Can redundant/irrelevant features be called a Noise? Let's say we want to predict job applicant' salary. We have a dataset with following features:
{Age, Experience, Education, Astrological_Sign, Weather_Today}
5 features in total.
In this set, features Astrological_Sign and Weather_Today are irrelevant to one's chances of getting a good (or bad) salary. If we train a model on all 5 of these features, it would perform worse than if we were to train it on Age, Experience and Education only, because it learns irrelevant information.
Q: Can these two features Astrological_Sign and Weather_Today, in terms of definitions, be called a Noise in our data? If no, how must they be called? Simply "irrelevant features" as I called them here?
 A: More a comment on the other answer by @Adzil.  Astrological sign is related to birth month, so astrological sign could be taken as a predictor (falsely so) of football (that is, soccer) talent in young players. See

A characteristic that has been proven to be crucial to identify at a
young age is birth month, an effect known as the relative age effect
difference (RAE). This effect among male players, who have been
selected or identified as talented at a young age, is characterised by
birth early in the year and especially during the first three months
(Helsen, Van Winckelmann, & Williams 2005; Musch & Hay 1999; Peterson
2011, Verhulst 1992). This means that the players who are  sæther |
identification of talent in soccer  (idrottsforum.org | 2014-03-19 4)
perceived as talented have advantages over other athletes in their
cohort, as a result of differences in maturation and development of
the individual which takes place at different speeds (Gagne 2000;
Martindale et al, 2007). Even though this effect has been known since
1985, the same effect still exists among Norwegian premier level
players (Wiium, Ommundsen & Enksen 2010). As this effect is so well
documented, there is risk that the coaches gamble on the wrong
players, instead of players who might have major development potential
and developed better skill.

This is from the paper Talent identification in Soccer What do Coaches Look for.  Note especially that this effect is also seen in (Norwegian) elite footballers, so the wrong prediction, creating expectations, and caused by relative fast maturing as compared to later births in same age cohort, do have longtime effects ...
A: The question to ask is
"Do they have any affect at all?"
If your astrological sign can somehow impact your salary, then yes, if you do not include it in your model then it is white noise. However, if astrological sign has no impact on a person's salary then how can it even generate white noise different from zero? 
An example off the top of my head...
Cucumber growth rate on average grades in a philosophy class.
Side note: In this specific instance, astrological sign is related to your birth month so that is something to consider. 
