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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Trustworthy AI | 5% | - Robustness and error mitigation - Ethical considerations and responsible use - Reliability, fairness, and safety in generative systems |
| Topic 2: Software Development and Engineering | 15% | - Best practices for building and maintaining systems - Development workflows for generative AI applications - Libraries, frameworks, and tools for multimodal AI |
| Topic 3: Experimentation | 25% | - Experiment design and methodology - Model training, fine-tuning, and evaluation - Metrics and validation strategies for generative models |
| Topic 4: Multimodal Data | 15% | - Characteristics of text, image, and audio data - Multimodal model architectures and integration - Data preprocessing, fusion, and representation |
| Topic 5: Data Analysis and Visualization | 10% | - Analyzing multimodal datasets and outputs - Interpretation of generative AI outputs - Visualization techniques for model behavior and results |
| Topic 6: Performance Optimization | 10% | - Scalability and deployment considerations - Model efficiency and inference optimization - Hardware acceleration with NVIDIA platforms |
| Topic 7: Core Machine Learning and AI Knowledge | 20% | - Fundamental concepts of machine learning and deep learning - Neural network architectures relevant to multimodal systems - Generative AI principles and techniques |
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NEW QUESTION # 11
You're training a VQA (Visual Question Answering) model. During evaluation, you notice the model performs well on common object recognition tasks but struggles with questions requiring reasoning about object relationships or scene understanding. What are the MOST effective strategies to improve the model's performance on these complex reasoning tasks? (Choose two)
Answer: D,E
Explanation:
More sophisticated attention mechanisms help the model focus on relevant image regions. A larger, more diverse dataset provides the model with more examples of complex reasoning scenarios. Increasing the image embedding size may help but is not as targeted. Decreasing the learning rate is a general optimization technique, and using a simpler RNN would likely degrade performance.
NEW QUESTION # 12
You are training a multimodal Generative A1 model for generating video captions. The model is overfitting to the training data, resulting in poor generalization to unseen videos. Which of the following regularization techniques would be MOST suitable to mitigate overfitting?
Answer: A,B,C,D
Explanation:
Dropout randomly deactivates neurons during training, preventing the model from relying on specific neurons. L1 regularization adds a penalty to the loss function based on the absolute values of the weights, encouraging sparsity. Data augmentation increases the diversity of the training data. Early stopping prevents the model from training for too long and overfitting. Batch normalization primarily improves training stability and speed but also provides some regularization.
NEW QUESTION # 13
You have been given a dataset with missing values. What is the first step you should take with the data?
Answer: B
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 # 14
Consider a multimodal dataset consisting of product reviews (text), product images, and customer demographics. You want to build a model that can predict customer satisfaction based on all three modalities. However, you suspect that there might be complex interactions between these modalities that are not easily captured by simple concatenation or averaging. What approach would be most effective for modeling these interactions?
Answer: C,E
Explanation:
Tensor fusion networks are designed to model complex, higher-order interactions between modalities. They create a tensor representation that captures all possible combinations of features from different modalities. This allows the model to learn intricate relationships that would be missed by simpler fusion techniques. Transfer learning is effective in scenarios where pre-trained models for image and text processing help boost the accuracy of final layer during downstream task.
NEW QUESTION # 15
You are tasked with monitoring a deployed multimodal model that takes text and image inputs to predict customer satisfaction. The model is deployed in a production environment and handles thousands of requests per day. Which of the following monitoring metrics would be MOST crucial for identifying potential issues related to data drift and model degradation?
Answer: B,C
Explanation:
Monitoring the distribution of input features (B) helps detect data drift, indicating that the input data characteristics have changed compared to the training data. Monitoring the distribution of predicted satisfaction scores (D) can reveal if the model's predictions are becoming less accurate or biased over time. Average prediction latency (A) and GPU utilization (E) are more related to infrastructure performance. Training loss (C) is irrelevant once the model is deployed.
NEW QUESTION # 16
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