Databricks-Machine-Learning-Associate Exam Dumps - Databricks ML Data Scientist Questions and Answers

Using an iterator in thepandas_udfensures that the model only needs to be loaded once per executor rather than once per batch. This approach reduces the overhead associated with repeatedly loading the model during the inference process, leading to more efficient and faster predictions. The data will be distributed across multiple executors, but each executor will load the model only once, optimizing the inference process.

References:

• Databricks documentation on pandas UDFs: Pandas UDFs

Vectorized pandas UDFs, also known as Pandas UDFs, are a powerful feature in PySpark that allows for more efficient operations than standard UDFs. They operate by processing data in batches, utilizing vectorized operations that leverage pandas to perform operations on whole batches of data at once. This approach is much moreefficient than processing data row by row as is typical with standard PySpark UDFs, which can significantly speed up the computation.

References

• PySpark Documentation on UDFs:https://spark.apache.org/docs/latest/api/python/user_guide/sql/arrow_pandas.html#pandas-udfs-a-k-a-vectorized-udfs

To retrieve the metadata description of a feature table created using the Feature Store Client (referred here asfs), the correct method involves callingget_tableon thefsclient with the table name as an argument, followed by accessing thedescriptionattribute of the returned object. The code snippetfs.get_table("new_table").descriptioncorrectly achieves this by fetching the table object for "new_table" and then accessing its description attribute, where the metadata is stored. The other options do not correctly focus on retrieving the metadata description.References:

• Databricks Feature Store documentation (Accessing Feature Table Metadata).

A train-validation split is often preferred over k-fold cross-validation (with k > 2) when computational efficiency is a concern. With a train-validation split, only two models (one on the training set and one on the validation set) are trained, whereas k-fold cross-validation requires training k models (one for each fold).

This reduction in the number of models trained can save significant computational resources and time, especially when dealing with large datasets or complex models.

References:

• Model Evaluation with Train-Test Split