As others have pointed out ... this is due to error introduced by premature rounding of the L,M, and S values. It is common to report z-scores for nutrition indicators to 2 decimal places but the rounding is always done at the final stage. Also, error is magnified by the use of a power term.
Computer scientists refer to the number of decimal places as "precision" because of the errors that come with rounding. As a rule you should (when dealing with quantities that are not constrained to be whole numbers) is to not round measurements and the results in and from intermediate calculations. Round only at the last posible moment.
While on the topic of "precision" ... most software will yield errors due to internal precision with small and large numbers. Almost all software will, for example, tell you that:
=1*(0.5-0.4-0.1)
is not zero (worse it might display zero but not use zero in calculations). A large number example is:
=123456789^2
Excel gives 15241578750190500 but the true answer is 15241578750190521.
Problems are not so bad when you use moderate sized numbers.
Special software allows arbitrary precision. The old UNIX tool "bc" gets these calculations correct.
This may seem trivial but small errors can mount up and cause software to fail. You should check out sites like here, here, and here.
I usually check results against "bc" for sets of calculations. Any problems can usually be sorted by expressing calculations differently. Here is a online calculator with arbitrary precision. If problems persist then you can use arbitrary precision libraries. There are a number for Excel.
I have to ask ... why use MAZ (MUAC-for-age z-score). It is an odd choice because:
(1) Age is often subject to considerable error in many settings. This usually manifests itself as age-heaping at 6, 12, 18, 24, 36, and 48 months in surveys. The effect of this can be to introduce a systematic bias. If the tendency is to report ages as "milestones passed" so that (e.g) a child aged >= 24 month and < 36 months will be reported as 2 years (24 months) then age will tend to be underestimates and MAZ (and HAZ and WAZ) will be underestimated.
(2) The key advantage of MUAC over (e.g.) WHZ is the relatively strong predictive power for near-term mortality of MUAC compared to WHZ. Work done on large cohorts shows that correcting MUAC for age or height does not improve prognostic value and may degrade prognostic value.
Easier and probably more useful, therefore, to use uncorrected MUAC and apply simple threshold case-definitions.
I hope this is of some use.