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Which of the following statements is true about stochastic gradient descent?

Which of the following statements is true about stochastic gradient descent? Correct Answer It processes one training example per iteration

Stochastic gradient descent processes one training example per iteration. That is it updates the weight vector based on one data point at a time. All other three are the features of Batch Gradient Descent.

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Stochastic gradient descent is also known as on-line gradient descent.
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Stochastic gradient descent (SGD) methods handle redundancy in the data much more efficiently than batch methods.
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In stochastic gradient descent the high variance frequent parameter updates causes the loss function to fluctuate heavily.
Stochastic gradient descent has the possibility of escaping from local minima.
Stochastic gradient descent cannot be used for risk minimisation.