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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Performance Optimization | 10% | - Monitoring and improving system efficiency - Techniques for optimizing AI performance |
| Topic 2: Trustworthy AI | 5% | - Ethical considerations in AI development - Ensuring fairness and transparency |
| Topic 3: Software Development & Engineering | 15% | - Python libraries for multimodal AI - Integration and deployment of multimodal AI systems |
| Topic 4: Core ML & AI Knowledge | 20% | - Key algorithms and techniques - Basic concepts and terminology |
| Topic 5: Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Topic 6: Data Analysis & Visualization | 10% | - Visualization techniques for multimodal data - Data preprocessing and feature engineering |
| Topic 7: Experimentation | 25% | - Hypothesis testing - Model evaluation and comparison - Experimental design - A/B testing |
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NEW QUESTION # 21
You are building an A1 model that takes video and corresponding subtitles as input to generate short summaries of video content. Which of the following strategies are most important to reduce the chance of your model generating biased summaries? (Select all that apply)
Answer: B,D,E
Explanation:
Debiasing pre-trained models helps remove existing biases. A diverse training dataset is important to reduce the influence of any single biased viewpoint. Evaluating model performance on different demographic groups allows you to find and rectify performance disparities. Random data shuffling (D) and increasing training epochs (E) do not directly address bias. Note this is a very tough question as all choices seem viable but only options, A, B and C are the correct choice.
NEW QUESTION # 22
Which of the following NVIDIA SDKs is most suitable for deploying a real-time, low-latency speech-to-text service as part of a multimodal AI application?
Answer: A
Explanation:
NVIDIA Riva is specifically designed for building and deploying real-time conversational A1 services, including speech-to-text, text-to- speech, and natural language understanding. Its focus is on low latency and high accuracy for conversational applications.
NEW QUESTION # 23
Consider the following Python code snippet used for evaluating a generative model. What potential issue exists with this code, and how would you rectify it to ensure a robust evaluation?
Answer: D
Explanation:
Generative models produce different outputs each time due to their inherent stochasticity. Evaluating only once can result in a score that isn't representative. The code needs to be run multiple times with different random seeds, and the results averaged to obtain a more reliable Inception Score. While FID is a good metric (A), the core issue here is the lack of accounting for stochasticity. While a larger sample size can help improve reliability, addressing the randomness is most important. Inception score is a valid metric (D) although it has limitations. The given sample code is assumed to be incomplete, hence it will not have any issue on its own.
NEW QUESTION # 24
What role does 'late fusion' play in multimodal machine learning?
Answer: B
Explanation:
Late fusion trains separate, independent models for each modality - each producing its own prediction, score, or decision - and combines those outputs only at the final decision stage, typically via averaging, weighted voting, a learned meta-classifier (stacking), or simple rule-based aggregation. This is the direct counterpart to early fusion (combination at the raw/feature input level, tested elsewhere in this set) and to intermediate/hybrid fusion (combination at one or more mid-network representation levels).
Late fusion's key practical advantage is modularity and robustness: because each modality's model operates independently until the final combination step, a missing or corrupted modality at inference time degrades performance gracefully rather than catastrophically - the surviving modalities' models can still contribute a prediction. It also allows each modality-specific model to be trained, validated, and even updated independently, which simplifies engineering in production systems. Its main disadvantage is that it cannot capture fine-grained, low-level cross-modal interactions, since by the time information reaches the fusion point, each modality has already been reduced to a high-level decision.
Options C and D describe feature-level and preprocessing-level combination respectively - both inconsistent with "late" in the fusion terminology, which specifically denotes the decision/output stage. Option B describes a training-schedule concept unrelated to fusion architecture.
Reference: Multimodal Data domain - late fusion vs. early/hybrid fusion, decision-level combination.
NEW QUESTION # 25
Which of the following is NOT a common challenge in training multimodal Generative AI models?
Answer: E
Explanation:
The computational complexity of training large unimodal models is a challenge for unimodal models, but not a distinct challenge inherent to multimodal models. Multimodal models have unique challenges related to data heterogeneity, feature alignment, handling missing modalities, and balancing performance across modalities.
NEW QUESTION # 26
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