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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Multimodal Data | 15% | - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation - Multimodal model architectures and integration |
| Topic 2: Performance Optimization | 10% | - Hardware acceleration with NVIDIA platforms - Scalability and deployment considerations - Model efficiency and inference optimization |
| Topic 3: Core Machine Learning and AI Knowledge | 20% | - Generative AI principles and techniques - Fundamental concepts of machine learning and deep learning - Neural network architectures relevant to multimodal systems |
| Topic 4: Experimentation | 25% | - Model training, fine-tuning, and evaluation - Experiment design and methodology - Metrics and validation strategies for generative models |
| Topic 5: 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 |
| Topic 6: Trustworthy AI | 5% | - Robustness and error mitigation - Ethical considerations and responsible use - Reliability, fairness, and safety in generative systems |
| Topic 7: Data Analysis and Visualization | 10% | - Visualization techniques for model behavior and results - Interpretation of generative AI outputs - Analyzing multimodal datasets and outputs |
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NEW QUESTION # 36
You have trained a multimodal model for visual question answering (VQA). During inference, the model often generates incorrect answers even though it seems to understand the question and the image content. Which of the following strategies could help improve the accuracy of the model's predictions? (Select all that apply)
Answer: B,C,E
Explanation:
Beam search can help explore more probable answer sequences, data augmentation can improve the model's robustness, and a loss function that penalizes incorrect answers more heavily can encourage the model to learn more accurate predictions. Increasing the learning rate might lead to instability, and reducing the dataset size is generally detrimental to performance.
NEW QUESTION # 37
Consider the following Python code snippet, which attempts to implement a basic form of cross-validation. What is the primary issue with this code and how would you fix it to prevent data leakage?
Answer: D
Explanation:
Data leakage happens because the StandardScaler is fit on the entire dataset before splitting into training and testing sets. This means that the scaling parameters (mean and standard deviation) are influenced by the test data, which then leaks information into the training process, leading to overoptimistic performance estimates. The correct fix is to fit the scaler only on the training data within each fold and then use the fitted scaler to transform both the training and testing data.
NEW QUESTION # 38
When deploying a multimodal Generative A1 model for a real-time application, such as a virtual assistant that responds to voice commands and displays relevant images, which of the following considerations are MOST critical for ensuring low latency and a smooth user experience? (Select TWO)
Answer: B,D
Explanation:
Model quantization and pruning reduce the model's size and computational complexity, leading to faster inference. Asynchronous processing and caching allow for pre-computation and storage of frequently used data, minimizing delays. Prioritizing accuracy over speed (A) is not suitable for real-time applications where responsiveness is crucial. Deploying on a single CPU core (D) would severely limit performance. Disabling logging (E) is detrimental for debugging and monitoring.
NEW QUESTION # 39
Given the following code snippet using NVIDIA Triton Inference Server for deploying a multimodal model:
What does 'format: FORMAT NCHW' signify for the 'image_input'?
Answer: B
Explanation:
'FORMAT NCHW' specifies that the image data is organized in a channel-first format, where the dimensions represent (Number of Images, Channels, Height, Width). This is a common format used in deep learning frameworks like PyTorch. Other options are incorrect because they relate to different aspects of image data representation or format.
NEW QUESTION # 40
What is a common method to reduce the computational cost of deep learning models during inference?
Answer: D
Explanation:
Pruning removes weights, neurons, or entire filters/channels that contribute minimally to model output - identified via magnitude-based criteria (removing near-zero weights), sensitivity analysis, or more sophisticated importance scoring - producing a smaller, sparser model that requires fewer computations and less memory at inference time while aiming to preserve accuracy through careful selection and, often, a fine- tuning step after pruning to recover any lost performance. Structured pruning (removing entire filters
/channels) yields hardware-friendly speedups on standard accelerators, while unstructured pruning (removing individual weights) achieves higher sparsity ratios but requires specialized sparse-computation hardware or libraries to realize actual speed gains.
The remaining options move in the wrong direction or address a different concern: adding more convolutional filters (B) increases model capacity and parameter count, which increases computational cost, the opposite of the stated goal. Increasing batch size (D) affects training throughput and memory usage per step but does not reduce the per-sample computational cost of inference - a larger batch does more total work, not less per inference call, and batch size at inference is often constrained by latency requirements rather than optimization goals. Option C's premise - selectively replacing activation functions with simpler ones in
"some neurons" - is not a standard or well-defined optimization technique; activation function choice is typically uniform within a layer and driven by training dynamics, not a piecemeal inference-cost lever.
Reference: Performance Optimization domain - pruning, alongside quantization and kernel fusion, as core inference-cost reduction techniques.
NEW QUESTION # 41
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