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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Core ML & AI Knowledge | 20% | - Key algorithms and techniques - Basic concepts and terminology |
| Topic 2: Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
| Topic 3: Software Development & Engineering | 15% | - Python libraries for multimodal AI - Integration and deployment of multimodal AI systems |
| Topic 4: Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Topic 5: Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Topic 6: Performance Optimization | 10% | - Techniques for optimizing AI performance - Monitoring and improving system efficiency |
| Topic 7: Experimentation | 25% | - Experimental design - Hypothesis testing - Model evaluation and comparison - A/B testing |
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NEW QUESTION # 46
What is the purpose of a kernel in a Convolutional Neural Network (CNN)?
Answer: C
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 # 47
You are building a system that uses both video and text to determine the sentiment of movie reviews. You notice that while your system works great on the training set, the performance is much worse on the validation set. What is the MOST likely reason for this and what methods can you use to improve the performance?
Answer: B,C
Explanation:
The most likely reason is that the data is overfitting and the model is not able to properly generalize to new data. Overfitting causes performance in the training set to be great but performance in the validation set to be poor. Regularization techniques (such as dropout, Ll or L2) can reduce this effect. The other likely reason is that the training data is not representative enough of the real world, as the data might not be realistic, too synthetic, or missing real world information.
NEW QUESTION # 48
Consider a system that generates captions for images, and a key metric is BLEU score. You observe that while the BLEU score is high, the generated captions often lack detailed descriptions of the objects and relationships within the image. Which of the following strategies would you employ to improve the descriptive richness of the generated captions?
Answer: C
Explanation:
Reinforcement Learning with reward functions like CIDEr or SPICE directly optimizes for metrics that correlate with human judgments of caption quality, including detail and descriptive richness. Increasing beam size (A) can improve fluency but doesn't guarantee more detail. Minimizing cross-entropy (B) focuses on matching ground truth captions, which may not always be the most descriptive. Reducing vocabulary size (D) would limit the model's ability to generate detailed descriptions. Early stopping based solely on BLEU (E) might lead to premature convergence on captions that score well on BLEU but lack detail.
NEW QUESTION # 49
When evaluating a multimodal generative model, which of the following metrics is MOST suitable for assessing the coherence and consistency between the generated image and its corresponding text description?
Answer: B
Explanation:
The CLIP score directly measures the similarity between image and text embeddings in a joint multimodal space. It's designed to assess how well the generated image aligns with its text description. IS and FID focus solely on image quality. BLEU and perplexity focus solely on text quality. Therefore, only CLIP is a multimodal coherence metric.
NEW QUESTION # 50
You are working on a project that involves training a large language model (LLM) on a massive dataset of text and code. You have limited GPU memory and need to optimize the training process. Which of the following techniques would be MOST effective in reducing memory consumption during training?
Answer: D
Explanation:
Gradient accumulation allows you to simulate a larger batch size without increasing memory consumption by accumulating gradients over multiple smaller batches. Mixed-precision training reduces the memory footprint of weights and activations. Increasing the batch size or using higher precision data types increases memory consumption. Using a smaller learning rate doesn't directly affect memory. More layers will also increase memory usage.
NEW QUESTION # 51
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