For the extremely special case where you can use a Fourier transform(*),
When you collect p real sample points (in this case, p = 294),
and then you feed that though a discrete Fourier transform,
it gives you 2 components at n = p/2 (in this case, n = 147) different frequencies.
(Internally we practically always calculate a in-phase I and a quadrature Q component at each frequency, but many times we take the I and Q at each frequency and transform them to the amplitude A and the phase phi at that frequency).
Those 2 components at n=p/2 different frequencies can be used to exactly reconstruct the original series of sample points -- there is no more information.
In practice, we practically always want to have anti-aliasing filters and we usually have DC-blocking filters, so we usually find that the first few and the last few of those n=p/2 frequency components have zero amplitude,
and in some situations we find that only 1 or 2 or 16 (DTMF) frequencies ever have amplitudes significantly more than zero.
In those cases you only need 1 or 2 sinusoids (or however many the Fourier transform indicates have nonzero amplitude) in order to exactly reconstruct the original series of sample points.
But in other situations we find that every one of the n=p/2 (in this case, 147) components has a large amplitude -- in those cases, you need all those amplitudes to exactly reconstruct the signal.
(*) the sample-time values are evenly spaced from t0=0 to tp,
the lowest non-zero frequency w1 = 1/(tp),
the frequencies are evenly spaced integer multiples of w1, etc.