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| Section | Weight | Objectives |
|---|---|---|
| Trustworthy AI | 5% | - Ethical considerations and responsible use - Reliability, fairness, and safety in generative systems - Robustness and error mitigation |
| Data Analysis and Visualization | 10% | - Analyzing multimodal datasets and outputs - Visualization techniques for model behavior and results - Interpretation of generative AI outputs |
| Core Machine Learning and AI Knowledge | 20% | - Neural network architectures relevant to multimodal systems - Fundamental concepts of machine learning and deep learning - Generative AI principles and techniques |
| Multimodal Data | 15% | - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation - Multimodal model architectures and integration |
| Experimentation | 25% | - Metrics and validation strategies for generative models - Model training, fine-tuning, and evaluation - Experiment design and methodology |
| Software Development and Engineering | 15% | - Libraries, frameworks, and tools for multimodal AI - Development workflows for generative AI applications - Best practices for building and maintaining systems |
| Performance Optimization | 10% | - Scalability and deployment considerations - Model efficiency and inference optimization - Hardware acceleration with NVIDIA platforms |
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NEW QUESTION # 31
You're training a conditional GAN to generate images of birds based on text descriptions. The GAN generates images, but they lack fine- grained details and often have artifacts. Which of the following techniques are MOST likely to improve the quality and realism of the generated images? (Select TWO)
Answer: A,E
Explanation:
Spectral normalization helps stabilize training by limiting the Lipschitz constant of the discriminator and generator, preventing exploding gradients and improving image quality. A deeper and wider generator network can capture more complex image features and generate more detailed images. A simple MLP wouldn't be suitable for generating high-resolution images. Reducing the input noise vector size might limit the diversity of generated images. A more powerful discriminator helps in better distinguishing between real and fake images, which guides the generator to produce more realistic outputs. However, spectral normalization directly addresses stability issues that cause artifacts.
NEW QUESTION # 32
Hyperparameter tuning is used for what purpose in machine learning experimentation?
Answer: A
Explanation:
Hyperparameters are configuration values set *before* training begins and are not updated by the optimization process itself - learning rate, batch size, number of layers, regularization strength, and number of training epochs are canonical examples. Hyperparameter tuning is the systematic search for the combination of these values that yields the best model performance on a validation set, using strategies such as grid search, random search, or more sample-efficient approaches like Bayesian optimization and population-based training.
This is explicitly distinct from option A, which describes the *training* process itself - weights and biases are trainable parameters, updated automatically via backpropagation and gradient descent, not selected through hyperparameter search. Option B describes algorithm selection, a higher-level modeling decision that may precede hyperparameter tuning but is not what tuning itself accomplishes (you tune hyperparameters
*within* a chosen algorithm/architecture). Option C describes data engineering work that happens upstream of model training entirely, unrelated to parameter search.
In practice, hyperparameter tuning requires careful experimental design to avoid overfitting to the validation set - techniques like k-fold cross-validation, held-out test sets, and tracking tools (e.g., experiment trackers logging each trial's configuration and resulting metric) are standard practice, connecting this topic directly to the Experimentation domain's broader emphasis on rigorous, reproducible model evaluation.
Reference: Experimentation domain - hyperparameter tuning, search strategies, validation methodology.
NEW QUESTION # 33
When training a multimodal generative model for image captioning, you notice the model generates grammatically correct but generic and uninformative captions. Which technique is MOST likely to improve the in formativeness and specificity of the generated captions?
Answer: A
Explanation:
Diverse beam search or sampling strategies encourage the model to explore different caption possibilities during inference, leading to more diverse and informative captions. Standard beam search often converges to the most likely caption, which tends to be generic. Increasing the image encoder Size might improve image feature extraction but doesn't directly address the caption informativeness problem. Decreasing the learning rate is a general training technique that might improve convergence but doesn't specifically target caption informativeness.
NEW QUESTION # 34
You are training a multimodal model that combines audio and video dat
a. You observe that the model performs well on the training data but generalizes poorly to unseen data. Which of the following regularization techniques is MOST likely to improve the generalization performance in this scenario?
Answer: C
Explanation:
Data augmentation is the most effective regularization technique in this scenario because it increases the diversity of the training data, making the model more robust to variations in unseen data L1 and L2 regularization can help prevent over fitting, but data augmentation directly addresses the issue of limited training data. Dropout also helps, but data augmentation is generally more impactful for multimodal data where variations are significant. Early stopping can also help, but it is not as effective as data augmentation.
NEW QUESTION # 35
Which of the following tasks can be performed using the transformer LLM encoder model?
Answer: C
Explanation:
Encoder-only transformer architectures (BERT and its derivatives being the canonical examples) process an entire input sequence bidirectionally, attending to both left and right context simultaneously, and output contextualized representations for each token - well suited to understanding tasks that require a rich representation of input meaning without generating new sequential output. Semantic analysis - including sentiment classification, named entity recognition, semantic similarity, and text classification more broadly - is a natural fit, typically implemented by taking the encoder's output representation (often a special [CLS] token embedding) and feeding it to a lightweight classification head.
The other options require architectures the encoder-only design is not built for: generating code (B) and open- ended text generation generally require autoregressive, causal (decoder-only or encoder-decoder) architectures that predict tokens sequentially, since encoder-only models produce a fixed set of contextualized representations rather than generating novel token sequences left-to-right. Image generation (C) requires an entirely different model family (diffusion models, GANs, or autoregressive image models), unrelated to text- encoder architectures. Speech recognition (D) requires an audio-processing pipeline - typically a CTC-based or encoder-decoder acoustic model operating on spectrogram or waveform input - not a text-only transformer encoder.
Reference: Core Machine Learning and AI Knowledge domain - transformer encoder vs. decoder vs.
encoder-decoder architectures and their respective task fits.
NEW QUESTION # 36
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