Ignoring features in XGBoost by setting them as "missing"

I have some data n x m and I want to ignore certain features. One idea I had is to mark those features as missing, since XGBoost can handle missing values by default, e.g. using nan when constructing the DMatrix:

n, m = 100, 10

X = np.random.uniform(size=(n, m))
y = (np.sum(X, axis=1) = 0.5 * m).astype(int)

# ignore certain features: mark them as missing
X[:, 2:7] = np.nan

dtrain = xgb.DMatrix(X, label=y, missing=np.nan)
model = xgb.train(params={'objective': 'binary:logistic'}, dtrain=dtrain)

My question is whether now all such missing features will be ignored during inference. Experimentally, I observed that if I feed the model a dtrain matrix copy, but such that the missing values are filled with random data, I get the same predictions as on the original dtrain:

X2 = X.copy()
X2[np.isnan(X2)] = np.random.uniform()

np.allclose(
    model.predict(dtrain), 
    model.predict(xgb.DMatrix(X2, missing=np.nan)),
    rtol=0,
    atol=1e-12
) # True

Is it safe to say that, in general, any missing values during train, but which may be present during inference are ignored? Are there any caveats?