TEST BANK FOR
b b
Data Analytics for Accounting, 3rd Edition Richardson
b b b b b b
Chapter 1-9
b b
Answers are at the End of Each Chapter
b b b b b b b
Chapter 01: b
Student name:
b
1) Data banalytics bis bthe bprocess bof bevaluating bdata bwith bthe bpurpose bof bdrawing bconclusions
to baddress bbusiness bquestions.
b
⊚ b true
⊚ b false
2) The bprocess bof bdata banalytics baims bto btransform braw binformation binto bdata bto bcreate bvalue.
⊚ b true
⊚ b false
3) Data banalytics bhas bthe bpotential bto btransform bthe bmanner bin bwhich bcompanies brun
their bbusinesses, bhowever bit bis bnot bpractical bin bthe bnear bfuture.
b
⊚ b true
⊚ b false
4) Auditors bcan buse bsocial bmedia bto bhear bwhat bcustomers bare bsaying babout ba bcompany
and bcompare bthis bto binventory bobsolescence band bother bestimates.
b
⊚ b true
⊚ b false
5) Data banalytics ballows bauditors bto bglean binsights bthat bare bbeneficial bto bthe bclient,
without bbreeching bindependence.
b
⊚ b true
⊚ b false
,6) The bpredictive banalytics bis ban bimportant baspect bof bdata banalytics bfor bauditors, bbut bis
not bapplicable bfor btax baccountants.
b
⊚ b true
⊚ b false
7) The bI bin bIMPACT bCycle brepresents bIdentify bthe bQuestion.
⊚ b true
⊚ b false
8) The bM bin bIMPACT bCycle brepresents bMaster bthe bData.
⊚ b true
⊚ b false
9) The bP bin bIMPACT bCycle brepresents bPredict bthe bResults.
⊚ b true
⊚ b false
10) The bA bin bIMPACT bCycle brepresents bAnalyze bthe bData.
⊚ b true
⊚ b false
11) The bC bin bIMPACT bCycle brepresents bContinuously bTrack.
⊚ b true
⊚ b false
12) The bT bin bIMPACT bCycle brepresents bTrack bOutcomes.
⊚ b true
⊚ b false
,13) The bIMPACT bcycle bis biterative, bas binsights bare bgained, boutcomes bare btracked, band
new bquestions bare bidentified.
b
⊚ b true
⊚ b false
14) Data banalysis bthrough bdata bmanipulation bis bperforming bbasic banalysis bto bunderstand
the bquality bof bthe bunderlying bdata band bits bability bto baddress bthe bbusiness bquestion.
b
⊚ b true
⊚ b false
15) To bbe bproficient bin bdata banalysis, baccountants bneed bto bbecome bdata bscientists.
⊚ b true
⊚ b false
16) By bdeveloping ban banalytics bmindset, baccountants bwill bbe bable bto brecognize bwhen band
how bdata banalytics bcan baddress bbusiness bquestions.
b
⊚ b true
⊚ b false
17) While bit bis bimportant bfor baccountants bto bclearly barticulate bthe bbusiness bproblem,
drawing bappropriate bconclusions, bbased bon bthe bdata, bshould bbe bleft bto bstatisticians.
b
⊚ b true
⊚ b false
18) Analytic-minded baccountants bshould breport bresults bof banalysis bin ban baccessible bway bto
each bvaried bdecision bmaker band btheir bspecific bneeds.
b
⊚ b true
⊚ b false
, 19) With ba bgoal bto bgive borganizations bthe binformation bthey bneed bto bmake bsound band
timely bbusiness bdecisions, bdata banalytics boften binvolves ball bof bthe bfollowing bexcept:
b
A) technologies.
B) statistics.
C) strategies.
D) databases.
20) Patterns bdiscovered bfrom b enable bbusinesses bto bidentify bopportunities band
risks band bbetter bplan bfor b
b .
A) past barchives; bthe bfuture
B) current bdata; bthe bfuture
C) current bdata; btoday
D) past barchives; btoday
21) Which bof bthe bfollowing bbest bdescribes bthe bgoal bof bdescriptive bdata banalysis:
A) recognize bwhat bis bmeant bby bdata bquality, bbe bit bcompleteness, breliability bor bvalidity
B) perform bbasic banalysis bto bunderstand bthe bquality bof bthe bunderlying bdata band bits
ability bto baddress bthe bbusiness bquestion
b
C) demonstrate bability bto bsort, brearrange, bmerge, band breconfigure bdata bin ba bmanner
bthat ballows benhanced banalysis
D) comprehend bthe bprocess bneeded bto bclean band bprepare bthe bdata bbefore banalysis
22) Which bof bthe bfollowing bMicrosoft bsoftware btool bspecializes bin bdata bjoining?
A) Excel
B) Power bQuery
C) Power bBI
D) Power bAutomate
23) Which bof bthe bfollowing bMicrosoft bsoftware btools bspecializes bin bcreating bdashboards?
