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| Section | Weight | Objectives |
|---|---|---|
| Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Experimentation | 25% | - Hypothesis testing - Experimental design - A/B testing - Model evaluation and comparison |
| Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
| Software Development & Engineering | 15% | - Integration and deployment of multimodal AI systems - Python libraries for multimodal AI |
| Performance Optimization | 10% | - Monitoring and improving system efficiency - Techniques for optimizing AI performance |
| Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
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NEW QUESTION # 25
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: A
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 # 26
A multimodal A1 model is trained on a dataset containing biased text and images. This bias leads to the model generating outputs that reinforce negative stereotypes. Which of the following steps are crucial for addressing and mitigating this bias during the model development lifecycle? (Select TWO)
Answer: A,E
Explanation:
Collecting a more diverse dataset helps to reduce the bias present in the training data. Adversarial training can be used to train the model to be more robust to biased inputs and generate fairer outputs. Increasing the learning rate or reducing the number of layers do not directly address bias. Model distillation mainly aims at model size reduction, not bias mitigation.
NEW QUESTION # 27
You have been given a dataset with missing values. What is the first step you should take with the data?
Answer: D
Explanation:
Before deciding *how* to handle missing data, best practice requires understanding *why* it's missing - analyzing whether missingness is Missing Completely at Random (MCAR, no systematic pattern), Missing at Random (MAR, related to other observed variables but not the missing value itself), or Missing Not at Random (MNAR, related to the missing value itself, e.g., patients with severe symptoms being less likely to complete a survey field). This diagnostic step determines which downstream handling strategy is statistically appropriate: naive row deletion under MNAR conditions can introduce systematic bias into the remaining dataset, while mean/median imputation applied blindly can distort variance and correlational structure if missingness isn't actually random.
Options B, C, and D each jump directly to a specific remedial action without first establishing whether that action is appropriate for the missingness pattern present. Removing rows (B) sacrifices sample size and can bias results if missingness correlates with the outcome of interest. Filling with a default value (C) without understanding the pattern risks introducing artificial structure that doesn't reflect the true underlying data.
Removing entire columns (D) may discard genuinely informative features if missingness in that column is low or non-systematic.
Only after this initial pattern analysis should you select an appropriate strategy: listwise deletion, mean/median
/mode imputation, model-based imputation (e.g., MICE, k-NN imputation), or explicit missingness indicators as additional features.
Reference: Data Analysis and Visualization domain - missing data diagnosis (MCAR/MAR/MNAR) prior to imputation strategy selection.
NEW QUESTION # 28
You're developing a multimodal model that takes both image and audio inputs to predict a relevant text description. You observe that the model is heavily biased towards the image data, effectively ignoring the audio input. Which of the following techniques could you employ to address this modality imbalance and ensure the model effectively utilizes both input modalities?
Answer: B,C,D,E
Explanation:
Increasing the learning rate for the audio pathway allows it to update its weights more aggressively, potentially counteracting the image bias. Applying modality-specific dropout to the image pathway forces the model to rely less on image features and more on audio. Oversampling the audio data ensures that the model sees more examples from the audio modality during training. Reducing the dimensionality of the image features can prevent them from dominating the fusion process. Increasing batch size is not specific to each modality and does not directly deal with modality imbalance, but can influence training dynamics.
NEW QUESTION # 29
Consider the following Python code snippet using PyTorch, intended for fusing image and text features in a multimodal model. Assume 'image_featureS and 'text_features' are tensors of shape Which of the following fusion methods is implemented in this code?
Answer: B
Explanation:
The code snippet uses 'torch.cat((image_features, text_features), dim=l y , which concatenates the image and text features along the feature dimension (dim=l This is a concatenation-based fusion method. Addition would involve 'image_features + text_featureS. Gated or cross-modal attention would involve learnable weights and attention mechanisms. Tensor product involves reshaping and matrix multiplication of the features. The given answer is A, but the code snippet is missing. Please provide the code for an accurate explanation.
NEW QUESTION # 30
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