A strange rule for determine convexity of optimization constraint?

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I am an electrical engineer who is currently working with some optimization problem. One of my senior has a strange rule of thumb to determine the convexity of constraint for minimization problem.

His rules for this constraint are listed as follows:

$f\left( x \right) \le g\left( x \right)$

1/ If $f\left( x \right)$ is a convex function and $g\left( x \right)$ is a linear function then the constraint $f\left( x \right) \le g\left( x \right)$ can be written as a convex constraint.

2/ If $f\left( x \right)$ is a convex function and $g\left( x \right)$ is a concave function then the constraint $f\left( x \right) \le g\left( x \right)$ can be written as a convex constraint.

The problem is that most people in my group just follow this without asking any question. Therefore, I was wondering if this is true or not since there is no proof.

Would you kindly help me with this ?

Thank you for your enthusiasm !