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Exam AWS Certified Machine Learning - Specialty topic 1 question 11 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 11
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The
Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences, and trends to enhance the website for better service and smart recommendations.
Which solution should the Specialist recommend?

  • A. Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.
  • B. A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database.
  • C. Collaborative filtering based on user interactions and correlations to identify patterns in the customer database.
  • D. Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database.
Show Suggested Answer Hide Answer
Suggested Answer: C 🗳️
by DonaldCMLIN at Nov. 16, 2019, 3:13 a.m.
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WWODIN
Highly Voted 3 years, 6 months ago
answer should be C Collaborative filtering is for recommendation, LDA is for topic modeling
upvoted 21 times
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syu31svc
Highly Voted 3 years, 6 months ago
In natural language processing, the latent Dirichlet allocation (LDA) is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. Amazon SageMaker Random Cut Forest (RCF) is an unsupervised algorithm for detecting anomalous data points within a data set Neural network is used for image detection Answer is C
upvoted 11 times
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Vernoxx
Most Recent 2 days, 12 hours ago
Selected Answer: C
I think it should be c
upvoted 1 times
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JonSno
2 months, 1 week ago
Selected Answer: C
Collab filtering it is.. Collaborative filtering is the most widely used approach for recommendation systems. It uses customer interactions (purchases, clicks, ratings) to determine preferences based on similar users or items. Implicit collaborative filtering (based on user behavior) and explicit collaborative filtering (based on ratings) can effectively personalize recommendations.
upvoted 1 times
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loict
7 months ago
Selected Answer: C
A. NO - LDA is for topic modeling B. NO - NN is a too generic term, you want Neural Collaborative C. YES - Collaborative filtering best fit D. NO - Random Cut Forest (RCF) for anomalities
upvoted 2 times
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Mickey321
7 months ago
Selected Answer: C
Collaborative filtering is a machine learning technique that recommends products or services to users based on the ratings or preferences of other users. This technique is well-suited for identifying customer shopping patterns and preferences because it takes into account the interactions between users and products.
upvoted 1 times
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killermouse0
1 year, 1 month ago
Selected Answer: A
From the doc: "You can use LDA for a variety of tasks, from clustering customers based on product purchases to automatic harmonic analysis in music." https://docs.aws.amazon.com/sagemaker/latest/dg/lda-how-it-works.html
upvoted 1 times
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Venkatesh_Babu
1 year, 9 months ago
Selected Answer: C
I think it should be c
upvoted 1 times
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Valcilio
2 years, 1 month ago
Selected Answer: C
C, always when talk about recommendation you can think about collaborative patterns!
upvoted 2 times
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stjokerli
2 years, 1 month ago
A LDA used before collaborative filtering is largely adopted. 1) the input data that we have doesn't lend itself to collaborative filtering - it requires a set of items and a set of users who have reacted to some of the items, which is NOT what we have 2) recommendation is just one thing that we want to do. What about trends? 3) collaborative filtering isn't one of the pre-built algorithms (weak argument, admittedly)
upvoted 2 times
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Shailendraa
2 years, 7 months ago
collaborative
upvoted 1 times
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apprehensive_scar
3 years, 2 months ago
C. Easy question.
upvoted 1 times
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technoguy
3 years, 6 months ago
its a appropriate use case of Collaborative filtering
upvoted 1 times
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roytruong
3 years, 6 months ago
this is C
upvoted 1 times
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sdsfsdsf
3 years, 6 months ago
I'm thinking that it is A because: 1) the input data that we have doesn't lend itself to collaborative filtering - it requires a set of items and a set of users who have reacted to some of the items, which is NOT what we have 2) recommendation is just one thing that we want to do. What about trends? 3) collaborative filtering isn't one of the pre-built algorithms (weak argument, admittedly)
upvoted 6 times
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cybe001
3 years, 6 months ago
Answer is C, demographics, past visits, and locality information data, LDA is appropriate
upvoted 3 times
cybe001
3 years, 6 months ago
Collaborative filtering is appropriate
upvoted 4 times
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DonaldCMLIN
3 years, 7 months ago
Answer A might be more suitable than other https://docs.aws.amazon.com/zh_tw/sagemaker/latest/dg/lda-how-it-works.html
upvoted 4 times
rsimham
3 years, 6 months ago
Not convinced with A. Answer C seems to be a better fit than A for recommendation model (LDA appears to be a topic-based model on unavailable data with similar patterns) https://aws.amazon.com/blogs/machine-learning/extending-amazon-sagemaker-factorization-machines-algorithm-to-predict-top-x-recommendations/
upvoted 10 times
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A (35%)
C (25%)
B (20%)
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