Exam Professional Machine Learning Engineer topic 1 question 15 discussion

Should be D

My option is D. Cite from Google Pag: to construct a Dataset from data in memory, use tf.data.Dataset.from_tensors() or tf.data.Dataset.from_tensor_slices(). When input data is stored in a file (not in memory), the recommended TFRecord format, you can use tf.data.TFRecordDataset(). tf.data.Dataset is for data in memory. tf.data.TFRecordDataset is for data in non-memory storage.

this is one of the review questions in Chapter 3 in the book "Official Google Cloud Certified Professional Machine Learning Engineer Study Guide". tf.data.Dataset.prefetch transformation decouples the time when data is produced to the time when data is consumed so it can reduce the latency. Also the transformation can reduce the memory usage. By the way, tf.data.Dataset.interleave transformation can also used to reduce the latency and memory usage.

D) Storing images in TFRecords optimises storage for images.

tf.data.Dataset is for data in memory. tf.data.TFRecordDataset is for data in non-memory storage.

why this website shows wrong option as answer, this is my observation from so many questions?

D is correct

D because: tf.data.Dataset is for data in memory. tf.data.TFRecordDataset is for data in non-memory storage.

all "correct" answers are wrong

Went with D

For all questions the given answers and voted answers are different. Which one should be considered for exam?

Converting your data into TFRecord has many advantages, such as: More efficient storage: the TFRecord data can take up less space than the original data; it can also be partitioned into multiple files. Fast I/O: the TFRecord format can be read with parallel I/O operations, which is useful for TPUs or multiple hosts

my option is D

Ans: D

ans: D

For data in memory use tf.data.Dataset, for data in non-memory storage use tf.data.TFRecordDataset. Since data don't fit in memory, go with option D.

Correct answer is "D"