A) Excel
B) Power bQuery
C) Power bBI
D) Power bAutomate
b b
Data Analytics for Accounting, 3rd Edition Richardson
b b b b b b
Chapter 1-9
b b
Answers are at the End of Each Chapter
b b b b b b b
Chapter 01: b
Student name:
b
1) Data banalytics bis bthe bprocess bof bevaluating bdata bwith bthe bpurpose bof bdrawing bconclusions
to baddress bbusiness bquestions.
b
⊚ b true
⊚ b false
2) The bprocess bof bdata banalytics baims bto btransform braw binformation binto bdata bto bcreate bvalue.
⊚ b true
⊚ b false
3) Data banalytics bhas bthe bpotential bto btransform bthe bmanner bin bwhich bcompanies brun
their bbusinesses, bhowever bit bis bnot bpractical bin bthe bnear bfuture.
b
⊚ b true
⊚ b false
4) Auditors bcan buse bsocial bmedia bto bhear bwhat bcustomers bare bsaying babout ba bcompany
and bcompare bthis bto binventory bobsolescence band bother bestimates.
b
⊚ b true
⊚ b false
5) Data banalytics ballows bauditors bto bglean binsights bthat bare bbeneficial bto bthe bclient,
without bbreeching bindependence.
b
⊚ b true
⊚ b false
,6) The bpredictive banalytics bis ban bimportant baspect bof bdata banalytics bfor bauditors, bbut bis
not bapplicable bfor btax baccountants.
b
⊚ b true
⊚ b false
7) The bI bin bIMPACT bCycle brepresents bIdentify bthe bQuestion.
⊚ b true
⊚ b false
8) The bM bin bIMPACT bCycle brepresents bMaster bthe bData.
⊚ b true
⊚ b false
9) The bP bin bIMPACT bCycle brepresents bPredict bthe bResults.
⊚ b true
⊚ b false
10) The bA bin bIMPACT bCycle brepresents bAnalyze bthe bData.
⊚ b true
⊚ b false
11) The bC bin bIMPACT bCycle brepresents bContinuously bTrack.
⊚ b true
⊚ b false
12) The bT bin bIMPACT bCycle brepresents bTrack bOutcomes.
⊚ b true
⊚ b false
,13) The bIMPACT bcycle bis biterative, bas binsights bare bgained, boutcomes bare btracked, band
new bquestions bare bidentified.
b
⊚ b true
⊚ b false
14) Data banalysis bthrough bdata bmanipulation bis bperforming bbasic banalysis bto bunderstand
the bquality bof bthe bunderlying bdata band bits bability bto baddress bthe bbusiness bquestion.
b
⊚ b true
⊚ b false
15) To bbe bproficient bin bdata banalysis, baccountants bneed bto bbecome bdata bscientists.
⊚ b true
⊚ b false
16) By bdeveloping ban banalytics bmindset, baccountants bwill bbe bable bto brecognize bwhen band
how bdata banalytics bcan baddress bbusiness bquestions.
b
⊚ b true
⊚ b false
17) While bit bis bimportant bfor baccountants bto bclearly barticulate bthe bbusiness bproblem,
drawing bappropriate bconclusions, bbased bon bthe bdata, bshould bbe bleft bto bstatisticians.
b
⊚ b true
⊚ b false
18) Analytic-minded baccountants bshould breport bresults bof banalysis bin ban baccessible bway bto
each bvaried bdecision bmaker band btheir bspecific bneeds.
b
⊚ b true
⊚ b false
, 19) With ba bgoal bto bgive borganizations bthe binformation bthey bneed bto bmake bsound band
timely bbusiness bdecisions, bdata banalytics boften binvolves ball bof bthe bfollowing bexcept:
b
A) technologies.
B) statistics.
C) strategies.
D) databases.
20) Patterns bdiscovered bfrom b enable bbusinesses bto bidentify bopportunities band
risks band bbetter bplan bfor b
b .
A) past barchives; bthe bfuture
B) current bdata; bthe bfuture
C) current bdata; btoday
D) past barchives; btoday
21) Which bof bthe bfollowing bbest bdescribes bthe bgoal bof bdescriptive bdata banalysis:
A) recognize bwhat bis bmeant bby bdata bquality, bbe bit bcompleteness, breliability bor bvalidity
B) perform bbasic banalysis bto bunderstand bthe bquality bof bthe bunderlying bdata band bits
ability bto baddress bthe bbusiness bquestion
b
C) demonstrate bability bto bsort, brearrange, bmerge, band breconfigure bdata bin ba bmanner
bthat ballows benhanced banalysis
D) comprehend bthe bprocess bneeded bto bclean band bprepare bthe bdata bbefore banalysis
22) Which bof bthe bfollowing bMicrosoft bsoftware btool bspecializes bin bdata bjoining?
A) Excel
B) Power bQuery
C) Power bBI
D) Power bAutomate
23) Which bof bthe bfollowing bMicrosoft bsoftware btools bspecializes bin bcreating bdashboards?
A) Excel
B) Power bQuery
C) Power bBI
D) Power bAutomate
