Deal of The Day! Hurry Up, Grab the Special Discount - Save 25% - Ends In 00:00:00 Coupon code: SAVE25
Free Preparation Discussions
MENU
Databricks Exam Databricks-Machine-Learning-Associate Topic 2 Question 2 Discussion
Actual exam question for
Databricks's
Databricks-Machine-Learning-Associate exam
Question #: 2
Topic #: 2
[All Databricks-Machine-Learning-Associate Questions]
Topic #: 2
A machine learning engineer is using the following code block to scale the inference of a single-node model on a Spark DataFrame with one million records:
Assuming the default Spark configuration is in place, which of the following is a benefit of using an Iterator?
Suggested Answer:
C
Using an iterator in the pandas_udf ensures 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.
Databricks documentation on pandas UDFs: Pandas UDFs
Mammie
11 months agoXochitl
10 months agoJospeh
10 months agoBernadine
10 months agoArthur
11 months agoIzetta
11 months agoDylan
11 months agoCallie
10 months agoTayna
10 months agoGerman
10 months agoAsuncion
10 months agoRebeca
10 months agoCarissa
10 months agoSkye
10 months agoYen
10 months agoErasmo
10 months agoLaura
11 months agoGlynda
11 months agoKaran
11 months ago