DSA-C03 Exam Dumps | A team is using Snowflake to build a supervised machine learning model for image classification. The

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A team is using Snowflake to build a supervised machine learning model for image classification. The images are stored in a Snowflake table, and the labels are in a separate table. The goal is to train a model using Snowpark Python. Which of the following code snippets represents the MOST efficient way to join the image data with its corresponding labels, pre-process the images (resize and normalize), and prepare the data for model training using Snowpark DataFrame transformations? Assume contains image data as binary, 'label df contains the image labels, and 'resize normalize udf' is a UDF that handles resizing and normalization.

Correct Answer: C,E

Options C and E represent the most efficient approaches using Snowpark DataFrames. Option C performs the join, preprocesses the images using the UDF, and selects the required columns, all within the Snowflake environment without pulling data to the client prematurely. It prepares the data for downstream tasks such as model training or saving to a new table. Option E enhances upon this by converting the Snowpark DataFrame to a Pandas DataFrame and then to NumPy arrays, which are common formats for machine learning libraries. This is a efficient way to perform complex transformations that are not readily available within the standard Snowpark API. Option A collects the entire DataFrame to the client, which is highly inefficient for large datasets. Option B uses RDDs (Resilient Distributed Datasets), which are an older Spark API and less efficient than DataFrames in Snowpark. Option D performs individual queries for each image ID, resulting in a large number of round trips to the database and is extremely inefficient. Option E also implicitly uses the power of pandas vectorized operations, leading to increased performance.

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