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| Section | Weight | Objectives |
|---|---|---|
| Multimodal Data | 15% | - Characteristics of text, image, and audio data - Multimodal model architectures and integration - Data preprocessing, fusion, and representation |
| Data Analysis and Visualization | 10% | - Interpretation of generative AI outputs - Analyzing multimodal datasets and outputs - Visualization techniques for model behavior and results |
| Trustworthy AI | 5% | - Robustness and error mitigation - Reliability, fairness, and safety in generative systems - Ethical considerations and responsible use |
| 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 |
| Experimentation | 25% | - Metrics and validation strategies for generative models - Model training, fine-tuning, and evaluation - Experiment design and methodology |
| Performance Optimization | 10% | - Scalability and deployment considerations - Hardware acceleration with NVIDIA platforms - Model efficiency and inference optimization |
| 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 |
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NEW QUESTION # 10
Consider the following PyTorch code snippet for a GAN discriminator:
Answer: A
Explanation:
The code calculates the hinge loss. The loss for real samples is - , which penalizes the discriminator when the output for real samples is less than 1. The loss for fake samples is + fake_output))' , which penalizes the discriminator when the output for fake samples is greater than -1. The 'torch.mean' function calculates the mean over all elements of the input tensor, so the 'dim' argument is not needed.
NEW QUESTION # 11
You have a dataset containing information about sales performance for different regions in the last ten years.
Which type of data visualization would be most appropriate to compare the sales performance across regions on a year-by-year basis?
Answer: B
Explanation:
Reviewer note: Marked answer (D, pie chart) is inconsistent with standard data-visualization practice for year-by-year, multi-region comparison; a line chart (B) is the technically defensible choice.
I need to flag this one directly: the marked answer (D, pie chart) does not hold up technically, and I won't present it as correct just because it's what the answer key says. A pie chart shows the proportional breakdown of a whole at a single point in time - it has no mechanism for representing a trend across ten years, and using ten overlapping pie charts (one per year) to compare regional performance would be one of the least readable choices available, not the most appropriate.
The technically correct choice is a line chart (B): with ten years of data per region, a line chart plots each region as a separate series across a shared time axis, making year-over-year trends, growth rates, inflection points, and cross-region divergence immediately visible - exactly the "year-by-year" comparison the question specifies. A grouped/clustered bar chart (C) is a reasonable secondary choice if the emphasis is discrete year-to-year comparison rather than continuous trend, but it becomes visually cluttered with ten years
× multiple regions. A scatter plot (A) is better suited to examining the relationship between two continuous variables (e.g., sales vs. marketing spend) than to a time-series comparison across categories.
If this exact answer appears on a live exam or official material, treat D with skepticism - this explanation reflects standard data visualization practice, not the source document's marked key.
NEW QUESTION # 12
Consider a multimodal dataset containing text, images, and corresponding GPS coordinates. You want to build a model that predicts the sentiment of a social media post based on this dat a. Which of the following data preprocessing steps are crucial to ensure the model's performance and prevent data leakage?
Answer: A,C,D,E
Explanation:
Normalizing text (A) and resizing images (B) are standard preprocessing steps. Time-based splitting (C) prevents data leakage by ensuring that the model is not trained on future data. Standardizing GPS coordinates (E) with training data prevents the test data from influencing the scaling. Random shuffling before splitting (D) can lead to data leakage in time-series data.
NEW QUESTION # 13
Consider a multimodal A1 system that generates recipes based on images of ingredients. The system uses attention maps to highlight the relevant ingredients in the image. You observe that the attention maps are often noisy and highlight irrelevant parts of the image, leading to incorrect recipes. Which of the following strategies could BEST improve the quality and interpretability of the attention maps?
Answer: A,D
Explanation:
Applying L1 regularization to the attention weights encourages sparsity, meaning that the model will focus on only the most relevant regions of the image, leading to cleaner and more interpretable attention maps. Option D is another possible answer as it will help create an image with more precise objects detection. Option A is unlikely to improve the quality and interpretability of the attention maps. The size of the convolutional filters is more related to the receptive field of the image encoder. Adding more layers (C) may not directly address the noisiness of the attention maps.
NEW QUESTION # 14
When deploying a Generative A1 model to a resource-constrained edge device (e.g., a mobile phone), what are the key considerations for model optimization and which techniques are most effective?
Answer: A
Explanation:
Edge deployment requires optimizing for both model size and computational efficiency. Quantization reduces model size, pruning removes unnecessary connections, and knowledge distillation creates smaller, faster models. Increasing model complexity is counterproductive in resource-constrained environments. Both parameter count and computational complexity are important factors.
NEW QUESTION # 15
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