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Dell EMC Data Science and BigData Certification Questions and Answers



Question : Which of the following is true with regards to the Apriori Algorithms?
A. Algorithm starts with the combination of all the distinct item, to find the frequent itemset and in next iteration, it reduces one item from that frequent Itemset.
B. Algorithm starts with one distinct item, to find the frequent itemset and in next iteration, it add one item to find the frequent itemset.
C. If combination has frequent itemset than its subset will also be frequent dataset.
D. If combination has frequent itemset than it does not guarantee that subset of that combination will also be frequent dataset.

: Which of the following is true with regards to the Apriori Algorithms?
1. A,B
2. B,C
3. C,D
4. A,D
5. B,D

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Explanation: In case of the Apriori algorithm, it will check the first one individual itemset like {bread}, {milk} etc. And once it finds the frequent itemset for individual than it will go for combination
like {bread, milk} and increases in each iteration. However, if does not find any itemset in frequent dataset than in next iteration it will remove that combination altogether.
If any combination is part of frequent dataset than its subset is also part of frequent dataset.





Question : If you have Association Rule as X->Y, which of the below represent the Confidence?


: If you have Association Rule as X->Y, which of the below represent the Confidence?
1. Support for {X}/Support for{X,Y}

2. Support for {X,Y}/Support for{X}

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4. Support for {Y}/Support for{X}


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Explanation: In an Association Rule Confidence can be defined as certainty or trustworthiness associated with discovered rule. Mathematically, confidence is the percent of transactions that contain both X
and Y out of all the transactions that contain X. Hence Confidence {X->Y} is defined as Support for {X,Y}/Support for{X}. Higher the confidence means the relationship is more trustworthiness.




Question : In the Apriori algorithm which statement is true with regards to Confidence for Association Rule {X->Y}?
A. It consider antecedent {X}
B. It consider consequent {Y}
C. It consider co-occurrence of {X,Y}
D. It does not consider consequent {Y}
E. Confidence cannot tell if a rule contains true implication of the relationship of if the rule is purely coincidental.

: In the Apriori algorithm which statement is true with regards to Confidence for Association Rule {X->Y}?
1. A,B,C
2. B,C,D
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4. A,C,D,E
5. A,B,C,D,E

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Explanation: Using confidence we can identify the interesting rules from all the candidate rules, it comes with a problem. Given rules in the form of X -> Y, confidence considers only the antecedent (X)
and the co-occurrence of X and Y; it does not take the consequent of the rule (Y) into concern. Therefore, confidence cannot tell if a rule contains true implication of the relationship or if the rule is purely
coincidental.


Related Questions

Question : Trend, seasonal, and cyclical are components of a time series. What is another component?

 : Trend, seasonal, and cyclical are components of a time series. What is another component?
1. Irregular
2. Linear
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4. Exponential



Question : You are studying the behavior of a population, and you are provided with multidimensional data at
the individual level. You have identified four specific individuals who are valuable to your study,
and would like to find all users who are most similar to each individual. Which algorithm is the
most appropriate for this study?
 : You are studying the behavior of a population, and you are provided with multidimensional data at
1. Association rules
2. Decision trees
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4. K-means clustering




Question : You are using MADlib for Linear Regression analysis. Which value does the statement return?
SELECT (linregr(depvar, indepvar)).r2 FROM zeta1;

 : You are using MADlib for Linear Regression analysis. Which value does the statement return?
1. Coefficients
2. Standard error
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4. P-value


Question : Which data asset is an example of quasi-structured data?


 : Which data asset is an example of quasi-structured data?
1. XML data file
2. Database table
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4. Webserver log


Question : What would be considered "Big Data"?

 : What would be considered
1. An OLAP Cube containing customer demographic information about 100, 000, 000 customers

2. Aggregated statistical data stored in a relational database table

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4. Spreadsheets containing monthly sales data for a Global 100 corporation



Question : When creating a presentation for a technical audience, what is the main objective?

 : When creating a presentation for a technical audience, what is the main objective?
1. Show that you met the project goals
2. Show how you met the project goals
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4. Show the technique to be used in the production environment