Exam Professional Machine Learning Engineer topic 1 question 28 discussion

AD - please weigh in guys

A. Use the interleave option for reading data. - Yes, that helps to parallelize data reading. B. Reduce the value of the repeat parameter. - No, this is only to repeat rows of the dataset. C. Increase the buffer size for the shuttle option. - No, there is only a shuttle option. D. Set the prefetch option equal to the training batch size. - Yes, this will pre-load the data. E. Decrease the batch size argument in your transformation. - No, could be even slower due to more I/Os. https://www.tensorflow.org/guide/data_performance

A) and D) are the right answers!

A and D : https://www.tensorflow.org/guide/data_performance , interleave and prefetch

Went with A & D

yes AD

Yes AD

YES.....AD - agree with danielp1

AD - agree with danielp1 By the way, this is handy to understand the significance of shuffle buffer_size: https://stackoverflow.com/a/48096625/1933315

I think D & E are correct.

AD should be the right answer.

Answers?

For me it should be D and E as well. Prefetching will help reading data while training is performed, which helps with the bottleneck, D is for sure right. I think decreasing batch size would help too, because less records will be read in each training step (reading a lot of records would lead to the bottleneck described, as reading data is costly). I'm not 100% sure on A, personally I don't think processing many input files concurrently would help in this case because the reading operation is precisely the problem. However, I'm no expert in this topic so I might be wrong.

I think it should be DE. I found this article https://towardsdatascience.com/overcoming-data-preprocessing-bottlenecks-with-tensorflow-data-service-nvidia-dali-and-other-d6321917f851