Data Science and Big Data Analytics - E20-007 Exam Practice Test

Question 1

What does R code nv <- v[v < 1000] do?

A. Removes elements of vector v less than 1000 and assigns the elements >= 1000 to nv

B. Selects values of vector v less than 1000,modifies v,and makes a copy to nv

C. Sets nv to TRUE or FALSE depending on whether all elements of vector v are less than 1000

D. Selects the values in vector v that are less than 1000 and assigns them to the vector nv

Correct Answer: D

Question 2

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,but looks bimodal; replot the data in the range 2,500-10,000 to be sure.

B. The data is extremely skewed. Split your analysis into two cohorts: accounts less than 2500,and accounts greater than 2500

C. The accounts of size greater than 2500 are rare,and probably outliers. Eliminate them from your future analysis.

D. The data is extremely skewed. Replot the data on a logarithmic scale to get a better sense of it.

Correct Answer: D

Question 3

Which word or phrase completes the statement? A data warehouse is to a centralized database for reporting as an analytic sandbox is to a _______?

A. Collection of data assets for ETL

B. Centralized database of KPIs

C. Collection of data assets for modeling

D. Collection of low-volume databases

Correct Answer: C

Question 4

Refer to the Exhibit.

In the Exhibit, the table shows the values for the input Boolean attributes "A", "B", and "C". It also shows the values for the output attribute "class". Which decision tree is valid for the data?

A. Tree D

B. Tree C

C. Tree A

D. Tree B

Correct Answer: D

Question 5

Consider the example of an analysis for fraud detection on credit card usage. You will need to ensure higher-risk transactions that may indicate fraudulent credit card activity are retained in your data for analysis, and not dropped as outliers during pre-processing. What will be your approach for loading data into the analytical sandbox for this analysis?

A. ETL

B. ELT

C. EDW

D. OLTP

Correct Answer: B

Question 6

Refer to the exhibit.

You are using K-means clustering to classify customer behavior for a large retailer. You need to determine the optimum number of customer groups. You plot the within-sum-of-squares (wss) data as shown in the exhibit. How many customer groups should you specify?

A. 2

B. 3

C. 4

D. 8

Correct Answer: C

Question 7

Data visualization is used in the final presentation of an analytics project. For what else is this technique commonly used?

A. ETLT

B. Model selection

C. Assessing data quality

D. Descriptive statistics

Correct Answer: C

Question 8

You have used k-means clustering to classify behavior of 100, 000 customers for a retail store. You decide to use household income, age, gender and yearly purchase amount as measures. You have chosen to use 8 clusters and notice that 2 clusters only have 3 customers assigned. What should you do?

A. Identify additional measures to add to the analysis

B. Decrease the number of measures used

C. Increase the number of clusters

D. Decrease the number of clusters

Correct Answer: D

Question 9

You do a Student's t-test to compare the average test scores of sample groups from populations A and B. Group A averaged 10 points higher than group B. You find that this difference is significant,
with a p-value of 0.03. What does that mean?

A. There is a 3% chance that you have identified a difference between the populations when in reality there is none.

B. There is a 3% chance that a sample group from population A will score 10 points higher that a sample group from population B.

C. The difference in scores between a sample from population A and a sample from population B will tend to be within 3% of 10 points.

D. There is a 97% chance that a sample group from population A will score 10 points higher that a sample group from population B.

Correct Answer: A

Question 10

Refer to the exhibit.

After analyzing a dataset, you report findings to your team:
1.Variables A and C are significantly and positively impacting the dependent variable.
2.Variable B is significantly and negatively impacting the dependent variable.
3.Variable D is not significantly impacting the dependent variable.
After seeing your findings, the majority of your team agreed that variable B should be positively impacting the dependent variable.
What is a possible reason the coefficient for variable B was negative and not positive?

A. Variable B needs a quadratic transformation due to its relationship to the dependent variable

B. Variable B is interacting with another variable due to correlated inputs

C. The information gain from variable B is already provided by another variable

D. Variable B needs a logarithmic transformation due to its relationship to the dependent variable

Correct Answer: B

Question 11

You are performing a market basket analysis using the Apriori algorithm. Which measure is a ratio describing the how many more times two items are present together than would be expected if those two items are statistically independent?

A. Lift

B. Leverage

C. Confidence

D. Support

Correct Answer: A

EMC Data Science and Big Data Analytics - E20-007 Exam Practice Test

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