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| Section | Weight | Objectives |
|---|---|---|
| Multimodal Data | 15% | - Applications and use cases - Handling and integrating text, image, and audio data |
| Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
| Experimentation | 25% | - Model evaluation and comparison - A/B testing - Experimental design - Hypothesis testing |
| Trustworthy AI | 5% | - Ethical considerations in AI development - Ensuring fairness and transparency |
| 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 |
| Software Development & Engineering | 15% | - Python libraries for multimodal AI - Integration and deployment of multimodal AI systems |
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NEW QUESTION # 51
You are designing a IJ-Net architecture for semantic segmentation of medical images. Your input images are 512x512 with 3 channels.
You want to ensure the final output segmentation map is also 512x512. Which of the following design choices are crucial for achieving this resolution, considering the downsampling and upsampling stages?
Answer: C
Explanation:
Maintaining the same resolution in IJ-Net requires symmetric downsampling and upsampling and the crucial use of skip connections. These connections pass feature maps from the downsampling (encoder) path to the corresponding layers in the upsampling (decoder) path, allowing the decoder to recover spatial information lost during downsampling. Option A omits skip connections. Option C's nearest neighbor interpolation can lead to blocky artifacts. Option D describes a bottleneck optimization unrelated to output resolution. Option E is about training parameters.
NEW QUESTION # 52
You are working with a large dataset and want to visualize the distribution of a continuous variable. Which type of data visualization would be most appropriate?
Answer: B
Explanation:
A histogram bins a continuous variable into contiguous intervals and plots the frequency (or density) of observations falling into each bin, making it the standard tool for visualizing the shape of a continuous distribution - skewness, modality, spread, and outliers are all immediately visible. This distinguishes it from a bar chart (B), which is designed for discrete or categorical variables where bars are separated and ordering is often arbitrary; applying a bar chart to continuous data loses the notion of a numeric scale between categories.
A line chart (C) is appropriate for showing trends of a variable across an ordered sequence, typically time, not for summarizing the overall shape of a value distribution. A pie chart (D) shows proportions of a whole across categorical segments and becomes visually unreadable and statistically meaningless for continuous data with many possible values.
In practice, histogram bin width is a critical hyperparameter: too few bins oversmooth the distribution and hide multimodality, while too many bins introduce noise. Tools like Freedman-Diaconis or Sturges' rule provide principled starting points, and kernel density estimates (KDE) are often overlaid as a smoothed alternative when bin-width sensitivity is a concern.
Reference: Data Analysis and Visualization domain - chart selection by variable type (continuous vs.
categorical vs. temporal).
NEW QUESTION # 53
You're evaluating the performance of a video captioning model. The model generates captions for video clips. You notice that while the captions are generally accurate, they often lack detail and creativity. Which metric(s) would be MOST suitable for assessing the diversity and originality of the generated captions? (Select all that apply)
Answer: E
Explanation:
Self-BLEU calculates the BLEU score of a set of generated captions against themselves. A lower Self-BLEU score indicates higher diversity, as it means the captions are less similar to each other. The other metrics (BLEU, CIDEr, SPICE, ROUGE) primarily focus on accuracy and semantic similarity to reference captions, not diversity. Although, SPICE could capture the semantic similarity of different captions, Self-BLEU is most used.
NEW QUESTION # 54
Consider a multimodal generative A1 model that produces images based on textual prompts. The model is prone to generating images that are similar to those in the training data, resulting in a lack of novelty. Which hyperparameter adjustment would be MOST effective in increasing the diversity of the generated images?
Answer: D
Explanation:
Increasing the temperature parameter during decoding introduces more randomness into the sampling process, leading to a wider range of possible outputs and thus increasing the diversity of generated images. Decreasing batch size doesn't directly affect diversity. Reducing the learning rate affects training, not the diversity of generated images after training. Decreasing layers might reduce model capacity but not necessarily increase diversity. Increasing weight decay can prevent overfitting but doesn't directly address the lack of novelty in generated images.
NEW QUESTION # 55
You're building a text generation model using a Transformer architecture. You observe that the generated text often gets stuck in repetitive loops, producing the same phrase over and over. Which of the following strategies is MOST likely to mitigate this issue?
Answer: C
Explanation:
Increasing the temperature parameter introduces more randomness into the sampling process during text generation. This makes the model less likely to repeatedly select the same high-probability token, thus reducing repetitive loops. Decreasing the learning rate or using a smaller vocabulary are unlikely to solve the repetition problem directly Beam search can sometimes amplify repetition, and increasing the number of attention heads primarily affects model capacity, not repetition.
NEW QUESTION # 56
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