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| Section | Weight | Objectives |
|---|---|---|
| Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
| Performance Optimization | 10% | - Techniques for optimizing AI performance - Monitoring and improving system efficiency |
| Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
| Experimentation | 25% | - Experimental design - Model evaluation and comparison - Hypothesis testing - A/B testing |
| Software Development & Engineering | 15% | - Python libraries for multimodal AI - Integration and deployment of multimodal AI systems |
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NEW QUESTION # 25
Which visualization technique is suitable for representing the distribution of performance scores for different multimodal ML models over different modalities?
Answer: B
Explanation:
A box plot (box-and-whisker plot) summarizes the distribution of a numeric variable - median, interquartile range, and outliers - as a single compact glyph, and critically, multiple box plots can be placed side by side to compare distributions across categorical groupings. This makes it well suited to the scenario described:
comparing the spread and central tendency of performance scores across several models, further faceted by modality, in one readable figure. Box plots make skew, variance, and outlier prevalence immediately comparable across groups in a way a single summary statistic (like mean accuracy) cannot.
A histogram (B) shows the distribution of a single variable well but does not scale cleanly to side-by-side comparison across many model/modality combinations without becoming visually cluttered. A heatmap (A) is excellent for showing a matrix of values (e.g., mean score per model × modality pair) but represents point estimates, not distributions - it cannot convey variance or spread. A pie chart (D) is inappropriate for any continuous performance metric.
In practice, a violin plot - which overlays a kernel density estimate on the box plot's summary statistics - is often preferred when the underlying distribution's shape (e.g., bimodality) matters, but among the given options, the box plot is the correct choice for distributional comparison across groups.
Reference: Data Analysis and Visualization domain - comparative distribution visualization, box plots vs.
heatmaps.
NEW QUESTION # 26
In the context of generative models, what is the primary purpose of using a normalizing flow?
Answer: C
Explanation:
Normalizing flows are used to transform a simple probability distribution into a complex one by applying a series of invertible transformations. This allows the model to learn more complex data distributions and generate more realistic samples.
NEW QUESTION # 27
You are experimenting with different multimodal transformer architectures for a video understanding task. You are using a large pre- trained model and fine-tuning it on your specific dataset. You observe that the model is overfitting and struggling to generalize to unseen videos. Which of the following techniques would be most effective in mitigating overfitting in this scenario? (Choose two)
Answer: B,E
Explanation:
Weight decay and dropout are standard regularization techniques that help prevent overfitting. Data augmentation increases the diversity of the training data, improving the model's ability to generalize. Reducing the number of layers is a potentially viable option, but requires experimentation to achieve optimum performance.
NEW QUESTION # 28
You are fine-tuning a pre-trained multimodal model for a visual question answering (VQA) task. You notice that the model performs well on common questions but struggles with questions requiring reasoning about object relationships (e.g., 'Is the object to the left of the table bigger than the one on the table?'). What data augmentation technique would MOST likely improve performance on these challenging questions?
Answer: D
Explanation:
Generating synthetic data specifically designed to challenge the model's reasoning abilities about object relationships is the most effective way to improve performance on those types of questions. Cropping, noise, and object replacement might help with robustness, but not with relational reasoning. Rotation won't inherently improve reasoning about relationships.
NEW QUESTION # 29
You are building a multimodal model that combines text and images to generate product descriptions. The text data is tokenized using spaCy, and the image data is represented as feature vectors extracted from a pre-trained ResNet model. How can you effectively align and fuse these heterogeneous data types before feeding them into a downstream generative model?
Answer: A,B
Explanation:
Direct concatenation or averaging doesn't capture the complex relationships between modalities. Cross-modal attention allows the model to learn which parts of the image are most relevant to the text, leading to better alignment and fusion. Projecting both modalities into a common embedding space allows for a unified representation that can be effectively used by the downstream generative model.
NEW QUESTION # 30
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