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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Trustworthy AI | 5% | - Ethical considerations and responsible use - Robustness and error mitigation - Reliability, fairness, and safety in generative systems |
| Topic 2: Experimentation | 25% | - Model training, fine-tuning, and evaluation - Experiment design and methodology - Metrics and validation strategies for generative models |
| Topic 3: 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 |
| Topic 4: Data Analysis and Visualization | 10% | - Analyzing multimodal datasets and outputs - Visualization techniques for model behavior and results - Interpretation of generative AI outputs |
| Topic 5: Multimodal Data | 15% | - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation - Multimodal model architectures and integration |
| Topic 6: Performance Optimization | 10% | - Hardware acceleration with NVIDIA platforms - Model efficiency and inference optimization - Scalability and deployment considerations |
| Topic 7: Software Development and Engineering | 15% | - Development workflows for generative AI applications - Best practices for building and maintaining systems - Libraries, frameworks, and tools for multimodal AI |
1. In a multimodal machine learning context, how are different modalities usually linked to each other?
A) Different modalities are linked through a shared representation that captures the relationships between the modalities.
B) Different modalities are linked through random connections.
C) Different modalities are not linked to each other in a multimodal machine learning context.
D) Different modalities are linked through separate models that are ensembled by tree-based models.
2. Which technique is commonly used to speed up AI model training and inference on hardware accelerators?
A) Dropout
B) Quantization
C) Data augmentation
D) Model enlargement
3. What is the significance of A/B testing in ML software engineering?
A) A/B testing helps in optimizing the hyperparameters of a machine learning model.
B) A/B testing is irrelevant in ML software engineering.
C) A/B testing is used to measure the impact of changes in the user interface of a ML application.
D) A/B testing helps in evaluating the performance and effectiveness of different machine learning models.
4. During the process of data cleansing, which of the following steps is NOT typically performed?
A) Transforming data into a different format
B) Identifying and handling missing values
C) Collecting additional data
D) Removing duplicates
5. Which visualization technique is suitable for representing the distribution of performance scores for different multimodal ML models over different modalities?
A) Heatmap
B) Pie chart
C) Box plot
D) Histogram
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: C |
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