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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Core Machine Learning and AI Knowledge | 20% | - Neural network architectures relevant to multimodal systems - Fundamental concepts of machine learning and deep learning - Generative AI principles and techniques |
| Topic 2: Multimodal Data | 15% | - Multimodal model architectures and integration - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation |
| Topic 3: Data Analysis and Visualization | 10% | - Analyzing multimodal datasets and outputs - Interpretation of generative AI outputs - Visualization techniques for model behavior and results |
| Topic 4: Trustworthy AI | 5% | - Ethical considerations and responsible use - Reliability, fairness, and safety in generative systems - Robustness and error mitigation |
| Topic 5: 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 6: Experimentation | 25% | - Model training, fine-tuning, and evaluation - Metrics and validation strategies for generative models - Experiment design and methodology |
| Topic 7: Performance Optimization | 10% | - Scalability and deployment considerations - Hardware acceleration with NVIDIA platforms - Model efficiency and inference optimization |
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NEW QUESTION # 20
When training a Variational Autoencoder (VAE) for generating new data points, which of the following objectives does the VAE optimize?
Answer: A
Explanation:
A VAE optimizes all three objectives. It aims to maximize the likelihood of the input data given the latent representation (reconstruction accuracy), minimize the KL divergence to ensure the latent space is well-structured and smooth, and maximize the similarity between the input and reconstructed data to ensure effective encoding and decoding.
NEW QUESTION # 21
You are evaluating the performance of an AI model for facial recognition. What is an important consideration when evaluating the model for bias?
Answer: C
Explanation:
Bias evaluation for facial recognition centers on measuring whether the model's accuracy - true positive rate, false positive rate, false match rate - is consistent across demographic subgroups (race, gender, age), rather than assuming a single aggregate accuracy figure represents performance fairly for all groups. This concern is well grounded empirically: independent benchmarking, including NIST's Face Recognition Vendor Test studies, has repeatedly documented substantial accuracy disparities across demographic groups in widely deployed facial recognition systems, with error rates for some subgroups measured many times higher than for others - a direct consequence of training data underrepresentation and the representativeness-bias issue covered elsewhere in this domain.
Processing speed (A) is a performance/latency engineering concern, not a bias or fairness concern - a model could process all demographic groups at identical speed while still exhibiting severe accuracy disparities between them. Facial expression recognition capability (C) addresses a different task dimension (emotion
/expression classification) than the identity-recognition bias question being asked. Operating system compatibility (D) is a software-deployment/engineering concern entirely unrelated to model fairness.
Subgroup accuracy disparities in facial recognition carry serious real-world consequences - misidentification risk in law enforcement or access-control contexts - which is why disaggregated (per-subgroup) evaluation, not just aggregate accuracy, is considered a baseline requirement under Trustworthy AI bias auditing practice.
Reference: Trustworthy AI domain - subgroup/disaggregated bias evaluation, fairness auditing.
NEW QUESTION # 22
You're building a chatbot that can understand both text and images. The chatbot is intended to answer questions about images uploaded by users. However, you observe that when presented with complex scenes containing multiple objects, the chatbot struggles to accurately identify and describe the objects being queried. Which of the following strategies would be MOST beneficial in improving the chatbot's performance on complex visual scenes?
Answer: A
Explanation:
Integrating an object detection model allows the chatbot to explicitly identify and localize the objects within the image, providing crucial information for answering questions about specific objects in complex scenes. A larger language model can help with general language understanding, but doesn't address the fundamental issue of object identification. Reducing image resolution or training on simple images will degrade performance on complex scenes. Removing image processing defeats the purpose.
NEW QUESTION # 23
You are building a system to translate spoken language into images. You have a large dataset of audio clips and corresponding images.
Which of the following is the MOST appropriate architecture?
Answer: C
Explanation:
Option C is the most appropriate. Transformer models can effectively handle sequence-to-sequence tasks and leverage attention mechanisms to capture the relationship between audio and visual features. The learned visual vocabulary helps in generating more coherent and realistic images. GANs (option B) could be part of the system but would likely need a transformer to provide the conditioned features.
NEW QUESTION # 24
You are training a multimodal model that combines audio and video dat
a. You observe that the model performs well on the training data but generalizes poorly to unseen data. Which of the following regularization techniques is MOST likely to improve the generalization performance in this scenario?
Answer: A
Explanation:
Data augmentation is the most effective regularization technique in this scenario because it increases the diversity of the training data, making the model more robust to variations in unseen data L1 and L2 regularization can help prevent over fitting, but data augmentation directly addresses the issue of limited training data. Dropout also helps, but data augmentation is generally more impactful for multimodal data where variations are significant. Early stopping can also help, but it is not as effective as data augmentation.
NEW QUESTION # 25
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