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| Section | Weight | Objectives |
|---|---|---|
| Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Multimodal Data | 15% | - Applications and use cases - Handling and integrating text, image, and audio data |
| Core ML & AI Knowledge | 20% | - Key algorithms and techniques - Basic concepts and terminology |
| Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
| Performance Optimization | 10% | - Monitoring and improving system efficiency - Techniques for optimizing AI performance |
| Experimentation | 25% | - Hypothesis testing - A/B testing - Model evaluation and comparison - Experimental design |
| Software Development & Engineering | 15% | - Python libraries for multimodal AI - Integration and deployment of multimodal AI systems |
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NEW QUESTION # 40
You are tasked with building a multimodal A1 system that can generate video descriptions from video footage. You have experimented with several architectures, including combining CNNs for visual feature extraction and LSTMs for sequence generation. However, you are facing challenges with the model capturing long-range dependencies in the video. Which of the following architectural modifications or training techniques is MOST likely to address this issue?
Answer: A
Explanation:
Transformers are known for their ability to capture long-range dependencies due to their self-attention mechanism. Replacing LSTMs with Transformers allows the model to attend to relevant parts of the video sequence regardless of their temporal distance. While CNNs can extract visual features, they don't inherently address long-range dependencies. RNNs are prone to vanishing gradients, making it difficult to learn long- range dependencies. Reducing the frame rate or batch size doesn't directly address the issue of capturing long-range dependencies within the video sequence.
NEW QUESTION # 41
What are some methods to overcome limited throughput between CPU and GPU?
Answer: C
Explanation:
CPU-GPU data transfer over the PCIe (or NVLink) bus is frequently a throughput bottleneck in ML pipelines, particularly when small, frequent transfers dominate rather than large batched ones - each transfer incurs fixed overhead independent of data size, so many small transfers waste a disproportionate amount of time on overhead rather than useful data movement. Memory pooling techniques - pre-allocating and reusing pinned (page-locked) host memory buffers rather than repeatedly allocating and freeing memory for each transfer - reduce this overhead and enable faster, more predictable DMA transfers between host and device. Related software-level techniques include using CUDA streams to overlap data transfer with computation (so the GPU keeps computing while the next batch transfers in the background), and batching transfers to amortize fixed per-transfer overhead across more data.
Options A, B, and D each propose hardware upgrades that address a different bottleneck than the one described: increasing CPU clock speed (A) or core count (B) improves CPU-side compute throughput, not the data-transfer bandwidth or latency between CPU and GPU specifically. Upgrading the GPU (D) increases GPU compute capability but does nothing to address a PCIe/interconnect bandwidth limitation - a faster GPU sitting idle waiting for data across the same bottlenecked bus would not see meaningfully improved end- to-end throughput. The question specifically asks about *throughput between* CPU and GPU, which points to interconnect/transfer-management optimization rather than raw compute upgrades on either side.
Reference: Performance Optimization domain - CPU-GPU data transfer optimization, pinned memory, memory pooling, CUDA streams.
NEW QUESTION # 42
You are working with a multimodal model that combines text and image inputs. You want to analyze the model's attention mechanisms to understand which parts of the image are most relevant to specific words in the input text. What technique can you use to visualize and interpret the model's attention weights in this scenario?
Answer: C
Explanation:
Attention heatmaps are a visualization technique used to highlight the regions of an image that the model is focusing on when processing specific words in the input text. By overlaying the attention weights onto the image, you can identify the most relevant areas. t-SNE and PCA are dimensionality reduction techniques used for visualizing high-dimensional data in lower dimensions. ROC curves and confusion matrices are used to evaluate the performance of classification models.
NEW QUESTION # 43
You have a dataset containing information about sales performance for different regions in the last ten years.
Which type of data visualization would be most appropriate to compare the sales performance across regions on a year-by-year basis?
Answer: A
NEW QUESTION # 44
What does 'modality alignment' refer to?
Answer: B
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 # 45
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