This Exam P sample reference tests Conditional Probability. The insured-and-failing joint probability is 0.80(0.03)=0.024. Dividing this by the overall failure probability 0.05 gives 0.48, so choice D is correct.
These notes identify the calculation error associated with each wrong letter when that error is reproducible.
AThis is the joint mass 0.80(0.03)=0.024 rounded to 0.02; it does not condition on the fact that failure occurred.
BThis comes from dividing the conditional failure rate by the insured prior, 0.03/0.80=0.0375, which reverses the multiplication needed to form the joint event.
CThis doubles the 0.03 conditional failure rate. Bayes' theorem requires weighting by the 0.80 prior and dividing by 0.05, not an arbitrary factor of two.
EThis is 0.03/0.05=0.60. It omits the insured prior 0.80 from the Bayes numerator.
Original practice · fully worked
Original variant: route identification after an alert
A data center routes 40% of packets through channel A, 35% through channel B, and 25% through channel C. The corresponding checksum-alert probabilities are 0.02, 0.05, and 0.01. Given that a packet generated a checksum alert, calculate the probability that it used channel B.
A 0.0175
B 0.0500
C 0.3500
D 0.6863
E 0.6250
Variant answer in brief
The total alert probability is 0.40(0.02)+0.35(0.05)+0.25(0.01)=0.028. Channel B contributes 0.0175, so its posterior probability is 0.625 and choice E is correct.
Setup
Setup
Weight each channel's alert rate by its traffic share.
wA=0.40(0.02)=0.008
wB=0.35(0.05)=0.0175
wC=0.25(0.01)=0.0025
Model
Model
The three routes partition packet traffic, so their alert masses add to the denominator.
Pr(L)=wA+wB+wC=0.028
Compute
Compute
Divide channel B's joint alert mass by the total alert mass.
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