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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Core ML & AI Knowledge | 20% | - Key algorithms and techniques - Basic concepts and terminology |
| Topic 2: Data Analysis & Visualization | 10% | - Visualization techniques for multimodal data - Data preprocessing and feature engineering |
| Topic 3: Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Topic 4: Software Development & Engineering | 15% | - Python libraries for multimodal AI - Integration and deployment of multimodal AI systems |
| Topic 5: Performance Optimization | 10% | - Techniques for optimizing AI performance - Monitoring and improving system efficiency |
| Topic 6: Multimodal Data | 15% | - Applications and use cases - Handling and integrating text, image, and audio data |
| Topic 7: Experimentation | 25% | - Experimental design - A/B testing - Model evaluation and comparison - Hypothesis testing |
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NEW QUESTION # 46
Which metric is commonly used to evaluate machine-translation models?
Answer: B
Explanation:
BLEU (Bilingual Evaluation Understudy) is the standard automatic metric for evaluating machine translation quality. It measures n-gram precision - the overlap of contiguous word sequences (unigrams through typically 4-grams) between the model's translated output and one or more human reference translations - combined with a brevity penalty to discourage overly short translations that could otherwise achieve artificially high precision. BLEU scores range from 0 to 1 (or 0-100 as a percentage), with higher scores indicating closer alignment to reference translations.
The distractors represent metrics standard to other task families: F1 score (A) evaluates classification tasks by balancing precision and recall over discrete positive/negative predictions, ill-suited to open-ended text generation where there is no fixed set of "correct" tokens. Accuracy (B) similarly assumes a discrete correct
/incorrect judgment, inappropriate for translation where multiple valid phrasings can convey the same meaning. Mean Absolute Error (C) is a regression metric measuring average magnitude of numeric prediction error, irrelevant to text output evaluation entirely.
It's worth noting BLEU has known limitations - it correlates imperfectly with human judgments of fluency and can penalize valid paraphrases - which has motivated complementary metrics like METEOR, ROUGE (more common for summarization), and learned metrics like BERTScore, though BLEU remains the benchmark most commonly referenced for translation specifically.
Reference: Core Machine Learning and AI Knowledge / Multimodal Data domains - task-specific evaluation metrics (BLEU for translation, WER for ASR, MOS for TTS).
NEW QUESTION # 47
You are tasked with building a multimodal generative AI model to create marketing content from product images and descriptions. The image encoder uses a pre-trained ResNet50 model, and the text encoder uses a pre-trained BERT model. After initial training, the generated content frequently misinterprets the image. Which of the following strategies is MOST effective in improving the model's ability to correctly interpret the image within the multimodal context?
Answer: C
Explanation:
Fine-tuning ResNet50 with a relevant image dataset and a contrastive loss function directly addresses the issue of misinterpreting the image. Freezing weights prevents learning, increasing BERT's learning rate imbalances the model, and a simpler image encoder might lose crucial image details. Decreasing batch size can improve generalization but isn't the primary solution for image misinterpretation.
NEW QUESTION # 48
You are working with a large dataset of images to train a Generative A1 model. You suspect that some images are corrupted or of poor quality, which could negatively impact training. Which of the following methods would be the MOST effective in identifying and removing these problematic images?
Answer: A,B,E
Explanation:
Checking file integrity to remove corruption images is an important first step. Computing Image Sharpness is an effective way to programmatically identify and filter blur or out-of-focus Images. Using pre-trained image assessment models is another advanced and automativ approach to identifying and removing images of low quality, even if they are not overtly corrupted. Manually checking would take too long. Average pixel intensity is often useful to filter.
NEW QUESTION # 49
When training a multimodal generative model for image captioning, you notice the model generates grammatically correct but generic and uninformative captions. Which technique is MOST likely to improve the in formativeness and specificity of the generated captions?
Answer: E
Explanation:
Diverse beam search or sampling strategies encourage the model to explore different caption possibilities during inference, leading to more diverse and informative captions. Standard beam search often converges to the most likely caption, which tends to be generic. Increasing the image encoder Size might improve image feature extraction but doesn't directly address the caption informativeness problem. Decreasing the learning rate is a general training technique that might improve convergence but doesn't specifically target caption informativeness.
NEW QUESTION # 50
You are working with a large multimodal dataset containing images and text. You want to efficiently load and preprocess this data for training a generative A1 model on an NVIDIA GPU. Which of the following approaches would be most effective for maximizing data loading speed and GPU utilization?
Answer: D
Explanation:
NVIDIA DALI is specifically designed for accelerating data loading and preprocessing on NVIDIA GPUs. It allows you to perform tasks like image decoding, resizing, and data augmentation directly on the GPIJ, minimizing CPIJ overhead and maximizing GPU utilizatiom Loading the entire dataset into CPU memory is impractical for large datasets. Python-based data loaders can be slow due to the GIL (Global Interpreter Lock). Querying a relational database adds overhead. Compressing the dataset can save storage space but may introduce decompression bottlenecks during training.
NEW QUESTION # 51
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