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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
| Topic 2: Software Development & Engineering | 15% | - Python libraries for multimodal AI - Integration and deployment of multimodal AI systems |
| Topic 3: Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Topic 4: Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
| Topic 5: Experimentation | 25% | - Hypothesis testing - Model evaluation and comparison - A/B testing - Experimental design |
| Topic 6: Performance Optimization | 10% | - Monitoring and improving system efficiency - Techniques for optimizing AI performance |
| Topic 7: Trustworthy AI | 5% | - Ethical considerations in AI development - Ensuring fairness and transparency |
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NEW QUESTION # 29
You are working with a multimodal model that combines text and image inputs. You want to analyze the model's attention mechanisms to understand which parts of the image are most relevant to specific words in the input text. What technique can you use to visualize and interpret the model's attention weights in this scenario?
Answer: D
Explanation:
Attention heatmaps are a visualization technique used to highlight the regions of an image that the model is focusing on when processing specific words in the input text. By overlaying the attention weights onto the image, you can identify the most relevant areas. t-SNE and PCA are dimensionality reduction techniques used for visualizing high-dimensional data in lower dimensions. ROC curves and confusion matrices are used to evaluate the performance of classification models.
NEW QUESTION # 30
You are working with a large multimodal dataset that contains images and corresponding text descriptions. The text descriptions are highly variable in length and content. Which of the following techniques is MOST effective for handling this variability when training a multimodal model?
Answer: C
Explanation:
Dynamic padding and masking allow the model to efficiently process variable-length sequences without losing information or introducing bias. Padding to a fixed length can waste computation, truncating loses data and ignoring descriptions also loses information. Discarding words could harm the model's learning abilities.
NEW QUESTION # 31
You are working on a project involving generating photorealistic images of human faces using a generative model. Ethical considerations are paramount. Which of the following practices are MOST important to incorporate into your development workflow to mitigate potential biases and misuse?
Answer: A
Explanation:
Addressing ethical considerations requires a multi-faceted approach, including training on diverse data, bias detection/mitigation, and transparency. Option A encompasses all these aspects. Ignoring ethical concerns (B, D) is irresponsible. Restricting access (C) might not be feasible or effective. Synthetic data (E) can still be biased. Claiming a model is completely unbiased is misleading and incorrect.
NEW QUESTION # 32
You are deploying a multimodal Generative A1 model on a cloud platform. The model takes video and text as input to generate video descriptions. The model's performance needs to be monitored to ensure it meets certain performance SLAs. Which of the following metrics are MOST crucial to monitor in a production environment to ensure both computational efficiency and output quality? (Select TWO)
Answer: B,D
Explanation:
Inference latency directly impacts the user experience and resource utilization. A high latency indicates potential bottlenecks. The BLEU score (or similar metric) measures the quality of the generated text, ensuring that the model is producing accurate and relevant descriptions. GPU utilization is important but secondary compared to latency and quality. Model size and lines of code are not direct indicators of runtime performance or output quality.
NEW QUESTION # 33
You are developing a system that generates 3D models from text descriptions. The system currently produces models that are geometrically accurate but lack fine-grained surface details and realistic textures. Which of the following steps would be MOST effective in improving the visual realism of the generated 3D models?
Answer: C
Explanation:
Training a separate texture generation model allows for specializing in generating realistic surface details and textures based on both the text description and the underlying 3D geometry. Increasing polygon count (A) can help, but doesn't address texturing. Simplifying the text encoder or reducing the dataset is counterproductive. Solely relying on procedural generation might lead to lack of variability.
NEW QUESTION # 34
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