AZURE AI-900 EXAM EXAMPLE
QUESTIONS WITH VERIFIED ANSWERS
You ssneed ssto ssidentify ssnumerical ssvalues ssthat ssrepresent ssthe ssprobability ssof
sshumans ssdeveloping ssdiabetes ssbased sson ssage ssand ssbody ssfat sspercentage.
Which sstype ssof ssmachine sslearning ssmodel ssshould ssyou ssuse? ss- ssAnswer ssMultiple
sslinear ssregression
It ssmodels ssa ssrelationship ssbetween sstwo ssor ssmore ssfeatures ssand ssa sssingle
sslabel, sswhich ssmatches ssthe ssscenario ssin ssthis ssitem. ss
Linear ssregression ssuses ssa sssingle ssfeature. ss
Logistic ssregression ssis ssa sstype ssof ssclassification ssmodel, sswhich ssreturns sseither ssa
ssBoolean ssvalue ssor ssa sscategorical ssdecision. ss
Hierarchical ssclustering ssgroups ssdata sspoints ssthat sshave sssimilar sscharacteristics.
Which sstype ssof ssmachine sslearning ssalgorithm ssgroups ssobservations ssis ssbased sson
ssthe sssimilarities ssof ssfeatures? ss- ssAnswer ssClustering
Clustering ssalgorithms ssgroup ssdata sspoints ssthat sshave sssimilar sscharacteristics.
ssRegression ssalgorithms ssare ssused ssto sspredict ssnumeric ssvalues. ssClassification
ssalgorithms ssare ssused ssto sspredict ssa sspredefined sscategory ssto sswhich ssan ssinput
ssvalue ssbelongs. ssSupervised sslearning ssis ssa sscategory ssof sslearning ssalgorithms
ssthat ssincludes ssregression ssand ssclassification, ssbut ssnot ssclustering.
Which sstype ssof ssmachine sslearning ssalgorithm ssassigns ssitems ssto ssa ssset ssof
sspredefined sscategories? ss- ssAnswer ssClassification ss
Classification ssalgorithms ssare ssused ssto sspredict ssa sspredefined sscategory ssto
sswhich ssan ssinput ssvalue ssbelongs. ssRegression ssalgorithms ssare ssused ssto sspredict
ssnumeric ssvalues. ssClustering ssalgorithms ssgroup ssdata sspoints ssthat sshave sssimilar
sscharacteristics. ssUnsupervised sslearning ssis ssa sscategory ssof sslearning ssalgorithms
ssthat ssincludes ssclustering, ssbut ssnot ssregression ssor ssclassification.
,An sselectricity ssutility sscompany sswants ssto ssdevelop ssa ssmobile ssapp ssfor ssits
sscustomers ssto ssmonitor sstheir ssenergy ssuse ssand ssto ssdisplay sstheir sspredicted
ssenergy ssuse ssfor ssthe ssnext ss12 ssmonths. ssThe sscompany sswants ssto ssuse
ssmachine sslearning ssto ssprovide ssa ssreasonably ssaccurate ssprediction ssof ssfuture
ssenergy ssuse ssby ssusing ssthe sscustomers' ssprevious ssenergy-use ssdata. ss
Which sstype ssof ssmachine sslearning ssis ssthis? ss- ssAnswer ssRegression
Regression ssis ssa ssmachine-learning ssscenario ssthat ssis ssused ssto sspredict ssnumeric
ssvalues. ssIn ssthis ssexample, ssregression sswill ssbe ssable ssto sspredict ssfuture ssenergy
ssconsumption ssbased sson ssanalyzing sshistorical sstime-series ssenergy ssdata ssbased
sson ssfactors, sssuch ssas ssseasonal ssweather ssand ssholiday ssperiods. ssMulticlass
ssclassification ssis ssused ssto sspredict sscategories ssof ssdata. ssClustering ssanalyses
ssunlabeled ssdata ssto ssfind sssimilarities sspresent ssin ssthe ssdata. ssClassification ssis
ssused ssto sspredict sscategories ssof ssdata.
You ssplan ssto ssuse ssmachine sslearning ssto sspredict ssthe ssprobability ssof sshumans
ssdeveloping ssdiabetes ssbased sson sstheir ssage ssand ssbody ssfat sspercentage.
What ssshould ssthe ssmodel ssinclude?
