= Solution
Uniform <signal sampling> at rate $f_s$ multiplies a continuous signal by a time-domain <Dirac comb>, giving frequency-domain replicas spaced by $f_s$:
$$
X_s(f)=f_s\sum_{k=-\infty}^{\infty}X(f-kf_s).
$$
If a baseband signal is restricted to $|f|\leq B$ and \b[$f_s>2B$], these replicas can be separated by an ideal reconstruction filter. Frequencies differing by integer multiples of $f_s$ have the same sampled phase progression; for real signals they fold into the interval from zero to $f_s/2$. This ambiguity is \b[<aliasing>].
\Image[/past-exam-of-the-physics-a-course-of-the-university-of-cambridge/2016/paper-1-sampling-alias.png]
{title=Two different continuous cosine frequencies produce the same samples when one lies above Nyquist}
{source=paper-1-sampling-alias.py}
For example, $0.8f_s$ and $0.2f_s$ cosines produce identical sample values. Apply an analogue <Anti-aliasing filter> before digitization and choose enough sample-rate margin for its finite transition band. A digital filter applied afterward cannot distinguish components already folded onto one another. Out-of-band noise also aliases unless filtered. The sample rate sets temporal/spectral coverage; <quantization (signal processing)> resolution sets amplitude precision and is a separate limitation. Bandpass <Signal sampling> has additional conditions beyond the simple baseband rule.<What the Nyquist Criterion Means to Your Sampled Data System Design by Walt Kester>[MT-002: spectral replicas, aliases and practical filtering]{ref}
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