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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Trustworthy AI | 5% | - Reliability, fairness, and safety in generative systems - Ethical considerations and responsible use - Robustness and error mitigation |
| Topic 2: Multimodal Data | 15% | - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation - Multimodal model architectures and integration |
| Topic 3: Software Development and Engineering | 15% | - Libraries, frameworks, and tools for multimodal AI - Best practices for building and maintaining systems - Development workflows for generative AI applications |
| Topic 4: Data Analysis and Visualization | 10% | - Analyzing multimodal datasets and outputs - Interpretation of generative AI outputs - Visualization techniques for model behavior and results |
| Topic 5: Performance Optimization | 10% | - Hardware acceleration with NVIDIA platforms - Scalability and deployment considerations - Model efficiency and inference optimization |
| Topic 6: Core Machine Learning and AI Knowledge | 20% | - Generative AI principles and techniques - Fundamental concepts of machine learning and deep learning - Neural network architectures relevant to multimodal systems |
| Topic 7: Experimentation | 25% | - Metrics and validation strategies for generative models - Model training, fine-tuning, and evaluation - Experiment design and methodology |
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NEW QUESTION # 15
You are working on a Generative A1 Multimodal model that takes text and audio as input and generates a video. During training, you observe that the generated videos often lack coherence with the input text. What are the potential issues you would investigate? (Select THREE)
Answer: A,C,E
Explanation:
Insufficient regularization can cause overfitting and lack of generalization, leading to incoherence. A weak conditioning mechanism means the model isn't effectively using the input text to guide the video generation. A lack of diverse training examples limits the model's ability to learn the relationships between text, audio, and video. A too-powerful discriminator can lead to mode collapse, but primarily affects diversity, not necessarily coherence directly. Input audio loudness is a preprocessing issue, not a fundamental architectural problem.
NEW QUESTION # 16
What does 'kernel fusion' refer to in the context of AI model optimization?
Answer: C
Explanation:
In GPU computing, "kernel" refers to a compiled function launched on the GPU to execute a specific operation (e.g., a matrix multiplication or an activation function). Executing a sequence of such operations naively launches a separate kernel for each one, incurring per-launch overhead (kernel launch latency) and requiring intermediate results to be written to and read back from GPU global memory between each operation - both of which waste time and memory bandwidth relative to the actual compute being performed. Kernel fusion combines multiple sequential operations into a single compiled kernel, so intermediate results stay in fast on-chip registers or shared memory rather than round-tripping through global memory, and only one kernel launch is needed instead of several. This reduces both launch overhead and memory-bandwidth-bound latency, which is often the dominant bottleneck for smaller operations on modern GPUs. NVIDIA's TensorRT applies kernel fusion (alongside quantization and precision calibration) as one of its core inference-optimization techniques, commonly fusing operations like convolution + bias + activation into a single kernel.
Option A describes pruning, a distinct technique covered elsewhere in this domain - reducing parameter count, not combining kernel launches. Option C misapplies "kernel" in the CNN-filter sense rather than the GPU-execution sense the question is asking about, and layering more kernels would not describe fusion at all.
Option D conflates kernel functions (as in kernel methods for SVMs) with GPU kernels - an unrelated use of the same term.
Reference: Performance Optimization domain - kernel fusion, TensorRT, GPU execution optimization.
NEW QUESTION # 17
Which technique is commonly used to speed up AI model training and inference on hardware accelerators?
Answer: B
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 # 18
You are building a multimodal AI system that generates 3D models of furniture from text descriptions and a few 2D images of similar furniture pieces. The system uses separate encoders for text and images. You want to fuse the information from both modalities effectively. Which TWO of the following fusion techniques would be the most appropriate for this task, considering the different nature of the text and image data?
Answer: A,B
Explanation:
Cross-attention (C) allows the model to dynamically learn the relationships between the text and image, highlighting relevant features from each modality- A gating mechanism (D) provides a learned way to control the contribution of each modality allowing the model to prioritize the more informative input Simple concatenation (A), addition (B), or averaging (E) are less sophisticated and might not effectively capture the complex interactions between the modalities.
NEW QUESTION # 19
You are working with time-series data from IoT sensors alongside video footage from surveillance cameras to detect anomalies in a factory production line. What data preprocessing steps are crucial for effectively integrating and analyzing these modalities in a multimodal AI model?
Answer: E
Explanation:
All the mentioned steps are crucial. Synchronizing timestamps is essential for temporal alignment. Normalizing time-series data ensures features are on the same scale, preventing bias. Downsampling video reduces computational burden, and grayscale conversion simplifies feature extraction without losing vital information for anomaly detection.
NEW QUESTION # 20
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