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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Core ML & AI Knowledge | 20% | - Key algorithms and techniques - Basic concepts and terminology |
| Topic 2: Performance Optimization | 10% | - Techniques for optimizing AI performance - Monitoring and improving system efficiency |
| Topic 3: Software Development & Engineering | 15% | - Python libraries for multimodal AI - Integration and deployment of multimodal AI systems |
| Topic 4: Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Topic 5: Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
| Topic 6: Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Topic 7: Experimentation | 25% | - Hypothesis testing - A/B testing - Model evaluation and comparison - Experimental design |
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NEW QUESTION # 49
You are building a system that generates image captions from images and vice vers a. Which evaluation metric(s) are MOST appropriate to assess the quality of the generated content? (Select all that apply)
Answer: B,D,E
Explanation:
BLEU and ROUGE are common metrics for evaluating text generation quality by comparing generated text to reference text. FID is appropriate for assessing the quality and diversity of generated images. Inception score is also relevant to asses quality and diversity but less so than FID. Accuracy isn't suitable in this context. It's more fitted to classification tasks.
NEW QUESTION # 50
You are building a multimodal application that needs to understand both image and text dat a. You want to use a pre-trained model but fine-tune it for your specific task. Which of the following strategies is MOST effective for fine-tuning a large pre-trained multimodal model?
Answer: A
Explanation:
Fine-tuning the entire model with a small learning rate allows the model to adapt to the specific nuances of the new task while leveraging the knowledge already learned during pre-training. Freezing layers can limit adaptability. Training only a new head might not fully utilize the pre-trained features.
NEW QUESTION # 51
You're developing a system that translates spoken language into sign language animations. Which of the following losses would be MOST suitable for training the model to generate realistic and accurate sign language sequences from speech input?
Answer: E
Explanation:
MSE loss ensures accurate joint positioning, while the temporal smoothness loss prevents jerky and unnatural movements. Cross-entropy is suitable for classification tasks, not continuous sequence generation. Cosine Similarity between embeddings might encourage general alignment, but doesn't guarantee accurate pose reproduction and Binary Cross entropy is only good for Binary Classification tasks.
NEW QUESTION # 52
You are developing a ML model for image classification. You have a dataset with 10,000 images of cats, dogs and birds. Which of the following ML models would be the most appropriate choice for this task?
Answer: B
Explanation:
CNNs are the standard architecture for image classification because their convolutional layers exploit the spatial locality and translation invariance inherent to image data: learned filters detect local patterns (edges, textures, shapes) that compose hierarchically into higher-level features (parts, objects) as depth increases, without requiring the manual feature engineering that traditional models would need to reach comparable accuracy on raw pixel data. Pooling layers further provide a degree of spatial invariance, and parameter sharing across the image keeps the model tractable relative to a fully connected network operating on raw pixels.
Logistic Regression (A) is a linear classifier that operates on flattened feature vectors; applied directly to raw pixels of a 3-class image problem, it cannot capture the non-linear spatial structure needed to separate cats, dogs, and birds reliably, though it could serve as a baseline or as the final classification head atop CNN- extracted features. K-Means (B) is an unsupervised clustering algorithm - inappropriate here because the task is supervised classification with labeled classes. Linear Regression (C) predicts continuous outputs and is not designed for categorical class prediction at all.
For 10,000 labeled images, a CNN (potentially fine-tuned from a pretrained backbone via transfer learning, given the modest dataset size) is the appropriate and industry-standard choice.
Reference: Core Machine Learning and AI Knowledge domain - CNN architecture for image classification.
NEW QUESTION # 53
You are developing a system to generate captions for videos. The video frames are processed using a pre-trained ResNet model, and the audio track is processed using a pre-trained Wav2Vec model. Which of the following techniques is MOST suitable for aligning the visual and audio features to generate accurate and coherent captions?
Answer: C
Explanation:
Cross-attention allows the model to learn the temporal relationships and dependencies between the visual and audio modalities. The audio features can attend to relevant visual features at each time step, and vice versa, leading to better alignment and more coherent captions. Simple concatenation and averaging are less effective at capturing these complex relationships. Ignoring the audio track loses valuable information.
NEW QUESTION # 54
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