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| Section | Weight | Objectives |
|---|---|---|
| Multimodal Data | 15% | - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation - Multimodal model architectures and integration |
| Trustworthy AI | 5% | - Robustness and error mitigation - Ethical considerations and responsible use - Reliability, fairness, and safety in generative systems |
| Data Analysis and Visualization | 10% | - Interpretation of generative AI outputs - Analyzing multimodal datasets and outputs - Visualization techniques for model behavior and results |
| Performance Optimization | 10% | - Scalability and deployment considerations - Hardware acceleration with NVIDIA platforms - Model efficiency and inference optimization |
| Core Machine Learning and AI Knowledge | 20% | - Fundamental concepts of machine learning and deep learning - Generative AI principles and techniques - Neural network architectures relevant to multimodal systems |
| Experimentation | 25% | - Experiment design and methodology - Metrics and validation strategies for generative models - Model training, fine-tuning, and evaluation |
| Software Development and Engineering | 15% | - Development workflows for generative AI applications - Libraries, frameworks, and tools for multimodal AI - Best practices for building and maintaining systems |
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NEW QUESTION # 25
You are building an A1 model that takes video and corresponding subtitles as input to generate short summaries of video content. Which of the following strategies are most important to reduce the chance of your model generating biased summaries? (Select all that apply)
Answer: B,C,D
Explanation:
Debiasing pre-trained models helps remove existing biases. A diverse training dataset is important to reduce the influence of any single biased viewpoint. Evaluating model performance on different demographic groups allows you to find and rectify performance disparities. Random data shuffling (D) and increasing training epochs (E) do not directly address bias. Note this is a very tough question as all choices seem viable but only options, A, B and C are the correct choice.
NEW QUESTION # 26
Consider the following code snippet, where you are trying to load image and text data for a multimodal model. What is the most likely cause of error if the code fails during the image loading step?
Answer: A
Explanation:
Since the error occurs specifically during the image loading step, the most likely cause is related to the image files themselves. Corrupted files or unsupported formats would prevent the image loading library from successfully reading the images. The other options are less likely to cause an error specifically during image loading.
NEW QUESTION # 27
What does 'kernel fusion' refer to in the context of AI model optimization?
Answer: D
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 # 28
You are training a conditional generative model to generate images based on text descriptions. You notice that the generated images often lack fine-grained details and tend to be blurry, even though the overall structure matches the text description. Which of the following techniques would be MOST effective in improving the image quality and adding finer details?
Answer: C
Explanation:
Perceptual loss functions, which often use features extracted from pre-trained convolutional neural networks, can guide the generator to produce images that are more visually realistic and contain finer details. They help the model capture high-level features and style information that are important for image quality. Increasing batch size, simplifying the generator, decreasing discriminator learning rate, or training for fewer epochs are unlikely to directly address the issue of missing fine-grained details.
NEW QUESTION # 29
You are tasked with fine-tuning a pre-trained multimodal model for a new task involving image and text inputs. The pre-trained model was trained on a large dataset of image-caption pairs. Which of the following strategies would be MOST effective for transfer learning in this scenario, considering computational efficiency and performance?
Answer: B
Explanation:
Option C is the most effective strategy. Fine-tuning a subset of layers allows the model to adapt to the new task while leveraging the pre-trained knowledge. Freezing the lower layers preserves the general features learned from the large dataset, while fine-tuning the feature extraction layers allows the model to learn task-specific features. Fine-tuning all layers (Option B) can lead to overfitting and is computationally expensive. Freezing all layers except the classification head (Option A) may not provide sufficient adaptation. Training from scratch (Option D) is computationally expensive and requires a large dataset. Knowledge distillation (Option E) is also a valid option but may not be the most direct approach for transfer learning when the pre-trained model's architecture is suitable.
NEW QUESTION # 30
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