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| Section | Weight | Objectives |
|---|---|---|
| Multimodal Data | 15% | - Multimodal model architectures and integration - Data preprocessing, fusion, and representation - Characteristics of text, image, and audio data |
| 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 |
| Performance Optimization | 10% | - Scalability and deployment considerations - Hardware acceleration with NVIDIA platforms - Model efficiency and inference optimization |
| 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% | - Robustness and error mitigation - Reliability, fairness, and safety in generative systems - Ethical considerations and responsible use |
| Software Development and Engineering | 15% | - Development workflows for generative AI applications - Libraries, frameworks, and tools for multimodal AI - Best practices for building and maintaining systems |
| Experimentation | 25% | - Model training, fine-tuning, and evaluation - Metrics and validation strategies for generative models - Experiment design and methodology |
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NEW QUESTION # 44
You are building a multimodal model for medical image diagnosis, using both radiology images (e.g., X-rays) and patient clinical notes.
The clinical notes are highly unstructured and contain significant medical jargon. What preprocessing steps would be MOST effective for improving the model's performance?
Answer: C
Explanation:
For medical text, NER and BioBERT are the best choice. NER extracts relevant medical information, while BioBERT is pre-trained on a large corpus of biomedical text, allowing it to better understand medical jargon and context. Raw text (A) would be ineffective. Basic cleaning and Word2Vec (B) are insufficient for complex medical language. Translation (D) isn't the primary preprocessing step needed. Sentiment analysis (E) is You have a multimodal generative model that produces images from text descriptions.
NEW QUESTION # 45
You are developing a multimodal model that takes both images and text as input. You want to fuse these modalities at an early stage.
Which of the following techniques is MOST appropriate for early fusion?
Answer: D
Explanation:
Cross-attention mechanisms at early layers allow the model to learn interactions between image and text features from the beginning, enabling a more nuanced understanding of their relationships. Concatenating feature vectors is a form of early fusion (A), but cross- attention is more powerful. Averaging predictions (B) is a late fusion technique. Concatenating raw pixels and tokens (E) is unlikely to work well due to the different nature of the data. Weighted sum (D) represents late fusion.
NEW QUESTION # 46
You are building a multimodal application that needs to understand both image and text dat a. You want to use a pre-trained model but fine-tune it for your specific task. Which of the following strategies is MOST effective for fine-tuning a large pre-trained multimodal model?
Answer: B
Explanation:
Fine-tuning the entire model with a small learning rate allows the model to adapt to the specific nuances of the new task while leveraging the knowledge already learned during pre-training. Freezing layers can limit adaptability. Training only a new head might not fully utilize the pre-trained features.
NEW QUESTION # 47
You are building an image generation pipeline that leverages both a U-Net and a pre-trained CLIP model. After generating an image with the U-Net, you want to use CLIP to assess how well the generated image aligns with a given text prompt. Which of the following steps are crucial for obtaining a meaningful similarity score between the image and the text using CLIP?
Answer: A,B,C
Explanation:
To assess the alignment between a generated image and a text prompt using CLIP, you need to encode both the image and the text into vector representations using CLIP's respective encoders (image and text encoders). Then, calculate the cosine similarity between these embeddings to quantify their semantic relatedness. Fine-tuning CLIP is not typically necessary for this purpose. High resolution is not mandatory as CLIP works well on medium resolution images and it's embedded space.
NEW QUESTION # 48
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: C,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 # 49
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