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| Section | Weight | Objectives |
|---|---|---|
| Data Analysis & Visualization | 10% | - Visualization techniques for multimodal data - Data preprocessing and feature engineering |
| Trustworthy AI | 5% | - Ethical considerations in AI development - Ensuring fairness and transparency |
| Performance Optimization | 10% | - Monitoring and improving system efficiency - Techniques for optimizing AI performance |
| Software Development & Engineering | 15% | - Python libraries for multimodal AI - Integration and deployment of multimodal AI systems |
| Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Experimentation | 25% | - A/B testing - Experimental design - Model evaluation and comparison - Hypothesis testing |
| Core ML & AI Knowledge | 20% | - Key algorithms and techniques - Basic concepts and terminology |
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NEW QUESTION # 49
You're training a model to generate code snippets from natural language descriptions. You are using a Transformer architecture and a large dataset of code examples. You notice the model frequently generates syntactically correct code, but the code doesn't accurately implement the described functionality (i.e., it's semantically incorrect). Select TWO methods which could improve the semantic correctness of the generated code.
Answer: A,D
Explanation:
Fine-tuning with unit tests (B) and using reinforcement learning with unit test rewards (D) directly address semantic correctness- The model learns to generate code that not only compiles but also behaves as expected- Increasing Transformer layers (A) or vocabulary size (E) might improve syntactic correctness or fluency, but don't guarantee semantic accuracy Beam search (C) generates more diverse outputs, but doesn't explicitly guide the model towards semantic correctness.
NEW QUESTION # 50
You are fine-tuning a pre-trained language model for a specific task. You notice that the model performs well on the training data but poorly on the validation dat a. Which of the following techniques can help mitigate this overfitting problem? (Select TWO)
Answer: C,E
Explanation:
Overfitting occurs when a model learns the training data too well and fails to generalize to new data. Weight decay (L2 regularization) penalizes large weights, preventing the model from becoming too complex. Dropout randomly deactivates neurons during training, forcing the model to learn more robust features. Increasing the learning rate might worsen overfitting. Decreasing the batch size can sometimes act as a regularizer, but its primary effect is on the training dynamics. While more training data is generally beneficial, if the new data is very similar to the existing training data, it won't necessarily solve the overfitting issue.
NEW QUESTION # 51
In multimodal machine learning, what does 'early fusion' refer to?
Answer: A
Explanation:
Early fusion concatenates or otherwise combines raw or lightly processed features from each modality before they enter the main model pipeline, producing a single joint input representation that a downstream network learns from jointly. This contrasts with late fusion (option C), where separate unimodal models process each modality independently and their outputs - logits, embeddings, or decisions - are combined only at the end, and with intermediate/hybrid fusion, where combination happens at one or more intermediate feature layers.
Early fusion's advantage is that it allows the model to learn cross-modal correlations from the earliest layers, potentially capturing low-level interactions that later fusion stages would miss. Its disadvantage is sensitivity to modality-specific noise, differing sampling rates, and missing modalities: if one input stream is corrupted or absent, the joint representation degrades more severely than in late fusion, where the surviving modality's model can still function independently.
Option B describes unimodal reduction, not fusion at all, and option D confuses a data-processing strategy with a project-management timeline - an easy distractor to eliminate. Exam questions frequently test the ability to distinguish early, late, and hybrid fusion by identifying *where* in the pipeline combination occurs, so anchor your answer to pipeline stage rather than performance characteristics.
Reference: Multimodal Data domain - fusion strategies (early, late, hybrid/intermediate).
NEW QUESTION # 52
Which of the following techniques are MOST likely to improve the energy efficiency of a large-scale multimodal AI model without significantly sacrificing accuracy?
Answer: A,B,D
Explanation:
Model quantization reduces the memory footprint and computational requirements by using lower precision numbers. Knowledge distillation transfers knowledge from a large model to a smaller one, reducing the computational cost. Pruning removes redundant connections, making the model more efficient. Increasing batch size (Option B) can improve throughput but doesn't inherently reduce energy consumption per sample. Using a larger model (Option D) increases energy consumption.
NEW QUESTION # 53
You are developing a virtual assistant using NVIDIAACE. You want to ensure that the avatar's facial expressions and lip movements are synchronized with the generated speech in real-time. Which NVIDIA SDKs and ACE components are essential for achieving this?
Answer: E
Explanation:
Achieving real-time synchronized facial animation requires a text-to-speech engine (NeMo), a system to generate blendshape weights from the audio (Audi02Face), and a rendering engine to display the animated avatar. Riva provides speech recognition, not necessarily synthesis in this case. While Omniverse is useful for 3D rendering (B,D), it isn't strictly required. CUDA and TensorRT (E) are foundational but don't directly address animation.
NEW QUESTION # 54
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