Why is pure sample covariance a bad metric to understand the degree of correlation between two variables?

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Covariance helps you understand how variables are linearly related.

Would it be possible to have two pairs of variables in a deterministic relationship (i.e. linearly correlated variables) that have different values for the covariance?

My guess would be that if you had one pair with low sample variances and the other pair with high sample variances, you could have the same relationship but a different covariance. Am I on the right path?