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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
| Topic 2: Software Development & Engineering | 15% | - Integration and deployment of multimodal AI systems - Python libraries for multimodal AI |
| Topic 3: Trustworthy AI | 5% | - Ethical considerations in AI development - Ensuring fairness and transparency |
| Topic 4: Experimentation | 25% | - Experimental design - Model evaluation and comparison - A/B testing - Hypothesis testing |
| Topic 5: Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Topic 6: Performance Optimization | 10% | - Techniques for optimizing AI performance - Monitoring and improving system efficiency |
| Topic 7: Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
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NEW QUESTION # 51
What does 'kernel fusion' refer to in the context of AI model optimization?
Answer: B
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 # 52
You have developed a multimodal model that uses both audio and video data to detect human emotions. During testing, you observe that the model performs exceptionally well on controlled lab recordings but poorly in real-world scenarios with background noise and varying lighting conditions. What technique would be MOST effective in improving the model's generalization ability to real-world data?
Answer: E
Explanation:
Data augmentation is the most effective way to improve a model's generalization ability to real-world data. By adding noise to the audio, simulating different lighting conditions for the video, we can create a more diverse training dataset that is more representative of the real world. Also leveraging pre-trained audio and video models helps to leverage the knowledge learned on large datasets.
NEW QUESTION # 53
You are evaluating a multimodal model that generates descriptions for video clips. You have human ratings for the relevance, fluency, and coherence of the generated descriptions. Which statistical test is MOST appropriate for determining if there is a statistically significant difference in the median ratings for each of these criteria (relevance, fluency, coherence) between two different versions of your model?
Answer: B
Explanation:
Since you're interested in comparing the medians of the ratings and not assuming a normal distribution (which is often the case with subjective human ratings), a non-parametric test is more appropriate than a t-test or ANOVA. The Mann-Whitney U test (also known as the Wilcoxon rank-sum test) is used to compare the medians of two independent groups. The Kruskal-Wallis test is used when you have more than two groups.
NEW QUESTION # 54
You are developing a multimodal system for medical diagnosis that integrates patient history (text), X-ray images, and heart rate data (time-series). A significant portion of the heart rate data is missing due to sensor failures. What is the MOST appropriate method to handle this missing data to ensure the model's accuracy and prevent bias?
Answer: A
Explanation:
Imputation using time-series techniques (C) is the most suitable method as it leverages the temporal dependencies within the heart rate data to estimate missing values, minimizing bias and preserving the integrity of the data. Mean imputation or arbitrary value assignment can introduce significant bias, and removing records reduces the dataset size.
NEW QUESTION # 55
You are experimenting with a multimodal model that takes both text and audio as input. During evaluation, you notice that the model is heavily biased towards the text input, largely ignoring the audio. Which of the following techniques could you employ to mitigate this modality imbalance and encourage the model to effectively utilize both inputs? (Select all that apply)
Answer: C,E
Explanation:
Modality imbalance is a common issue in multimodal learning. Applying modality-specific dropout to the dominant modality (text, in this case) forces the model to rely more on the other modality (audio). A contrastive loss directly encourages the model to learn aligned representations between the two modalities. Increasing the audio encoder's learning rate (A) might help, but it is less targeted than dropout or contrastive loss. Reducing the text encoder size (D) is unlikely to be helpful in a controlled way. Replacing Audio features with raw waveform might introduce noise.
NEW QUESTION # 56
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