The model defined with layers_dims = [X.shape[0], 20, 3, 1]. So, the dimension of A1=(20,m) but it doesn’t pass the test (as copied below). But passes all tests if I change (20,m) to (2,m).

in forward_propagation_with_dropout(X, parameters, …
42 D1 = np.random.rand(20,m)
43 D1 = (D1<keep_prob).astype(int)
—> 44 A1 = A1*D1
45 A1 = A1/keep_prob
46 # YOUR CODE ENDS HERE

ValueError: operands could not be broadcast together with shapes (2,5) (20,5)

It is a mistake to “hardcode” the dimensions on the np.random.rand call. The better idea is to use the “shape” attribute of the relevant A matrix (A1 in the case you are showing).

BTW this is a bug in your code, not a bug in the tests or the grader, so I will take the liberty of editing the title of your post.

I haven’t quite figured out the correct arguments to pass into np.random.rand()… tried a few different approaches and still getting below error. What am I missing?

As D1 is expected to have the same shape of A1, you need to pass the size of A1 as parameters.
If you pass (2, A1.shape[0]), then, the shape of output is (2, 2). On the other hand, the shape of A1 is (2,5). Where “2” comes from ? You can get dimension of A1 as A1.shape, and extract each size like A1.shape[0], A1.shape[1].