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| Section | Weight | Objectives |
|---|---|---|
| Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Experimentation | 25% | - Hypothesis testing - Model evaluation and comparison - A/B testing - Experimental design |
| Software Development & Engineering | 15% | - Python libraries for multimodal AI - Integration and deployment of multimodal AI systems |
| Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
| Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Data Analysis & Visualization | 10% | - Visualization techniques for multimodal data - Data preprocessing and feature engineering |
| Performance Optimization | 10% | - Techniques for optimizing AI performance - Monitoring and improving system efficiency |
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NEW QUESTION # 28
Which of the following are valid techniques for dealing with overfitting in a deep learning model trained on image data?
Answer: A,C,D
Explanation:
Overfitting occurs when a model learns the training data too well and performs poorly on unseen data. L1/L2 regularization penalizes large weights, preventing the model from becoming too complex. Data augmentation increases the Size and diversity of the training data, reducing overfitting. Dropout randomly deactivates neurons during training, preventing co-adaptation and improving generalization. Increasing model complexity or reducing training data would likely worsen overfitting.
NEW QUESTION # 29
You are developing a text-to-image generative model and want to evaluate the quality and diversity of the generated images. Which metric is MOST appropriate for assessing the diversity of generated images, considering computational efficiency is also important?
Answer: B
Explanation:
FID is a commonly used metric for evaluating the quality and diversity of generated images, and it's more robust than IS. While LPIPS and MS-SSIM are useful for measuring perceptual similarity between images, they don't directly quantify diversity across a large set of generated images. Simply counting unique images doesn't capture the semantic diversity of the generated content. FID calculates the distance between the feature representations of real and generated images, capturing both quality and diversity.
NEW QUESTION # 30
You are using NeMo to fine-tune a large language model for a text-to-image task. During training, you encounter a CUDA out-of-memory error, despite using mixed-precision training. What is the MOST effective strategy to reduce memory consumption and continue training without significantly degrading model performance?
Answer: E
Explanation:
Gradient checkpointing (also known as activation recomputation) trades compute for memory. It avoids storing all intermediate activations during the forward pass, instead recomputing them during the backward pass. This significantly reduces memory footprint, allowing you to train larger models or use larger batch sizes. Increasing batch size increases memory consumption. Decreasing gradient accumulation steps might help slightly but is less effective than gradient checkpointing. Switching to a smaller model or disabling mixed precision would degrade model performance.
NEW QUESTION # 31
For building a zero-shot image classification pipeline, what could be a crucial step in the process?
Answer: D
Explanation:
Zero-shot image classification, by definition, requires classifying images into categories the model was never explicitly trained to recognize, with no task-specific labeled examples. CLIP-style models enable this by encoding both images and candidate text labels (e.g., "a photo of a {class}") into a shared embedding space; classification then reduces to a similarity comparison - computing cosine similarity between the image embedding and each candidate text embedding and selecting the closest match. This is the crucial architectural step: without a shared embedding space linking visual and textual semantics, there is no mechanism to generalize to unseen classes using only their names or descriptions.
Option B directly contradicts the "zero-shot" premise - manual labeling of the target dataset is precisely what zero-shot classification is designed to avoid; if labels were being collected for the target classes, the task would be standard supervised classification, not zero-shot. Option A (image enhancement) may marginally help downstream accuracy but is not the crucial, defining step. Option D is incoherent with how CLIP-style zero-shot classification actually works - the textual description of each candidate class is the essential input that makes zero-shot generalization possible; eliminating it would remove the mechanism entirely, not improve it.
Reference: Multimodal Data domain - zero-shot classification via shared embedding spaces (CLIP).
NEW QUESTION # 32
Which of the following techniques is LEAST likely to improve the performance of a Generative A1 model tasked with generating realistic images from text descriptions?
Answer: B
Explanation:
Reducing the dimensionality of text embeddings will likely degrade performance, as it removes information that the model needs to accurately generate images. The other options (A, B, C, and E) are all established techniques for improving the quality and fidelity of generated images.
NEW QUESTION # 33
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