Create starting point for numpy multiply, dot exercise. Clarify sum function calls

Author: ashwinvis

content/numpy.rst

@@ -191,14 +191,32 @@ Exercises 2
 
 .. challenge:: Exercises: Numpy-2
 
-   - **Matrix multiplication** What is the difference between :data:`numpy.multiply` and :func:`numpy.dot` ? Try it.
-   - **Axis** What is the difference between :func:`np.sum(axis=1) <numpy.sum>` vs
-     :func:`np.sum(axis=0) <numpy.sum>` on a two-dimensional array? What if you leave out the axis parameter?
+   Create the following arrays:
+
+   .. code-block:: python
+
+      # 1-dimensional arrays
+      x = np.array([1, 10, 100])
+      # TODO: similar to `x`, create another 1-dimensional array with shape (3,)
+      # y = np.array(...)
+
+      # 2-dimensional arrays
+      a = np.array([[1, 2], [3, 4]])
+      # TODO: similar to `a`, create another 2-dimensional array with shape (2, 2)
+      # b = np.array(...)
+
+   - **Matrix multiplication** What is the difference between :data:`numpy.multiply` and :func:`numpy.dot` ? Try calling these functions
+     with either ``x, y`` (1D arrays) or ``a, b`` (2D arrays) as input and observe the behaviour.
+   - **Axis** What is the difference between :func:`np.sum(a, axis=1) <numpy.sum>` vs
+     :func:`np.sum(a, axis=0) <numpy.sum>` on a two-dimensional array? What if you leave out the axis parameter?
 
 
 .. solution:: Solutions: Numpy-2
 
-   - **Matrix multiplication** ``np.multiply`` does elementwise multiplication on two arrays, while ``np.dot`` enables matrix multiplication.
+   - **Matrix multiplication** ``np.multiply`` does elementwise multiplication on two arrays. The function ``np.dot`` enables:
+     - *dot product* and returns a scalar, when both input arrays are 1 dimensional
+     - *matrix multiplication* and returns back a 2-dimensional array, when both the input arrays are 2 dimensional
+     However, ``a @ b`` is preferred over ``np.dot(a, b)`` to express matrix multiplication.
    - **Axis** ``axis=1`` does the operation (here: ``np.sum``) over each row, while axis=0 does it over each column. If axis is left out, the sum of the full array is given.