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Exam AWS Certified Machine Learning - Specialty All Questions

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Exam AWS Certified Machine Learning - Specialty topic 1 question 309 discussion

An online retailer collects the following data on customer orders: demographics, behaviors, location, shipment progress, and delivery time. A data scientist joins all the collected datasets. The result is a single dataset that includes 980 variables.

The data scientist must develop a machine learning (ML) model to identify groups of customers who are likely to respond to a marketing campaign.

Which combination of algorithms should the data scientist use to meet this requirement? (Choose two.)

  • A. Latent Dirichlet Allocation (LDA)
  • B. K-means
  • C. Semantic segmentation
  • D. Principal component analysis (PCA)
  • E. Factorization machines (FM)
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Suggested Answer: BD 🗳️

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KariDam0909
8 months, 4 weeks ago
Selected Answer: BD
K means and PCA
upvoted 1 times
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vkbajoria
1 year, 1 month ago
Selected Answer: BD
K means and PCA
upvoted 1 times
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AIWave
1 year, 1 month ago
Selected Answer: BD
Classic clustering problem A: No - LDA is for topic modelling B: Yes - K-means is a clustering algorithm C: No - applies to images D: Yes - PCA makes sure only relevant features are selected E: No - FM is supervised regression/classification recommendation algorithm for sparse data
upvoted 1 times
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Alice1234
1 year, 2 months ago
B. K-means: This algorithm is effective for clustering customers into distinct groups based on similarities across their features, which can reveal segments more likely to respond to marketing campaigns. D. Principal Component Analysis (PCA): Given the high dimensionality of the dataset, PCA can reduce the number of variables to a manageable size while retaining most of the variance, making the dataset more tractable for clustering algorithms like K-means.
upvoted 3 times
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delfoxete
1 year, 2 months ago
Selected Answer: BD
k-means for clustering due to no comments regarding labels in the data and also PCA in order to reduce the amount of features
upvoted 2 times
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