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| Section | Weight | Objectives |
|---|---|---|
| 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 |
| Core Machine Learning and AI Knowledge | 20% | - Fundamental concepts of machine learning and deep learning - Generative AI principles and techniques - Neural network architectures relevant to multimodal systems |
| Data Analysis and Visualization | 10% | - Visualization techniques for model behavior and results - Interpretation of generative AI outputs - Analyzing multimodal datasets and outputs |
| Trustworthy AI | 5% | - Reliability, fairness, and safety in generative systems - Robustness and error mitigation - Ethical considerations and responsible use |
| Performance Optimization | 10% | - Model efficiency and inference optimization - Scalability and deployment considerations - Hardware acceleration with NVIDIA platforms |
| 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 |
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NEW QUESTION # 36
Assume you need to implement a multimodal pipeline to diagnose brain cancer type using MRI scans and their corresponding radiology reports. What do you need to include in the ablation study?
Answer: B
Explanation:
An ablation study systematically removes or isolates individual components of a system to measure each one's individual contribution to overall performance. In a multimodal pipeline combining MRI scans and radiology reports, a proper ablation study requires training and evaluating separate unimodal pipelines - an image-only model on MRI scans alone, and a text-only model on radiology reports alone - alongside the full multimodal pipeline. Comparing these unimodal baselines against the combined system's performance is what actually demonstrates whether fusion is adding genuine diagnostic value beyond what either modality provides independently, and it surfaces whether one modality is doing most of the work while the other contributes marginally (or is even introducing noise) - critical information for both model design decisions and clinical validation in a high-stakes diagnostic context.
Option A describes an early-fusion design choice, not an ablation methodology - it's a modeling decision, not a validation technique for understanding component contribution. Option C proposes abandoning one modality's diagnostic value entirely, which undermines rather than tests the multimodal hypothesis. Option D describes data quality/preprocessing work relevant earlier in the pipeline, not the comparative, component- isolating structure that defines an ablation study.
In a clinical context specifically, this ablation approach is also essential for regulatory and interpretability purposes - demonstrating that a diagnostic claim rests on genuine cross-modal signal, not a spurious correlation from a single dominant input.
Reference: Multimodal Data / Experimentation domains - ablation studies for validating fusion architecture design.
NEW QUESTION # 37
You are working on a project to classify images of different types of flowers. You have a relatively small dataset (around 500 images per class). Which of the following techniques would be the MOST effective to improve the performance of your image classifier, considering the limited data?
Answer: E
Explanation:
Transfer learning, specifically fine-tuning a pre-trained model, is highly effective when dealing with small datasets. Pre-trained models have already learned useful features from large datasets, and fine-tuning them allows the model to adapt to the specific characteristics of your flower dataset. Training a deep network from scratch with limited data will likely lead to overfitting. Data augmentation helps, but transfer learning is generally more impactful. Reducing image resolution might lose important details, and a linear classifier might be too simple to capture the complexity of image features.
NEW QUESTION # 38
You are building a system that translates sign language videos into spoken text. You have a dataset of videos and corresponding text transcriptions. You notice that the test data contains significant variations in lighting conditions and camera angles compared to the training dat a. Which of the following techniques would be MOST effective in addressing this domain shift and improving the generalization of your model?
Answer: B
Explanation:
Domain adaptation techniques (C) are specifically designed to address domain shift by learning features that are invariant to the source and target domains. Data augmentation (A) can help but might not be sufficient. Fine-tuning on test data (B) is data leakage and invalidates the test set. Reducing model size (D) may not address the core issue of domain shift. Selecting a subset of the test data (E) defeats the purpose of testing generalization.
NEW QUESTION # 39
Consider the following Python code snippet using PyTorch, designed to combine text and image embeddings before feeding them into a transformer. Assume 'text_embedding' has shape '(batch_size, seq_len, hidden_dim)' and 'image_embedding' has shape '(batch_size, image_features)'. Which of the following code snippets MOST correctly combines these embeddings for a multimodal transformer input?
Answer: D
Explanation:
Option B correctly expands the image embedding to match the sequence length of the text embedding and then concatenates them along the hidden dimension (dim=2), creating a combined multimodal input. Option A is incorrect as the shapes are incompatible, and Option C performs element-wise addition, requiring compatible shapes. Option D performs matrix multiplication, which is not a suitable way to combine embeddings. Option E is not a proper way of combining the tensors
NEW QUESTION # 40
Explain the role of Tensor Cores and mixed-precision training (e.g., using FP16 or bfloat16) in accelerating the training of large generative AI models.
Answer: A
Explanation:
Tensor Cores are designed to accelerate matrix multiplication, the core operation in deep learning, using lower precision data types. Mixed-precision training leverages this by using lower precision for the bulk of the computation, while maintaining higher precision for critical variables to avoid instability. Tensor Cores are used both for training and inference.
NEW QUESTION # 41
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