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| Section | Weight | Objectives |
|---|---|---|
| Experimentation | 25% | - Metrics and validation strategies for generative models - Experiment design and methodology - Model training, fine-tuning, and evaluation |
| 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% | - Ethical considerations and responsible use - Reliability, fairness, and safety in generative systems - Robustness and error mitigation |
| Multimodal Data | 15% | - Data preprocessing, fusion, and representation - Multimodal model architectures and integration - Characteristics of text, image, and audio data |
| Core Machine Learning and AI Knowledge | 20% | - Fundamental concepts of machine learning and deep learning - Neural network architectures relevant to multimodal systems - Generative AI principles and techniques |
| 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 |
| Performance Optimization | 10% | - Model efficiency and inference optimization - Hardware acceleration with NVIDIA platforms - Scalability and deployment considerations |
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NEW QUESTION # 44
You want to ensure that the system respects user privacy and avoids generating avatars that resemble real people without their consent.
Which of the following strategies would be MOST effective in addressing this ethical concern?
Answer: E
Explanation:
Using a diverse and anonymized dataset reduces the risk of the model learning to generate avatars that are too similar to specific individuals. Preventing the generation of highly realistic facial features further mitigates this risk while still allowing for personalized avatars. Ethical concerns are paramount when developing generative A1 systems, particularly when dealing with potentially sensitive data such as facial features.
NEW QUESTION # 45
Consider a scenario where you want to use a Transformer model for generating music. Which of the following modifications to the standard Transformer architecture would be most beneficial for capturing the long-range dependencies and musical structure inherent in music?
Answer: A
Explanation:
Relative positional embeddings are particularly effective for capturing relationships based on relative positions, which is crucial in music where the intervals and relationships between notes are significant. Standard positional embeddings only encode absolute position. Capturing those relationships greatly improves model accuracy While the other options might change behavior, they wouldn't specifically improve sequence modeling ability.
NEW QUESTION # 46
During the process of data cleansing, which of the following steps is NOT typically performed?
Answer: A
Explanation:
Data cleansing (or data cleaning) operates on data you already have: it identifies and resolves quality issues within an existing dataset - handling missing values (A), removing duplicate records (D), correcting formatting or type inconsistencies (B), fixing structural errors, and standardizing units or encodings.
Collecting additional data (C) belongs to a conceptually earlier and separate phase of the pipeline: data acquisition or data collection, which determines what data enters the pipeline in the first place, rather than what is done to improve the quality of data already collected.
This distinction matters operationally: a cleansing step is typically deterministic and reversible against the existing dataset (you can inspect, log, and audit exactly which rows were dropped or imputed), whereas collecting more data is a scoping decision that may require new labeling budgets, new consent/privacy review, or new data-source integration - a materially different workflow with different stakeholders.
That said, insufficient data volume discovered *during* cleansing (e.g., after removing corrupted records the sample size becomes too small for the target class) can trigger a decision to go back and collect more - but that action itself is not classified as a cleansing step; it is the trigger for restarting an earlier pipeline stage.
Reference: Data Analysis and Visualization domain - data cleansing vs. data acquisition pipeline stages.
NEW QUESTION # 47
You are building a text-to-image generation pipeline using CLIP and a diffusion model. After training, you notice that the generated images often lack the specific details mentioned in the text prompts. Which of the following strategies could you employ to improve the alignment between text and image?
Answer: B
Explanation:
All the strategies mentioned can help improve the alignment between text and image. Increasing U-Net layers can improve image detail. Fine-tuning CLIP improves semantic understanding. Negative prompts refine image generation. More diffusion steps can improve image quality. All options contribute to better alignment.
NEW QUESTION # 48
You're developing a real-time multimodal A1 system that processes live video and audio streams. The system's performance is lagging behind requirements. Which of the following optimization strategies would be MOST effective in improving the system's throughput and reducing latency?
Answer: C,D,E
Explanation:
TensorRT optimizes models for NVIDIA GPUs, significantly improving inference speed and reducing latency. Mixed-precision inference reduces memory bandwidth requirements and allows for faster computations on Tensor Cores. Reducing the number of layers decreases the computational complexity of the model. RNNs are not typically faster than Transformers, especially when optimized with libraries like TensorRT. While a larger batch size can increase throughput, it also increases latency, which is detrimental for a real-time system.
NEW QUESTION # 49
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