1. (LO s1-1) sBig sData sis soften sdescribed sby sthe sfour sVs, sor
a. volume, svelocity, sveracity, sand svariability.
b. volume, svelocity, sveracity, sand svariety.
c. volume, svolatility, sveracity, sand svariability.
d. variability, svelocity, sveracity, sand svariety.
Answer: sb
2. LO s1-4) sWhich sdata sapproach sattempts sto sassign seach sunit sin sa spopulation sinto sa ssmall sset
sof sclasses s(or sgroups) swhere sthe sunit sbest sfits?
a. Regression
b. Similarity smatching
c. Co-occurrence sgrouping
d. Classification
Answer: sd
3. (LO s1-4) sWhich sdata sapproach sattempts sto sidentify ssimilar sindividuals sbased son sdata
sknown sabout sthem?
a. Classification
b. Regression
c. Similarity smatching
d. Data sreduction
Answer: sc
4. (LO s1-4) sWhich sdata sapproach sattempts sto spredict sconnections sbetween stwo sdata sitems?
a. Profiling
b. Classification
c. Link sprediction
d. Regression
Answer: sc
5. (LO s1-6) sWhich sof sthese sterms sis sdefined sas sbeing sa scentral srepository sof sdescriptions sfor sall
sof sthe sdata sattributes sof sthe sdataset?
Croesus steacher
, a. Big sData
b. Data swarehouse
c. Data sdictionary
d. Data sAnalytics
a. Developed san sanalytics smindset
b. Data sscrubbing sand sdata spreparation
c. Classification sof stest sapproaches
d. Statistical sdata sanalysis scompetency
Answer: sc
7. (LO s1-5) sIn swhich sareas swere sskills snot semphasized sfor sanalytic-minded saccountants?
a. Data squality
b. Descriptive sdata sanalysis
c. Data svisualization sand sdata sreporting
d. Data sand ssystems sanalysis sand sdesign
a. data spreparation.
b. communicate sinsights.
c. address sand srefine sresults.
d. perform stest splan.
Answer: sa
10. LO s1-1) sBy sthe syear s2024, sthe svolume sof sdata screated, scaptured, scopied, sand
sconsumed sworldwide swill sbe s149 s .
Croesus steacher
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