models are harder to train than unimodal ones. Missing modality data requires specific handling. Different temporal resolutions need alignment. Overfitting remains a concern. Open source availability is improving, but multimodal specific support remains more niche than unimodal.
NEW QUESTION # 21
You are developing a GenAI-Multimodal system that uses data from various sources. What is one potential issue you need to consider in relation to bias in data?
Answer: D
Explanation:
Representativeness bias occurs when a training dataset systematically over- or under-samples subpopulations relative to the population the deployed system will actually encounter - for example, a facial recognition dataset skewed toward lighter-skinned faces, or a multimodal medical dataset drawn predominantly from one demographic group. Because models learn statistical patterns from their training distribution, an unrepresentative dataset produces a model whose accuracy, calibration, and fairness properties degrade for underrepresented groups, even when aggregate accuracy metrics look acceptable.
This is precisely why aggregate accuracy is an insufficient safeguard: option B's framing - that bias doesn't matter "as long as predictions are accurate" - conflates overall accuracy with subgroup accuracy, and a model can post strong aggregate numbers while systematically failing specific populations. Option D is factually false; AI systems have no inherent neutrality - they inherit and can amplify whatever patterns (including societal biases) exist in their training data and objective function. Option C is also incorrect:
mitigating representativeness bias is significantly cheaper and more effective when addressed at the data- collection and curation stage - through stratified sampling, bias audits, and diverse data sourcing - than after deployment, when it becomes a retraining and remediation problem, and by then real-world harm may have already occurred.
Reference: Trustworthy AI domain - data representativeness, fairness, bias identification and mitigation.
NEW QUESTION # 22
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,B,C,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 # 23
You are working on a project that involves generating music from video. The approach uses a pre-trained video encoder and a pre- trained music decoder. You find that the generated music often lacks a clear connection to the visual content of the video. To improve the coherence between the video and the generated music, which of the following steps would be the MOST effective? (Select TWO)
Answer: A,E
Explanation:
Cross-modal attention (B) allows the model to explicitly learn relationships between visual and musical features. End-to-end fine- tuning with a suitable loss function (D) encourages the model to learn a coherent mapping between the two modalities. Training the encoders and decoders separately won't necessarily improve coherence. Generating from random noise defeats the purpose. Limiting video length may reduce complexity but doesn't fundamentally address the alignment problem.
NEW QUESTION # 24
Which technique is commonly used to speed up AI model training and inference on hardware accelerators?
Answer: D
Explanation:
Quantization reduces the numerical precision used to represent model weights and activations - for example, converting FP32 weights to INT8 - which decreases memory bandwidth requirements and allows hardware accelerators (GPU Tensor Cores, dedicated INT8 inference engines) to execute more operations per cycle, directly speeding up both training (in its mixed-precision form) and, especially, inference. Post-training quantization and quantization-aware training are the two dominant approaches, with the latter simulating quantization effects during training to better preserve accuracy at reduced bit-widths. NVIDIA's TensorRT relies heavily on quantization (alongside layer fusion and kernel auto-tuning) to accelerate deployed inference.
The distractors describe techniques that serve entirely different purposes: data augmentation (B) increases training data diversity to improve generalization, not computational speed - it typically adds preprocessing overhead rather than reducing it. Model enlargement (C) does the opposite of speeding up computation; larger models require more FLOPS and memory, increasing latency. Dropout (D) is a regularization technique applied during training to prevent overfitting by randomly zeroing activations - it has no role in inference- time speed (and is typically disabled at inference) and does not meaningfully accelerate training compute either.
Quantization is frequently paired with pruning and kernel/operator fusion as the three core techniques for hardware-accelerated performance optimization.
Reference: Performance Optimization domain - quantization, TensorRT, inference acceleration techniques.
NEW QUESTION # 25
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