- ssThree ssfeatures
- ssThree sslabels
- ssTwo ssfeatures ssand ssone sslabel
- ssTwo sslabels ssand ssone ssfeature ss- ssAnswer ssTwo ssfeatures ssand ssone sslabel
The ssscenario ssrepresents ssa ssmodel ssthat ssis ssmeant ssto ssestablish ssa ssrelationship
ssbetween sstwo ssfeatures ss(age ssand ssbody ssfat sspercentage) ssand ssone sslabel ss(the
sslikelihood ssof ssdeveloping ssdiabetes). ssThe ssfeatures ssare ssdescriptive ssattributes
ss(serving ssas ssthe ssinput), sswhile ssthe sslabel ssis ssthe sscharacteristic ssyou ssare sstrying
ssto sspredict ss(serving ssas ssthe ssoutput).
In ssa ssregression ssmachine sslearning ssalgorithm, sshow ssare ssfeatures ssand sslabels
sshandled ssin ssa ssvalidation ssdataset?
1. ssFeatures ssare sscompared ssto ssthe ssfeature ssvalues ssin ssa sstraining ssdataset.
2. ssFeatures ssare ssused ssto ssgenerate sspredictions ssfor ssthe sslabel, sswhich ssis
sscompared ssto ssthe ssactual sslabel ssvalues.
This ssanswer ssis sscorrect.
3. ssLabels ssare sscompared ssto ssthe sslabel ssvalues ssin ssa sstraining ssdataset.
4. ssThe sslabel ssis ssused ssto ssgenerate sspredictions ssfor ssfeatures, sswhich ssare
sscompared ssto ssthe ssactual ssfeature ssvalues. ss- ssAnswer ss2. ss
In ssa ssregression ssmachine sslearning ssalgorithm, ssfeatures ssare ssused ssto ssgenerate
sspredictions ssfor ssthe sslabel, sswhich ssis sscompared ssto ssthe ssactual sslabel ssvalue.
, ssThere ssis ssno ssdirect sscomparison ssof ssfeatures ssor sslabels ssbetween ssthe
ssvalidation ssand sstraining ssdatasets.
Which ssassumption ssof ssthe ssmultiple sslinear ssregression ssmodel ssshould ssbe
sssatisfied ssto ssavoid ssmisleading sspredictions?
1. ssFeatures ssare ssdependent sson sseach ssother.
2. ssFeatures ssare ssindependent ssof sseach ssother.
3. ssLabels ssare ssdependent sson sseach ssother.
4. ssLabels ssare ssindependent ssof sseach ssother. ss- ssAnswer ss2. ssFeatures ssare
ssindependent ssof sseach ssother. ss
Multiple sslinear ssregression ssmodels ssthe ssrelationship ssbetween ssseveral ssfeatures
ssand ssa sssingle sslabel. ssThe ssfeatures ssmust ssbe ssindependent ssof sseach ssother,
ssotherwise, ssthe ssmodel's sspredictions sswill ssbe ssmisleading.
Which ssfeature ssmakes ssregression ssan ssexample ssof sssupervised ssmachine
sslearning? ss- ssAnswer ssUse ssof sshistorical ssdata sswith ssknown sslabel ssvalues ssto
sstrain ssa ssmodel.
Regression ssis ssan ssexample ssof sssupervised ssmachine sslearning ssdue ssto ssthe ssuse
ssof sshistorical ssdata sswith ssknown sslabel ssvalues ssto sstrain ssa ssmodel. ssRegression
ssdoes ssnot ssrely sson ssrandomly ssgenerated ssdata ssfor sstraining.
In ssa ssregression ssmachine sslearning ssalgorithm, sswhat ssare ssthe sscharacteristics ssof
ssfeatures ssand sslabels ssin ssa sstraining ssdataset? ss- ssAnswer ssKnown ssfeature ssand
sslabel ssvalues
In ssa ssregression ssmachine sslearning ssalgorithm, ssa sstraining ssset sscontains ssknown
ssfeature ssand sslabel ssvalues.
A sscompany ssis ssusing ssmachine sslearning ssto sspredict sshouse ssprices ssbased sson
ssappropriate sshouse ssattributes.
For ssthe ssmachine sslearning ssmodel, sswhich ssattribute ssis ssthe sslabel?
- ssAge ssof ssthe sshouse
- ssFloor ssspace sssize
- ssNumber ssof ssbedrooms
- ssPrice ssof ssthe sshouse ss- ssAnswer ssThe ssprice ssof ssthe sshouse ssis ssthe sslabel ssyou
ssare ssattempting ssto sspredict ssthrough ssthe ssmachine sslearning ssmodel. ssThis ssis
sstypically ssdone ssby ssusing ssa ssregression ssmodel. ssFloor ssspace sssize, ssnumber ssof
QUESTIONS WITH VERIFIED ANSWERS
You ssneed ssto ssidentify ssnumerical ssvalues ssthat ssrepresent ssthe ssprobability ssof
sshumans ssdeveloping ssdiabetes ssbased sson ssage ssand ssbody ssfat sspercentage.
