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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Performance Optimization | 10% | - Model efficiency and inference optimization - Hardware acceleration with NVIDIA platforms - Scalability and deployment considerations |
| Topic 2: Multimodal Data | 15% | - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation - Multimodal model architectures and integration |
| Topic 3: Software Development and Engineering | 15% | - Libraries, frameworks, and tools for multimodal AI - Development workflows for generative AI applications - Best practices for building and maintaining systems |
| Topic 4: Data Analysis and Visualization | 10% | - Visualization techniques for model behavior and results - Analyzing multimodal datasets and outputs - Interpretation of generative AI outputs |
| Topic 5: Experimentation | 25% | - Metrics and validation strategies for generative models - Model training, fine-tuning, and evaluation - Experiment design and methodology |
| Topic 6: 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 7: Trustworthy AI | 5% | - Ethical considerations and responsible use - Robustness and error mitigation - Reliability, fairness, and safety in generative systems |
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NEW QUESTION # 37
When using prompt engineering with text-to-image models, which of the following techniques are most effective in improving the fidelity and relevance of generated images to the input text?
Answer: B,C,D
Explanation:
Effective prompt engineering involves providing the model with enough specific details to understand the desired image attributes, style, and composition. Negative prompts help refine the output by explicitly excluding unwanted elements, leading to improved fidelity and relevance. Vague prompts are less effective, and omitting context can lead to undesirable or unexpected results.
NEW QUESTION # 38
You are developing an Avatar Cloud Engine (ACE) application for a virtual assistant that needs to generate realistic facial expressions based on user emotions detected from text. Which ACE microservice would be most directly responsible for this functionality?
Answer: E
Explanation:
The Facial Animation microservice within ACE is specifically designed to generate realistic facial expressions for avatars. While NLU detects the emotion, Facial Animation translates that emotion into corresponding facial movements.
NEW QUESTION # 39
You are tasked with deploying a generative A1 model for image inpainting using Triton Inference Server. The model requires significant GPU memory and you want to maximize throughput. Which Triton configuration parameters would be MOST important to tune, and why?
Answer: B
Explanation:
'instance_group' with 'KIND_GPIY assigns the model to specific GPUs. Increasing (B) leverages GPU parallelism. Enabling 'dynamic_batching' and setting (C) allows Triton to dynamically batch requests to maximize throughput. Model warmup reduces first request latency. (A) is incomplete (missing KIND_GPU). (D) is relevant for latency optimization but not as crucial for throughput in a memory-constrained scenario. Therefore both B and C are most crucial in optimizing throughput while dealing with memory constraint.
NEW QUESTION # 40
What does 'modality alignment' refer to?
Answer: C
Explanation:
Modality alignment is the process of establishing correspondence between semantically related elements across different data types - for example, matching a spoken word to its corresponding lip movement in video, or a caption phrase to the image region it describes. It is distinct from fusion (combining modalities into a joint representation) and from data integration (option B, which describes ingestion rather than alignment). Alignment can be explicit, as in dynamic time warping for audio-text synchronization, or implicit, learned end-to-end through attention mechanisms such as cross-attention in transformer architectures. CLIP's contrastive objective is itself a form of learned alignment: it pulls matching image-text pairs together in embedding space while pushing non-matching pairs apart, producing an aligned shared representation without explicit temporal correspondence. Alignment quality directly affects downstream fusion: poorly aligned modalities introduce noise that fusion layers cannot fully compensate for, which is why alignment is typically treated as a prerequisite step, not an afterthought.
Option A describes model reuse for custom tasks (closer to transfer learning), while C describes handling missing modality data, a separate robustness concern. Neither captures the correspondence-building nature of alignment. On the NCA-GENM exam, expect alignment questions to be paired with fusion and co-embedding concepts.
Reference: Multimodal Data domain - modality alignment, co-embedding spaces, cross-modal attention.
NEW QUESTION # 41
You are tasked with fine-tuning a pre-trained multimodal model for a new task involving image and text inputs. The pre-trained model was trained on a large dataset of image-caption pairs. Which of the following strategies would be MOST effective for transfer learning in this scenario, considering computational efficiency and performance?
Answer: D
Explanation:
Option C is the most effective strategy. Fine-tuning a subset of layers allows the model to adapt to the new task while leveraging the pre-trained knowledge. Freezing the lower layers preserves the general features learned from the large dataset, while fine-tuning the feature extraction layers allows the model to learn task-specific features. Fine-tuning all layers (Option B) can lead to overfitting and is computationally expensive. Freezing all layers except the classification head (Option A) may not provide sufficient adaptation. Training from scratch (Option D) is computationally expensive and requires a large dataset. Knowledge distillation (Option E) is also a valid option but may not be the most direct approach for transfer learning when the pre-trained model's architecture is suitable.
NEW QUESTION # 42
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