Try to answer these 100+ Data Analytics MCQs and check your understanding of the Data Analytics subject.
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A.
A
B.
B
C.
c
D.
D
A. Explanative analysis
B. Spectral analysis
C. Forecasting
D. Descriptive analysis
A. PredictCaseLikelihood(
B. PredictCaseLikelihood([NORMALIZEDINONNORMALIZEDD
C. PredictCaseLikelihood(
D. PredictCaseLikelihood(
A. PredictVariance(
B. PredictVariance(
C. PredictVariance(
D. PredictVariance(
A. Catalog design
B. Basket data analysis
C. Cross-marketing
D. Loss-leader analysis
E. All of the above
F. None of the above
A. It can automatically process messages and emails.
B. It can investigate competitors by crawling their web sites.
C. It can analyze open-ended survey responses.
D. It can analyze warranty or insurance claims.
E. All of the above.
A.
A
B.
B
C.
c
D.
0
A.
A
B.
B
C.
C
D.
D
For what purpose is the following R function run?
print(getwd)
A.
To get and print the current working directory.
B.
To get and print all working directories.
C.
To count and print all working directories.
D.
To print the location of a working directory.
A. Input neuron
B. Hidden neuron
C. Output neuron
D. None of the above
A. It is used for calculating the conditional probability between input and predictable columns and it assumes that the columns are independent.
B. It is used for performing automatic feature selection to limit the number of values that are considered when building a model.
C. It is provided by Microsoft SQL Server analysis services for use in predictive modeling.
D. It is used for considering each pair of input attribute values and output attribute values.
E. All of the above.
A. It is used for encouraging group effect in case of highly correlated variables.
B. It is used for finding the probability of event=Success and event=Failure.
C. It is used for adding and removing predictors as needed for each step.
D. It is used for penalizing the absolute size of the regression coefficients.
A. It is used for predicting one or more continuous numeric variables; for example. profit or loss that is based on other attributes in a dataset.
B. It is used for finding correlations between different attributes in a dataset.
C. It is used for dividing data into groups or clusters of items that have similar properties.
D. It is used for summarizing frequent sequences or episodes in data; for example. a series of log events preceding machine maintenance.
A. ltemsets
B. Dependency Network
C. Rules
D. None of the above
A. It is used for finding whether an event can lead to a change in a time series.
B. It is used for finding a trend or pattern in a time series through the use of graphs or other tools.
C. It is used extensively in budgeting. which is based on historical trends.
D. It is used for studying the cross correlation between two time series and their dependence on another.
A.
PredictAssociation(<NodelD>)
B.
PredictAssociation(<cluster column reference>, [<predicted state>))
C.
PredictAssociation(<scalar column reference>)
D.
PredictAssociation(<tabIe column reference>, optionl, option2, n ...)
A. 10
B. 3
C. 1
D. 0.4
A. glm(formula, family=familytype(link=linkfunction), data=)
B. glm(formula, data=, method=,control=)
C. glm(vector, start=. end=, frequency=)
D. glm(bootobject. conf=, type=)
Find the output of the following R programming language code.
z1 <- c(7,5,8,4,4,16)
z2 <- c(9,6)
add.result <- 21+22
print(add.result)
sub.result <- 21-22
print(sub.result)
A.
[1]161184416
[1}2-184416
B.
[1]1511171313 25
[1] -2 -1 2 -2 -210
C.
[1]1611171013 22
[1] -2 -1 -1 -2 -5 10
D.
[1]151074814
[1]-1-216 -4 -3
Find the output of the following code of the R programming language.
z1 <- c(4,3,TRUE,2+6i)
z2 <- c(4,7,TRUE.2+7i)
print(z1&22)
A.
[1] TRUE TRUE TRUE TRUE
B.
[1] TRUE FALSE TRUE FALSE
C.
[1] FALSE TRUE FALSE TRUE
D.
[1] FALSE FALSE FALSE FALSE
What will be the output of the following R code?
c(4,7,TRUE,3+7i) -> v1
c(9,6,FALSE,3+7i) ->> v2
print(v1)
print(v2)
A.
[114+01 4+1i 7+01 3+7i
[1] 9+0i 9+1i 6+0i 3+7i
B.
[1]4+0i7+0i1+0i3+7i
[1] 9101 6+0i 0+01 3+7i
C.
[1) 4+0i 7+7l1+1i 3+7i
[1] 9+Oi 9+1i 6+6i 3+7i
D.
[1]4+4i7+7i1+1i3+7i
[119+9i 6+6i1+1i 3+7i
A. grepl.any(installed.packages("xlsx")) library("xlsx")
B. any(grepl("xlsx“,installed.package())) library("xlsx")
C. any.grepl(xlsx,installed.package50) |ibrary(xlsx)
D. grepl(any(installed.packages(xlsx))) |ibrary(xlsx)
A. Cluster(
B. Cluster([
C. Cluster()
D. Cluster([
A.
A
B.
B
C.
C
D.
0
A. Clustering
B. Categorization
C. Visualization
D. Information extraction
What will be the output of the following code of the R programming language?
a <- c(9,0.FALSE,2+9i)
b <- c(8,0,TRUE,2+7i)
print(alb)
A.
[1] FALSE TRUE FALSE FALSE
B.
[1] TRUE TRUE TRUE FALSE
C.
[1] TRUE FALSE TRUE TRUE
D.
[1] FALSE FALSE FALSE TRUE
Find the output of the following R programming language code.
a <- c(7.5.FALSE.4+4i)
b <- c(6,0,TRUE,4+7i)
print(a&&b)
A.
