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| Section | Weight | Objectives |
|---|---|---|
| Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
| Data Analysis & Visualization | 10% | - Visualization techniques for multimodal data - Data preprocessing and feature engineering |
| Software Development & Engineering | 15% | - Integration and deployment of multimodal AI systems - Python libraries for multimodal AI |
| Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Performance Optimization | 10% | - Techniques for optimizing AI performance - Monitoring and improving system efficiency |
| Experimentation | 25% | - A/B testing - Model evaluation and comparison - Experimental design - Hypothesis testing |
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NEW QUESTION # 45
You are fine-tuning a large pre-trained language model for a specific downstream task. During training, you observe that the model performs well on the training data but generalizes poorly to the validation dat a. Which of the following strategies could help improve the model's generalization performance?
Answer: A,C,D,E
Explanation:
Decreasing the learning rate can help the model to converge to a better solution and avoid overfitting. Increasing the training data size provides the model with more examples to learn from, improving generalization. Early stopping prevents the model from training for too long and overfitting the training data. Increasing weight decay adds more regularization, preventing the model from learning overly complex patterns. Increasing the learning rate might worsen overfitting.
NEW QUESTION # 46
What are some methods to overcome limited throughput between CPU and GPU?
Answer: C
Explanation:
CPU-GPU data transfer over the PCIe (or NVLink) bus is frequently a throughput bottleneck in ML pipelines, particularly when small, frequent transfers dominate rather than large batched ones - each transfer incurs fixed overhead independent of data size, so many small transfers waste a disproportionate amount of time on overhead rather than useful data movement. Memory pooling techniques - pre-allocating and reusing pinned (page-locked) host memory buffers rather than repeatedly allocating and freeing memory for each transfer - reduce this overhead and enable faster, more predictable DMA transfers between host and device. Related software-level techniques include using CUDA streams to overlap data transfer with computation (so the GPU keeps computing while the next batch transfers in the background), and batching transfers to amortize fixed per-transfer overhead across more data.
Options A, B, and D each propose hardware upgrades that address a different bottleneck than the one described: increasing CPU clock speed (A) or core count (B) improves CPU-side compute throughput, not the data-transfer bandwidth or latency between CPU and GPU specifically. Upgrading the GPU (D) increases GPU compute capability but does nothing to address a PCIe/interconnect bandwidth limitation - a faster GPU sitting idle waiting for data across the same bottlenecked bus would not see meaningfully improved end- to-end throughput. The question specifically asks about *throughput between* CPU and GPU, which points to interconnect/transfer-management optimization rather than raw compute upgrades on either side.
Reference: Performance Optimization domain - CPU-GPU data transfer optimization, pinned memory, memory pooling, CUDA streams.
NEW QUESTION # 47
Consider a multimodal emotion recognition system that uses both facial expressions (images) and speech (audio). You want to fuse the information from these two modalities at the decision level. Which of the following techniques would be MOST suitable for decision-level fusion?
Answer: D
Explanation:
Weighted averaging allows you to give more weight to the modality that is more reliable or confident in its prediction for a given input. Simply averaging treats all modalities equally. Concatenation is feature-level fusion. The image classifier as input to audio classifier is a specific cascade approach. Using a single transformer is possible, but less common for decision fusion specifically. It is feature level fusion.
NEW QUESTION # 48
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 # 49
Which NVIDIA SDK would be most appropriate for building a real-time, interactive avatar that can respond to voice commands and generate realistic facial expressions?
Answer: C
Explanation:
Avatar Cloud Engine (ACE) is specifically designed for building and deploying interactive avatars. It provides pre-built components and APIs for speech recognition, natural language understanding, and realistic avatar animation. Riva focuses primarily on speech AI, NeMo on training large language models, and Triton on model serving. RAPIDS is for data analytics and GPU-accelerated machine learning.
NEW QUESTION # 50
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