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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: Data Analysis & Visualization | 10% | - Visualization techniques for multimodal data - Data preprocessing and feature engineering |
| Topic 4: Experimentation | 25% | - Experimental design - Model evaluation and comparison - Hypothesis testing - A/B testing |
| Topic 5: Software Development & Engineering | 15% | - Integration and deployment of multimodal AI systems - Python libraries for multimodal AI |
| Topic 6: Multimodal Data | 15% | - Applications and use cases - Handling and integrating text, image, and audio data |
| Topic 7: Core ML & AI Knowledge | 20% | - Key algorithms and techniques - Basic concepts and terminology |
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NEW QUESTION # 11
You have trained a text-to-image diffusion model. During inference, you notice that the generated images often lack fine-grained details and appear blurry. Which of the following techniques could you apply to improve the image quality without retraining the model?
Answer: A
Explanation:
Increasing the number of diffusion steps during sampling allows the model to refine the generated image more thoroughly, leading to finer details and reduced blurriness. The guidance scale controls how closely the generated image adheres to the input text prompt; increasing it typically improves adherence but can sometimes reduce diversity. Batch size primarily affects computational efficiency. Reducing the learning rate is relevant during training, not inference. Adding model layers requires retraining.
NEW QUESTION # 12
You are experimenting with a multimodal model that takes both text and audio as input. During evaluation, you notice that the model is heavily biased towards the text input, largely ignoring the audio. Which of the following techniques could you employ to mitigate this modality imbalance and encourage the model to effectively utilize both inputs? (Select all that apply)
Answer: D,E
Explanation:
Modality imbalance is a common issue in multimodal learning. Applying modality-specific dropout to the dominant modality (text, in this case) forces the model to rely more on the other modality (audio). A contrastive loss directly encourages the model to learn aligned representations between the two modalities. Increasing the audio encoder's learning rate (A) might help, but it is less targeted than dropout or contrastive loss. Reducing the text encoder size (D) is unlikely to be helpful in a controlled way. Replacing Audio features with raw waveform might introduce noise.
NEW QUESTION # 13
You are working with a multimodal generative model that combines text and image inputs. The model's performance is suboptimal when generating images conditioned on complex text descriptions. Which data analysis technique would be MOST effective in identifying the root cause of this issue?
Answer: D
Explanation:
Analyzing the correlation between text complexity and image quality directly investigates whether the model struggles with more intricate text descriptions. Sentiment analysis is relevant for identifying biases, but doesn't address the complexity issue directly. The average image resolution and pixel intensity are general image statistics and not specific to the multimodal problem. Object frequency in images can be useful but less direct than correlating text complexity with image quality.
NEW QUESTION # 14
You're building a multimodal model that predicts customer satisfaction based on their written reviews and associated call center audio recordings. You've pre-trained separate text and audio encoders. What's the MOST effective strategy to fuse these modalities for the final prediction task?
Answer: C
Explanation:
An attention mechanism allows the model to dynamically learn the importance of each modality based on the input. This is more flexible and effective than simple concatenation or averaging (A, B, E), which treat both modalities equally. Fine-tuning only one encoder (C) doesn't leverage the benefits of multimodal fusion. While concatenation (A) is a common starting point, attention provides a more nuanced and powerful way to combine modalities. Adding features (E) is also not effective.
NEW QUESTION # 15
You are evaluating a Generative A1 model for image captioning. Which of the following metrics is MOST appropriate for assessing the semantic similarity between the generated captions and the ground truth captions?
Answer: D
Explanation:
CIDEr (Consensus-based Image Description Evaluation) is specifically designed for image captioning and is highly correlated with human judgments of caption quality. While BLEU and ROUGE are useful for general text generation, CIDEr excels at capturing semantic similarity in image captions. Inception Score assesses the quality of generated images, not captions, and Perplexity measures the uncertainty of a language model.
NEW QUESTION # 16
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