You could test if there is a copy e.g. by defining a numpy array with 1 billion entries (8 GB for float64) and passing it to Julia, while looking at your system memory consumption.
The most simple calling of Julia functions on Numpy arrays seems to make indeed a copy of them.
Test code:
import julia
jl = julia.Julia()
import numpy as np
my_sum = jl.eval("my_sum(x) = sum(x)")
small_a = np.random.randn(100)
my_sum(small_a) # compile method
a = np.random.randn(1_000_000_000) # 8 GB
np.sum(a)
my_sum(a) # memory consumtion increases significantly, indicating a copy