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| Section | Weight | Objectives |
|---|---|---|
| Core Machine Learning and AI Knowledge | 20% | - Generative AI principles and techniques - Neural network architectures relevant to multimodal systems - Fundamental concepts of machine learning and deep learning |
| Performance Optimization | 10% | - Model efficiency and inference optimization - Hardware acceleration with NVIDIA platforms - Scalability and deployment considerations |
| Experimentation | 25% | - Experiment design and methodology - Model training, fine-tuning, and evaluation - Metrics and validation strategies for generative models |
| Trustworthy AI | 5% | - Robustness and error mitigation - Ethical considerations and responsible use - Reliability, fairness, and safety in generative systems |
| 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 |
| Multimodal Data | 15% | - Data preprocessing, fusion, and representation - Characteristics of text, image, and audio data - Multimodal model architectures and integration |
| Data Analysis and Visualization | 10% | - Visualization techniques for model behavior and results - Analyzing multimodal datasets and outputs - Interpretation of generative AI outputs |
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NEW QUESTION # 35
You're working on a multimodal AI system that combines text and image dat a. You're using a contrastive learning approach to learn joint embeddings of text and images. However, you notice that the system performs well on seen image-text pairs but poorly on unseen combinations. What technique MOST directly addresses this generalization problem?
Answer: A
Explanation:
Hard negative mining focuses on selecting the most challenging negative examples (incorrect image-text pairs) during training. This forces the model to learn more robust and discriminative embeddings that generalize better to unseen combinations. Increasing embedding dimension or using larger batch size might help to some extent, but hard negative mining directly addresses the core issue of distinguishing similar but incorrect pairs. Decreasing the temperature parameter can make the contrastive loss too sensitive, potentially hindering generalization. A simpler model architecture may be detrimental if it lacks the capacity to capture the complex relationships
NEW QUESTION # 36
In ML applications, which machine learning algorithm is commonly used for creating new data based on existing data?
Answer: D
Explanation:
GANs are purpose-built generative models: as covered in the previous question, the generator component learns the underlying distribution of a training dataset and produces new synthetic samples that resemble it - new images, audio, or other data types that did not exist in the original dataset but are statistically consistent with it. This generative capability is GAN's defining characteristic and the reason it is the correct answer among the options given, distinguishing it from the other three algorithms, all of which are fundamentally discriminative or unsupervised techniques rather than generative ones.
Decision trees (A) and support vector machines (B) are supervised discriminative algorithms - they learn a decision boundary or a set of rules to classify or predict outputs from inputs, with no mechanism for producing novel data samples resembling a training distribution. K-means clustering (C) is unsupervised but serves a partitioning function, grouping existing data points into clusters based on similarity - it identifies structure in data that already exists rather than synthesizing new data points that didn't exist before.
It's worth noting GANs are one of several generative model families (alongside variational autoencoders and diffusion models, both covered elsewhere in this set) - among the four options presented here, however, GAN is the only one designed for generation at all, making this a comparatively direct elimination once the discriminative-vs-generative distinction is applied.
Reference: Core Machine Learning and AI Knowledge domain - generative vs. discriminative algorithms, GANs.
NEW QUESTION # 37
A multimodal dataset consists of video footage of human actions and corresponding wearable sensor data (accelerometer, gyroscope). The goal is to predict the type of action being performed. However, the sensor data is noisy and often misaligned with the video frames. Consider the following code snippet designed to synchronize and clean the sensor data:
What is the primary purpose of the 'resample' function in this code, and what potential issues might arise from using a simple aggregation method during resampling?
Answer: E
Explanation:
The 'resample' function aligns the sensor data to the video frame rate, making the data streams compatible for analysis. However, using .mean()' during resampling can smooth out critical features in the time-series data, potentially leading to a loss of important information for action recognition. More sophisticated resampling techniques (e.g., interpolation, or using a median value) might be more appropriate.
NEW QUESTION # 38
You're working with a multimodal model that fuses text and image features. You've noticed that the model performs poorly when the text and image are semantically misaligned (e.g., an image of a dog and the caption 'a cat on a mat'). Which of the following techniques can help improve the model's robustness to such misalignment?
Answer: B
Explanation:
A contrastive loss function directly addresses the issue of semantic misalignment by penalizing the model when it produces similar embeddings for text and images that don't correspond semantically. This encourages the model to learn more robust and meaningful feature representations.
NEW QUESTION # 39
You're developing a system that translates spoken language into sign language animations. Which of the following losses would be MOST suitable for training the model to generate realistic and accurate sign language sequences from speech input?
Answer: D
Explanation:
MSE loss ensures accurate joint positioning, while the temporal smoothness loss prevents jerky and unnatural movements. Cross-entropy is suitable for classification tasks, not continuous sequence generation. Cosine Similarity between embeddings might encourage general alignment, but doesn't guarantee accurate pose reproduction and Binary Cross entropy is only good for Binary Classification tasks.
NEW QUESTION # 40
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