[1] FALSE
B.
[1] FALSE TRUE
C.
(1] FALSE FALSE
D.
[1] TRUE
A. Segmentation algorithm
B. Classification algorithm
C. Sequence analysis algorithm
D. Association algorithm
What will the following R code do?
mydata$v2 <- mydata$v4 <- NULL
A.
It will replace the value of variable v2 with v4 and will delete the variable v4.
B.
It will replace the value of variable v4 with v2 and will delete the variable v2.
C.
It will delete the variables v2 and v4.
D.
None of the above.
A. match associations [as pattern_name] analyze {measure(s) }
B. mine associations [as pattern_name] analyze classifying_attribute_or_dimension
C. mine associations [as [pattern_name]] {matching {metapattern}}
D. mine associations [as pattern_name] analyze prediction_attribute_or_dimension {set [attribute_or_dimension_i= value_i}]
Choose True or False.
Text mining is used in spam filtering. content enrichment and contextual advertising.
A.
True
B.
False
A user wants to read and print the contents of a CSV file named myexample-csv that is present in his current working directory. Which of the following is the correct syntax of the command that should be executed by him to accomplish this task?
A.
data <- read(myexample.csv)
print(data)
B.
data <- read.file(”myexample.csv")
print(data)
C.
data <- read.csv("myexample.csv")
print(data)
D.
data <~ read.data(myexample.csv)
print(data)
A. Stepwise regression
B. Polynomial regression
C. Linear regression
D. Logistic regression
A. PredictSupport(
B. PredictSupport(
C. PredictSupport(
D. PredictSupport(
A. It supports the cyclical, key and table content types.
B. It supports the key, table and ordered content types.
C. It supports the continuous, key and table content types.
D. It supports the continuous, cyclical and ordered content types.
Using the following information, find the correct syntax of the R function used for creating binary files.
Assume object as the binary file to be written. n as the number Of bytes and con as the connection object.
A.
writeBin(object, n, con)
B.
writeBin(object)
C.
writeBin(object, n)
D.
writeBin(object, con)
A. It specifies how a model should be mixed for optimizing forecasting.
B. It specifies which algorithm to use for analysis and prediction.
C. It specifies a numeric value between 0 and 1 that detects periodicity.
D. It specifies the minimum number of time slices that are required to generate a split in each time series tree.
Find the output of the following code of the R programming language.
Iista <- Iist(5:7)
print(lista)
Iistb <-Iist(12:14)
print(listb)
x1 <- unlist(lista)
x2 <- unlist(listb)
print(xl)
print(x2)
r <- x1+x2
print(r)
A.
[[1]]
[1] 5 6 7
[[1]]
[1] 12 13 14
[1] 5 6 7
[1] 12 13 14
[1] 17 19 21
B.
[[1]]
[1] 5 6 7
[[1]]
[1] 12 13 14
[1] 5 6 7
[1] 12 13 14
[1] 15 16 17
C.
[[1]]
[1] 5 6 7
[[1]]
[1] 12 13 14
[1] 5 6 7
[1] 12 13 14
[1] 24 25 26
D.
[[1]]
[1] 5 6 7
[[1]]
[1] 12 13 14
[1] 5 6 7
[1] 12 13 14
[1] 11 12 13
A. 0.6
B. 0.1
C. 10
D. 1
A. total <~ merge(data myFrame1 with myFrame2, by=c(lD,Country))
B. total <- merge(data myFrame1,data myFrame2,by=c("lD","Country"))
C. total <- merge(data by=c("lD","Country") for myFrame1, myFrame2)
D. total <- merge(data for myFrame1, myFrame2,by=C(lD,Country))
A.
A
B.
B
C.
c
D.
D
Which of the following options represent correct application of the time series analysis?
i) Yield Projections
ii) Workload Projections
iii) Census Analysis
iv) Inventory Studies
A.
Only options i) and ii)
B.
Only options ii) and iv)
C.
Only Options i). ii) and iv)
D.
Only options ii). iii) and iv)
E.
All options i), ii). iii) and iv)
A. PredictAdjustedProbability(
B. PredictAdjustedProbability(
C. PredictAdjustedProbability(
D. PredictAdjustedProbabilityo
A. It can be used to produce an unrotated principal component analysis.
B. It can be used to produce maximum likelihood factor analysis.
C. It can be used to bootstrap the structural equation model.
D. It can be used to fit an autoregressive integrated moving average model.
A. F-score = recall - precision + (recall x precision) / 9
B. F-score = recall + precision - (recall x precision) I 7
C. F-score = recall x precision / (recall + precision) / 2
D. F-score = recall I precision x (recall - precision) / 5
A. 10
B. 1
C. 0
D. 5
A. Regression analysis
B. ANOVA
C. Factor analysis
D. Logistic regression
A. precision: l[Relevant] n [Retrieved]l / l[Retrieved]l
B. Precision= l[Retrieved} U [F-score]l + l[F-score}l
C. Precision= l[Recall] / [F-scorejl x l[RecalI]l
D. Precision= l[F-score] x [Recalljl - l[F—score)l
Which of the given options will be the output of the following code when it is executed in R?
var <— c(8.4.NA.12)
mean(var, na.rm=TRUE)
A.
[1) 2
B.
[114
C.
[1) 8
D.
[1110
E.
The code will throw an error.
A. Precision
B. Recall
C. F-score
D. None of the above