Exam AWS Certified Machine Learning - Specialty topic 1 question 68 discussion

C: (OK) Use PCA for reducing number of variables. Each citizen's response should have answer for 500 questions, so it should have 500 variables D: (OK) Use K-means clustering A: (Not OK) Factorization Machines Algorithm is usually used for tasks dealing with high dimensional sparse datasets B: (Not OK) The Latent Dirichlet Allocation (LDA) algorithm should be used for task dealing topic modeling in NLP E: (Not OK) Random Cut Forest should be used for detecting anormal in data

https://aws.amazon.com/blogs/machine-learning/analyze-us-census-data-for-population-segmentation-using-amazon-sagemaker/ Answer: C and D

If the form contains free-text answers, it would be interesting to apply LDA to identify the most frequent/relevant topics in the answers

Option C and D

C and D are the way

CD, C - for reduce number of columns. D - for data clustering

C. The principal component analysis (PCA) algorithm D. The k-means algorithm PCA is a dimensionality reduction technique that can be used to identify the underlying structure of the census data. This algorithm can help to identify the most important questions and provide an overview of the relationship between the questions and the responses. K-means is an unsupervised learning algorithm that can be used to segment the population into different groups based on their responses to the census questions. This algorithm can help to determine the healthcare and social program needs by province and city based on the responses collected from each citizen. These algorithms can help to provide insights into the patterns and relationships within the census data, which can inform decision making for healthcare and social program planning.

Reduce dimensionality and cluster subjects.

This is the same question as Topic 2 Q3