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Quiz 2025 EMC Pass-Sure D-DS-FN-23: Visual Dell Data Science Foundations Cert Exam

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EMC Dell Data Science Foundations Sample Questions (Q135-Q140):

NEW QUESTION # 135
Which of the following is an example of quasi-structured data?

A. OLAP
B. Clickstream data
C. Customer record table
D. OLTP

Answer: B

NEW QUESTION # 136
When building a K-means clustering model, you notice that the clusters did not segment on variables that you expected. What should you do?

A. Decrease the value of K
B. Check that the data was properly scaled
C. Multiply each variable by its standard deviation
D. Add the WSS to each variable

Answer: B

Explanation:
When building a K-means clustering model, it's important to ensure that the data is properly scaled, as K- means is sensitive to the scale of the variables. If the variables have different scales, the clustering may not segment as expected. Standardizing or normalizing the data can often improve the results.

NEW QUESTION # 137
Refer to the exhibit.

You have plotted the distribution of savings account sizes for your bank.
How would you proceed, based on this distribution?

A. The data is extremely skewed. Replot the data on a logarithmic scale to get a better sense of it.
B. The accounts of size greater than 2500 are rare, and probably outliers. Eliminate them from your future analysis.
C. The data is extremely skewed, but looks bimodal; replot the data in the range 2, 500-10, 000 to be sure.
D. The data is extremely skewed. Splityour analysis into two cohorts: accountsless than 2500, and accounts greater than 2500

Answer: A

NEW QUESTION # 138
In addition to the business question and descriptions of available data sets, what else would an analytic plan include?

A. Existing solutions to the business question
B. Relevant statistical tests
C. Initial hypotheses
D. Access credentials to database and/or Hadoop cluster

Answer: C

NEW QUESTION # 139
You build a decision tree to classify five different types of customers based on their browsing history from a sample of 500. The resulting decision tree has 17 layers. One of the leaf nodes has only three customers.
What do you conclude?

A. The decision tree needs to be rebuilt to see if the results change
B. The sample size is too small, so the classes may not be accurate
C. Due to large number of layers, there may be an overfitting problem
D. The decision tree needs to be rebuilt without the three customers

Answer: C

Explanation:
The decision tree having 17 layers and a leaf node with only three customers suggests that the tree is overfitting the data. This is because a deep tree with many layers can lead to overfitting, where the model fits the training data too closely, capturing noise and small details that do not generalize well to new data.
Reducing the tree depth or pruning the tree can help mitigate overfitting.

NEW QUESTION # 140
......

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