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| Section | Weight | Objectives |
|---|---|---|
| Multimodal Data | 15% | - Applications and use cases - Handling and integrating text, image, and audio data |
| Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Software Development & Engineering | 15% | - Integration and deployment of multimodal AI systems - Python libraries for multimodal AI |
| Performance Optimization | 10% | - Monitoring and improving system efficiency - Techniques for optimizing AI performance |
| Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
| Experimentation | 25% | - Model evaluation and comparison - Hypothesis testing - A/B testing - Experimental design |
| Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
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NEW QUESTION # 30
You are analyzing a dataset of customer reviews for a new product using Natural Language Processing (NLP). The dataset contains both positive and negative reviews, but a significant portion of the negative reviews uses sarcasm. Which of the following NLP techniques would be MOST effective in accurately identifying the sentiment expressed in sarcastic reviews?
Answer: C
Explanation:
Pre-trained transformer models capture contextual information and complex relationships between words, which is crucial for detecting sarcasm. Bag-of-words, sentiment lexicons, and rule-based systems rely on individual words or simple patterns and are easily fooled by sarcasm. Averaging word embeddings loses the contextual information vital for sarcasm detection.
NEW QUESTION # 31
What does mixed-precision training refer to?
Answer: D
Explanation:
Mixed-precision training performs the bulk of computation - matrix multiplications and convolutions - in a lower-precision floating-point format (typically FP16 or BF16 on NVIDIA Tensor Cores) while maintaining a master copy of weights and accumulating certain sensitive operations (like loss scaling and gradient accumulation) in FP32 to preserve numerical stability. The result is substantially faster training throughput and reduced memory footprint, since lower-precision arithmetic runs at higher effective FLOPS on hardware with dedicated Tensor Cores, without a meaningful loss of final model accuracy when combined with techniques like dynamic loss scaling to prevent gradient underflow.
Note that option A's specific mention of "double-precision" (FP64) is not how mixed precision is practiced in modern deep learning - production mixed-precision training combines FP16/BF16 with FP32, not FP64, since FP64 offers no throughput advantage on Tensor Core hardware and is rarely used in training pipelines.
Despite that imprecision in the option's wording, A is still the only choice capturing the correct underlying concept: combining multiple numeric precision levels within one training run. Options B, C, and D all misdescribe mixed precision as a *data-type* or *modality* strategy, confusing numerical precision (a performance/optimization concept) with data modality (a multimodal-data concept) - a distinction the exam tests directly.
Reference: Performance Optimization domain - mixed-precision training, Tensor Cores, loss scaling.
NEW QUESTION # 32
You are tasked with monitoring a deployed multimodal model that takes text and image inputs to predict customer satisfaction. The model is deployed in a production environment and handles thousands of requests per day. Which of the following monitoring metrics would be MOST crucial for identifying potential issues related to data drift and model degradation?
Answer: B,C
Explanation:
Monitoring the distribution of input features (B) helps detect data drift, indicating that the input data characteristics have changed compared to the training data. Monitoring the distribution of predicted satisfaction scores (D) can reveal if the model's predictions are becoming less accurate or biased over time. Average prediction latency (A) and GPU utilization (E) are more related to infrastructure performance. Training loss (C) is irrelevant once the model is deployed.
NEW QUESTION # 33
Which of the following techniques are commonly used to address the 'hallucination' problem in generative A1 models, where the model generates content that is factually incorrect or nonsensical? (Select all that apply)
Answer: A,D,E
Explanation:
RLHF helps align the model with human values, reducing nonsensical outputs. RAG grounds the generation process in external knowledge, ensuring factual accuracy. Lowering the temperature reduces the likelihood of sampling low-probability, potentially hallucinated, tokens. Increasing model Size or using adversarial training can improve model performance, but doesn't directly address hallucination.
NEW QUESTION # 34
Which of the following regularization techniques is MOST effective for preventing overfitting in a multimodal deep learning model with a large number of parameters and complex interactions between different modalities?
Answer: A
Explanation:
Dropout is particularly effective for preventing overfitting in deep neural networks with many parameters. It randomly drops out neurons during training, forcing the network to learn more robust features and reducing co-adaptation between neurons. L1 and L2 regularization can also help, but they might not be as effective as dropout for very deep and complex models. Batch Normalization primarily helps with training stability and speed, although it can also have a slight regularization effect. Early stopping prevents overfitting by stopping training when the performance on a validation set starts to degrade.
NEW QUESTION # 35
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