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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Topic 2: Data Analysis & Visualization | 10% | - Visualization techniques for multimodal data - Data preprocessing and feature engineering |
| Topic 3: Core ML & AI Knowledge | 20% | - Key algorithms and techniques - Basic concepts and terminology |
| Topic 4: Performance Optimization | 10% | - Monitoring and improving system efficiency - Techniques for optimizing AI performance |
| Topic 5: Experimentation | 25% | - Experimental design - Model evaluation and comparison - A/B testing - Hypothesis testing |
| Topic 6: Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Topic 7: Software Development & Engineering | 15% | - Integration and deployment of multimodal AI systems - Python libraries for multimodal AI |
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NEW QUESTION # 25
You are analyzing the latent space of a GAN trained to generate images of human faces. You notice that interpolating between two points in the latent space often results in unrealistic or distorted faces. Which of the following techniques could potentially improve the smoothness and interpretability of the latent space?
Answer: C
Explanation:
Regularizing the latent space directly encourages smoothness, making interpolations more realistic. Spectral normalization in the discriminator improves training stability but doesn't directly address latent space smoothness. Increasing discriminator layers or decreasing generator learning rate might influence performance, but regularization is the most direct approach. Batch size is less impactful on latent space interpretability.
NEW QUESTION # 26
You are fine-tuning a pre-trained multimodal model for a specific task that involves generating short video clips from text prompts. The pre-trained model was trained on a large dataset of diverse videos and text descriptions. However, you observe that the fine-tuned model tends to generate video clips that are visually appealing but often deviate significantly from the meaning of the text prompts. Which of the following techniques is LEAST likely to improve the semantic consistency between the generated video clips and the text prompts?
Answer: C
Explanation:
Freezing the weights of the video encoder will prevent it from adapting to the specific nuances of the fine-tuning task, potentially hindering the model's ability to generate videos that accurately reflect the meaning of the text prompts. A lower learning rate, reinforcement learning, data augmentation, or contrastive learning are all techniques that can help improve semantic consistency.
NEW QUESTION # 27
You are training a deep convolutional generative adversarial network (DCGAN) for generating high-resolution images. After several epochs, you observe mode collapse the generator produces only a few similar images. Which of the following strategies would be most effective in mitigating mode collapse?
Answer: A
Explanation:
Feature matching encourages the generator to produce outputs that have similar statistics to real data at intermediate layers of the discriminator, preventing it from converging to a narrow set of outputs. Other options might provide marginal improvements, but feature matching directly addresses the issue of mode collapse.
NEW QUESTION # 28
You are deploying a multimodal model that uses both video and audio data for real-time emotion recognition. The model is deployed on an edge device with limited computational resources. Which optimization techniques would be MOST effective for reducing latency and improving the model's inference speed on the edge device?
Answer: E
Explanation:
Quantization to a lower precision (e.g., INT8) significantly reduces the model size and computational requirements, leading to faster inference speeds on edge devices. Pruning further reduces the model's complexity. Increasing model complexity (A) or using FP32 (B) would increase latency. Offloading to the cloud (D) introduces network latency. Increasing video resolution (E) increases the computational load.
NEW QUESTION # 29
What is the purpose of a kernel in a Convolutional Neural Network (CNN)?
Answer: A
Explanation:
A kernel (or filter) in a CNN is a small matrix of learnable weights that slides across the input (an image, feature map, or intermediate activation) computing a dot product at each spatial position - the convolution operation. Each kernel is trained to detect a specific local pattern: early-layer kernels typically learn to detect low-level features like edges and color gradients, while kernels in deeper layers combine these into detectors for more complex, higher-level patterns (textures, object parts, and eventually whole-object representations as receptive fields grow with depth). A convolutional layer typically applies many kernels in parallel, each producing its own output channel, collectively forming the layer's feature map.
The other options describe separate CNN components with distinct responsibilities: the loss function (B) is computed at the network's output based on the difference between predictions and ground truth, entirely separate from the kernel's role in feature extraction. Classification (C) is typically performed by fully connected (dense) layers - often with a softmax activation - placed after the convolutional feature- extraction stack, not by the kernels themselves. Normalization (D) is handled by dedicated layers such as batch normalization or layer normalization, inserted between convolutional layers to stabilize activations, again a separate mechanism from the convolution operation itself.
Reference: Core Machine Learning and AI Knowledge domain - CNN architecture, kernels/filters, feature extraction.
NEW QUESTION # 30
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