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| Section | Weight | Objectives |
|---|---|---|
| Core Machine Learning and AI Knowledge | 20% | - Fundamental concepts of machine learning and deep learning - Neural network architectures relevant to multimodal systems - Generative AI principles and techniques |
| Experimentation | 25% | - Model training, fine-tuning, and evaluation - Metrics and validation strategies for generative models - Experiment design and methodology |
| Performance Optimization | 10% | - Hardware acceleration with NVIDIA platforms - Scalability and deployment considerations - Model efficiency and inference optimization |
| Multimodal Data | 15% | - Characteristics of text, image, and audio data - Multimodal model architectures and integration - Data preprocessing, fusion, and representation |
| Software Development and Engineering | 15% | - Best practices for building and maintaining systems - Libraries, frameworks, and tools for multimodal AI - Development workflows for generative AI applications |
| Data Analysis and Visualization | 10% | - Analyzing multimodal datasets and outputs - Visualization techniques for model behavior and results - Interpretation of generative AI outputs |
| Trustworthy AI | 5% | - Ethical considerations and responsible use - Robustness and error mitigation - Reliability, fairness, and safety in generative systems |
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問題 #16
You are working with a multimodal dataset containing medical images (X-rays) and corresponding patient reports (text). Some of the reports are missing or incomplete. Which of the following strategies would be most appropriate to handle this missing data in a multimodal AI model?
答案：E
解題說明：
Using a multimodal autoencoder or a masked language model allows the model to leverage the relationship between the image and text modalities to infer the missing information. Discarding data or using simple imputation methods can lead to information loss or biased results. A multimodal autoencoder or masked language model can help to reconstruct the missing reports from the available image data, or using a masked language model to predict missing words in the existing reports, conditioned on the image.
問題 #17
Consider the following code snippet used for creating a multimodal dataset with PyTorch. The dataset contains images and corresponding text descriptions. However, during training, you observe a significant imbalance in the data distribution of text lengths. Which of the following techniques would BEST address this issue?
答案：D
解題說明：
Padding or truncating text sequences to a fixed length is a standard technique for handling variable-length sequences in NLP tasks. This ensures that all text inputs have the same dimensionality, which is required for efficient batch processing in neural networks- While image augmentation can improve the model's robustness to variations in image data, it does not directly address the issue of text length imbalance. Learning rate scheduling and batch normalization are general training techniques that can improve convergence, but they do not specifically address the text length imbalance.
問題 #18
You are working with a multimodal dataset that contains images and corresponding captions. You want to use contrastive learning to learn joint embeddings for images and text. Which of the following loss functions is the most suitable for this task?
答案：E
解題說明：
Triplet loss is specifically designed for contrastive learning, where the goal is to learn embeddings such that similar pairs are closer in the embedding space than dissimilar pairs- Cross-entropy and binary cross-entropy are classification losses. MSE loss is a regression loss- NLL loss is often used with sequence models but doesn't directly address contrastive learning goals.
問題 #19
Consider a multimodal dataset containing patient records: text descriptions of symptoms, MRI images, and audio recordings of heart sounds. Some records are missing MRI images. Which of the following methods is BEST suited for handling this missing data within a multimodal learning framework?
答案：B
解題說明：
Masking provides a way to leverage the available information in all records, even those with missing modalities. The model learns to infer the missing data from the available data, which can improve overall performance. Deleting data or using a simple average imputation can introduce bias or information loss. Training a separate model or ignoring the MRI data altogether does not effectively utilize the available multimodal information for all records.
問題 #20
You want to evaluate the performance of an AI model. Which of the following is a method for AI model evaluation?
答案：D
解題說明：
Valid model evaluation requires measuring performance on held-out data the model has not seen during training - this is the foundational principle behind train/validation/test splits and cross-validation, and it exists specifically to estimate how the model will generalize to genuinely new data, rather than how well it memorized patterns specific to its training set. Option B correctly describes this: sampling from a portion of the dataset explicitly excluded from training and calculating accuracy on it.
Options C and D both violate this principle by evaluating on the training set itself, which produces optimistically biased performance estimates: a model - particularly an overparameterized deep learning model - can achieve very high training accuracy or very low training loss simply by memorizing training examples (overfitting) without that performance transferring to new data at all. Reporting training-set accuracy (C) or training-set loss (D) as an evaluation of "performance" conflates fit-to-training-data with generalization, the central failure mode that held-out evaluation is designed to catch. Option A describes a qualitative, subjective process - interviewing developers - that provides no quantitative, reproducible performance measurement and is not a recognized model evaluation methodology.
This principle extends further in rigorous experimentation: a validation set used repeatedly for hyperparameter tuning can itself become "leaked" through repeated selection, which is why a separate, untouched test set is typically reserved for final, one-time performance reporting.
Reference: Experimentation domain - held-out evaluation, train/validation/test methodology, avoiding overfitting bias in reported metrics.
問題 #21
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