Which sstype ssof ssmachine sslearning ssmodel ssshould ssyou ssuse? ss- ssAnswer ssMultiple
sslinear ssregression
It ssmodels ssa ssrelationship ssbetween sstwo ssor ssmore ssfeatures ssand ssa sssingle
sslabel, sswhich ssmatches ssthe ssscenario ssin ssthis ssitem. ss
Linear ssregression ssuses ssa sssingle ssfeature. ss
Logistic ssregression ssis ssa sstype ssof ssclassification ssmodel, sswhich ssreturns sseither ssa
ssBoolean ssvalue ssor ssa sscategorical ssdecision. ss
Hierarchical ssclustering ssgroups ssdata sspoints ssthat sshave sssimilar sscharacteristics.
Which sstype ssof ssmachine sslearning ssalgorithm ssgroups ssobservations ssis ssbased sson
ssthe sssimilarities ssof ssfeatures? ss- ssAnswer ssClustering
Clustering ssalgorithms ssgroup ssdata sspoints ssthat sshave sssimilar sscharacteristics.
ssRegression ssalgorithms ssare ssused ssto sspredict ssnumeric ssvalues. ssClassification
ssalgorithms ssare ssused ssto sspredict ssa sspredefined sscategory ssto sswhich ssan ssinput
ssvalue ssbelongs. ssSupervised sslearning ssis ssa sscategory ssof sslearning ssalgorithms
ssthat ssincludes ssregression ssand ssclassification, ssbut ssnot ssclustering.
Which sstype ssof ssmachine sslearning ssalgorithm ssassigns ssitems ssto ssa ssset ssof
sspredefined sscategories? ss- ssAnswer ssClassification ss
Classification ssalgorithms ssare ssused ssto sspredict ssa sspredefined sscategory ssto
sswhich ssan ssinput ssvalue ssbelongs. ssRegression ssalgorithms ssare ssused ssto sspredict
ssnumeric ssvalues. ssClustering ssalgorithms ssgroup ssdata sspoints ssthat sshave sssimilar
sscharacteristics. ssUnsupervised sslearning ssis ssa sscategory ssof sslearning ssalgorithms
ssthat ssincludes ssclustering, ssbut ssnot ssregression ssor ssclassification.
,An sselectricity ssutility sscompany sswants ssto ssdevelop ssa ssmobile ssapp ssfor ssits
sscustomers ssto ssmonitor sstheir ssenergy ssuse ssand ssto ssdisplay sstheir sspredicted
ssenergy ssuse ssfor ssthe ssnext ss12 ssmonths. ssThe sscompany sswants ssto ssuse
ssmachine sslearning ssto ssprovide ssa ssreasonably ssaccurate ssprediction ssof ssfuture
ssenergy ssuse ssby ssusing ssthe sscustomers' ssprevious ssenergy-use ssdata. ss
Which sstype ssof ssmachine sslearning ssis ssthis? ss- ssAnswer ssRegression
Regression ssis ssa ssmachine-learning ssscenario ssthat ssis ssused ssto sspredict ssnumeric
ssvalues. ssIn ssthis ssexample, ssregression sswill ssbe ssable ssto sspredict ssfuture ssenergy
ssconsumption ssbased sson ssanalyzing sshistorical sstime-series ssenergy ssdata ssbased
sson ssfactors, sssuch ssas ssseasonal ssweather ssand ssholiday ssperiods. ssMulticlass
ssclassification ssis ssused ssto sspredict sscategories ssof ssdata. ssClustering ssanalyses
ssunlabeled ssdata ssto ssfind sssimilarities sspresent ssin ssthe ssdata. ssClassification ssis
ssused ssto sspredict sscategories ssof ssdata.
You ssplan ssto ssuse ssmachine sslearning ssto sspredict ssthe ssprobability ssof sshumans
ssdeveloping ssdiabetes ssbased sson sstheir ssage ssand ssbody ssfat sspercentage.
What ssshould ssthe ssmodel ssinclude?
