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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Multimodal Data | 15% | - Applications and use cases - Handling and integrating text, image, and audio data |
| Topic 2: Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
| Topic 3: Data Analysis & Visualization | 10% | - Visualization techniques for multimodal data - Data preprocessing and feature engineering |
| Topic 4: Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Topic 5: Performance Optimization | 10% | - Monitoring and improving system efficiency - Techniques for optimizing AI performance |
| Topic 6: Software Development & Engineering | 15% | - Integration and deployment of multimodal AI systems - Python libraries for multimodal AI |
| Topic 7: Experimentation | 25% | - A/B testing - Hypothesis testing - Model evaluation and comparison - Experimental design |
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NEW QUESTION # 10
You are designing an experiment to compare two different multimodal A1 model architectures for video summarization. Model A is a transformer-based model, and Model B is a recurrent neural network (RNN)-based model. Which of the following evaluation metrics would be MOST appropriate for comparing the quality of the generated summaries, considering both content relevance and fluency?
Answer: B
Explanation:
ROUGE is a recall-based metric that effectively measures the overlap between the generated summary and reference summaries. It's well-suited for evaluating the content relevance of summaries. BLEU, while used for text generation, focuses on precision and might penalize summaries with different wording but similar meaning. Perplexity measures fluency but not relevance. MSE is inappropriate for text. Inception score is used primarily for images.
NEW QUESTION # 11
You're using Stable Diffusion with a custom prompt to generate images of landscapes. You notice that the generated images consistently lack detail and appear blurry, despite increasing the number of inference steps. Which of the following prompt engineering techniques, combined with appropriate parameter tuning, is MOST likely to address this issue and improve the image's sharpness and detail?
Answer: E
Explanation:
Adding keywords specifically related to image quality ('photorealistic', 'high resolution', '8k', 'detailed') helps guide the model towards generating sharper and more detailed images. 'clip_skip' influences the model to incorporate more details into the output images. Adjusting 'clip_skip' along with quality prompt keywords will enhance the image quality.
NEW QUESTION # 12
Which of the following best describes the role of machine learning in handling multimodal data?
Answer: A
Explanation:
Machine learning's role in multimodal contexts is to build models capable of jointly learning from, aligning, and interpreting heterogeneous data types - text, images, audio, video, time series, and beyond - extracting patterns and relationships that span modality boundaries rather than treating each stream in isolation. This is the general framing that unifies the more specific concepts tested elsewhere in this domain (fusion strategies, shared embedding spaces, cross-modal attention): all of them are mechanisms in service of this broader goal of learning from diverse data types jointly.
Option A incorrectly narrows the scope to text alone, contradicting the entire premise of multimodal learning.
Option B is not a defining characteristic - multimodal models often require *more*, not less, data to learn reliable cross-modal correspondences, though they can improve sample efficiency for a given task relative to a comparably-performing unimodal model by exploiting complementary signal across modalities; this is a possible benefit, not the defining role. Option C overstates ML's function; human oversight, labeling, validation, and bias auditing remain integral to responsible multimodal system development, particularly under Trustworthy AI principles - ML augments rather than eliminates human involvement in the broader data-analysis workflow.
Reference: Multimodal Data domain - foundational definition of multimodal machine learning.
NEW QUESTION # 13
You are tasked with deploying a generative A1 model using NVIDIA Triton Inference Server. Which configuration parameter within Triton is MOST crucial for optimizing throughput and minimizing latency when serving a large number of concurrent requests?
Answer: B
Explanation:
The 'Instance Group Count' parameter in Triton determines how many instances of the model are loaded onto the GPU(s) and/or CPU(s). Increasing the number of instances (up to the hardware's capacity) allows Triton to handle more concurrent requests in parallel, thereby improving throughput and reducing latency. While batching and max queue size can also help, the instance count is the most fundamental for parallelism. The default model filename is irrelevent to performance and input data type is a requirement not a performance consideration.
NEW QUESTION # 14
When deploying a multimodal Generative A1 model for a real-time application, such as a virtual assistant that responds to voice commands and displays relevant images, which of the following considerations are MOST critical for ensuring low latency and a smooth user experience? (Select TWO)
Answer: A,C
Explanation:
Model quantization and pruning reduce the model's size and computational complexity, leading to faster inference. Asynchronous processing and caching allow for pre-computation and storage of frequently used data, minimizing delays. Prioritizing accuracy over speed (A) is not suitable for real-time applications where responsiveness is crucial. Deploying on a single CPU core (D) would severely limit performance. Disabling logging (E) is detrimental for debugging and monitoring.
NEW QUESTION # 15
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