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Dell EMC Data Science Associate Certification Questions and Answers (Dumps and Practice Questions)



Question : What is the most common reason for a k-means clustering algorithm to returns a sub-optimal
clustering of its input?

: What is the most common reason for a k-means clustering algorithm to returns a sub-optimal
1. Non-negative values for the distance function
2. Input data set is too large
 3. Access Mostly Uused Products by 50000+ Subscribers
4. Poor selection of the initial controls

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Related Questions

Question : What is the most common reason for a k-means clustering algorithm to returns a sub-optimal
clustering of its input?

 : What is the most common reason for a k-means clustering algorithm to returns a sub-optimal
1. Non-negative values for the distance function
2. Input data set is too large
3. Access Mostly Uused Products by 50000+ Subscribers
4. Poor selection of the initial controls


Question : A data scientist is asked to implement an article recommendation feature for an on-line magazine.
The magazine does not want to use client tracking technologies such as cookies or reading
history. Therefore, only the style and subject matter of the current article is available for making
recommendations. All of the magazine's articles are stored in a database in a format suitable for
analytics.
Which method should the data scientist try first?
 : A data scientist is asked to implement an article recommendation feature for an on-line magazine.
1. K Means Clustering
2. Naive Bayesian
3. Logistic Regression
4. Association Rules




Question : How are window functions different from regular aggregate functions?
 : How are window functions different from regular aggregate functions?
1. Rows retain their separate identities and the window function can access more than the current row.
2. Rows are grouped into an output row and the window function can access more than the current row.
3. Rows retain their separate identities and the window function can only access the current row.
4. Rows are grouped into an output row and the window function can only access the current row.



Question : Consider these item sets:
(hat, scarf, coat)
(hat, scarf, coat, gloves)
(hat, scarf, gloves)
(hat, gloves)
(scarf, coat, gloves)
What is the confidence of the rule (hat, scarf) -> gloves?

 : Consider these item sets:
1. 66%
2. 40%
3. 50%
4. 60%