- ssThree ssfeatures
- ssThree sslabels
- ssTwo ssfeatures ssand ssone sslabel
- ssTwo sslabels ssand ssone ssfeature ss- ssAnswer ssTwo ssfeatures ssand ssone sslabel
The ssscenario ssrepresents ssa ssmodel ssthat ssis ssmeant ssto ssestablish ssa ssrelationship
ssbetween sstwo ssfeatures ss(age ssand ssbody ssfat sspercentage) ssand ssone sslabel ss(the
sslikelihood ssof ssdeveloping ssdiabetes). ssThe ssfeatures ssare ssdescriptive ssattributes
ss(serving ssas ssthe ssinput), sswhile ssthe sslabel ssis ssthe sscharacteristic ssyou ssare sstrying
ssto sspredict ss(serving ssas ssthe ssoutput).
In ssa ssregression ssmachine sslearning ssalgorithm, sshow ssare ssfeatures ssand sslabels
sshandled ssin ssa ssvalidation ssdataset?
1. ssFeatures ssare sscompared ssto ssthe ssfeature ssvalues ssin ssa sstraining ssdataset.
2. ssFeatures ssare ssused ssto ssgenerate sspredictions ssfor ssthe sslabel, sswhich ssis
sscompared ssto ssthe ssactual sslabel ssvalues.
This ssanswer ssis sscorrect.
3. ssLabels ssare sscompared ssto ssthe sslabel ssvalues ssin ssa sstraining ssdataset.
4. ssThe sslabel ssis ssused ssto ssgenerate sspredictions ssfor ssfeatures, sswhich ssare
sscompared ssto ssthe ssactual ssfeature ssvalues. ss- ssAnswer ss2. ss
In ssa ssregression ssmachine sslearning ssalgorithm, ssfeatures ssare ssused ssto ssgenerate
sspredictions ssfor ssthe sslabel, sswhich ssis sscompared ssto ssthe ssactual sslabel ssvalue.
, ssThere ssis ssno ssdirect sscomparison ssof ssfeatures ssor sslabels ssbetween ssthe
ssvalidation ssand sstraining ssdatasets.
Which ssassumption ssof ssthe ssmultiple sslinear ssregression ssmodel ssshould ssbe
sssatisfied ssto ssavoid ssmisleading sspredictions?
1. ssFeatures ssare ssdependent sson sseach ssother.
2. ssFeatures ssare ssindependent ssof sseach ssother.
3. ssLabels ssare ssdependent sson sseach ssother.
4. ssLabels ssare ssindependent ssof sseach ssother. ss- ssAnswer ss2. ssFeatures ssare
ssindependent ssof sseach ssother. ss
Multiple sslinear ssregression ssmodels ssthe ssrelationship ssbetween ssseveral ssfeatures
ssand ssa sssingle sslabel. ssThe ssfeatures ssmust ssbe ssindependent ssof sseach ssother,
ssotherwise, ssthe ssmodel's sspredictions sswill ssbe ssmisleading.
Which ssfeature ssmakes ssregression ssan ssexample ssof sssupervised ssmachine
sslearning? ss- ssAnswer ssUse ssof sshistorical ssdata sswith ssknown sslabel ssvalues ssto
sstrain ssa ssmodel.
Regression ssis ssan ssexample ssof sssupervised ssmachine sslearning ssdue ssto ssthe ssuse
ssof sshistorical ssdata sswith ssknown sslabel ssvalues ssto sstrain ssa ssmodel. ssRegression
ssdoes ssnot ssrely sson ssrandomly ssgenerated ssdata ssfor sstraining.
In ssa ssregression ssmachine sslearning ssalgorithm, sswhat ssare ssthe sscharacteristics ssof
ssfeatures ssand sslabels ssin ssa sstraining ssdataset? ss- ssAnswer ssKnown ssfeature ssand
sslabel ssvalues
In ssa ssregression ssmachine sslearning ssalgorithm, ssa sstraining ssset sscontains ssknown
ssfeature ssand sslabel ssvalues.
A sscompany ssis ssusing ssmachine sslearning ssto sspredict sshouse ssprices ssbased sson
ssappropriate sshouse ssattributes.
For ssthe ssmachine sslearning ssmodel, sswhich ssattribute ssis ssthe sslabel?
- ssAge ssof ssthe sshouse
- ssFloor ssspace sssize
- ssNumber ssof ssbedrooms
- ssPrice ssof ssthe sshouse ss- ssAnswer ssThe ssprice ssof ssthe sshouse ssis ssthe sslabel ssyou
ssare ssattempting ssto sspredict ssthrough ssthe ssmachine sslearning ssmodel. ssThis ssis
sstypically ssdone ssby ssusing ssa ssregression ssmodel. ssFloor ssspace sssize, ssnumber ssof
