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NVIDIA Exam NCA-AIIO Topic 2 Question 2 Discussion

Actual exam question for NVIDIA's NCA-AIIO exam
Question #: 2
Topic #: 2
[All NCA-AIIO Questions]

You are working with a large healthcare dataset containing millions of patient records. Your goal is to identify patterns and extract actionable insights that could improve patient outcomes. The dataset is highly dimensional, with numerous variables, and requires significant processing power to analyze effectively. Which two techniques are most suitable for extracting meaningful insights from this large, complex dataset? (Select two)

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Suggested Answer: D, E

A large, high-dimensional healthcare dataset requires techniques to uncover patterns and reduce complexity. K-means Clustering (Option D) groups similar patient records (e.g., by symptoms or outcomes), identifying actionable patterns using NVIDIA RAPIDS cuML for GPU acceleration. Dimensionality Reduction (Option E), like PCA, reduces variables to key components, simplifying analysis while preserving insights, also accelerated by RAPIDS on NVIDIA GPUs (e.g., DGX systems).

SMOTE (Option A) addresses class imbalance, not general pattern extraction. Data Augmentation (Option B) enhances training data, not insight extraction. Batch Normalization (Option C) is a training technique, not an analysis tool. NVIDIA's data science tools prioritize clustering and dimensionality reduction for such tasks.


Contribute your Thoughts:

Lindsey
1 days ago
I think Dimensionality Reduction (e.g., PCA) would be helpful for simplifying the dataset.
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Linette
3 days ago
D and E seem like the way to go. K-means clustering can help identify patterns, and dimensionality reduction will make the data more manageable to analyze. The dataset is huge, so we need techniques that can handle the complexity.
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