  Motivated by upcoming photometric and spectroscopic surveys (Vera C. Rubin
Observatory Legacy Survey of Space and Time (LSST), Manuakea Spectroscopic
Explorer), we design the statistical proxies to measure the cadence effects on
active galactic nuclei (AGN) variability-observables (time-lags, periodicity,
and structure-function (SF)). We constructed a multiple-regression model to
statistically identify the cadence-formal error pattern knowing AGN time-lags
and periodicity from different surveys. We defined the simple metric for the
SF's properties, accounting for the 'observed' SF's deviation relative to those
obtained from the homogenously-sampled light curves. We tested the regression
models on different observing strategies: the optical dataset of long
light-curves of eight AGN with peculiarities and the artificial datasets based
on several idealized and LSST-like cadences. The SFs metric is assessed on
synthetic datasets. The regression models (for both data types) predict similar
cadences for time-lags and oscillation detection, whereas for light curves with
low variability ($\sim 10\%$), cadences for oscillation detection differ. For
higher variability ($\sim20\%$), predicted cadences are larger than for
$F_{var}\sim 10\%$. The predicted cadences are decreasing with redshift. SFs
with dense and homogenous cadences are more likely to behave similarly. SFs
with oscillatory signals are sensitive to the cadences, possibly impacting
LSST-like operation strategy.
  The proposed proxies can help to select spectroscopic and photometric-surveys
cadence strategies, and they will be tested further in larger samples of
objects.
