You trained a binary classifier model which gives very high accuracy on thetraining data, but much lower accuracy on validation data. The following maybe true: a. This is an instance of overfitting.
b. This is an instance of underfitting.
c. The training was not well regularized.
d. The training and testing examples are sampled from different distributions.

a. This is an instance of overfitting.

Explanation:

In data modeling and machine learning practice, data modeling begins with model training whereby the training data is used to train and fit a prediction model. When a trained model performs well on training data and has low accuracy on the test data, then we say say the model is overfitting. This means that the model is memorizing rather Than learning and hence, model fits the data too well, hence, making the model unable to perform well on the test or validation set. A model which underfits will fail to perform well on both the training and validation set.

it would be a. excessive network collisions or d. slow connectivity between devices those would be my answer.