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| Section | Weight | Objectives |
|---|---|---|
| Experimentation | 25% | - Experiment design and methodology - Model training, fine-tuning, and evaluation - Metrics and validation strategies for generative models |
| Data Analysis and Visualization | 10% | - Interpretation of generative AI outputs - Visualization techniques for model behavior and results - Analyzing multimodal datasets and outputs |
| 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 |
| Core Machine Learning and AI Knowledge | 20% | - Generative AI principles and techniques - Fundamental concepts of machine learning and deep learning - Neural network architectures relevant to multimodal systems |
| Trustworthy AI | 5% | - Ethical considerations and responsible use - Reliability, fairness, and safety in generative systems - Robustness and error mitigation |
| Performance Optimization | 10% | - Hardware acceleration with NVIDIA platforms - Model efficiency and inference optimization - Scalability and deployment considerations |
| Multimodal Data | 15% | - Multimodal model architectures and integration - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation |
Question 1
During the process of data cleansing, which of the following steps is NOT typically performed?
A. Identifying and handling missing values
B. Collecting additional data
C. Removing duplicates
D. Transforming data into a different format
Question 2
You are developing a GenAI-Multimodal system that uses data from various sources. What is one potential issue you need to consider in relation to bias in data?
A. Bias in data is not a concern for AI systems as they are designed to be neutral and objective.
B. Bias in data can only be addressed after the AI system has been deployed.
C. The data used to train the AI system may not be representative of the population it is intended to serve.
D. Bias in data is irrelevant as long as the AI system produces accurate predictions.
Question 3
What is a common method to reduce the computational cost of deep learning models during inference?
A. Adding more convolutional filters.
B. By replacing activation functions in some neurons with simpler ones.
C. Pruning weights or neurons.
D. Increasing the batch size.
Question 4
You have a dataset containing information about sales performance for different regions in the last ten years.
Which type of data visualization would be most appropriate to compare the sales performance across regions on a year-by-year basis?
A. Pie chart
B. Line chart
C. Bar chart
D. Scatter plot
Question 5
What does 'modality alignment' refer to?
A. Addressing challenges related to missing or incomplete information across different modalities.
B. Aligning different modalities within multimodal data to ensure meaningful connections and associations.
C. The process of integrating diverse data types such as text, images, audio, time series, and geospatial information.
D. The integration of pretrained models to perform custom tasks involving different types of data.
Solutions:
| Question 1 Answer: B | Question 2 Answer: C | Question 3 Answer: C | Question 4 Answer: A | Question 5 Answer: B |
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