SPS-C01 Exam Dumps | You're building a Snowpark Python application that processes sensor data from various devices. The data

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Question 32/151

You're building a Snowpark Python application that processes sensor data from various devices. The data arrives as a stream of JSON objects, each containing the device ID, timestamp, and sensor readings. You want to use a Streamlit application to visualize near real- time aggregates on the data'. You're aiming to create a Snowpark DataFrame from this data, perform transformations, and then serve this DataFrame to Streamlit. Which of the following approaches concerning creating the initial DataFrame from JSON data is generally the MOST efficient and scalable for handling such a stream of data?

A. Read the JSON data directly from the stream into a Pandas DataFrame using , then convert the Pandas DataFrame to a Snowpark DataFrame using 'session.createDataFrame(pandas_df)'.

B. Write each incoming JSON object to a temporary file in cloud storage (e.g., AWS S3 or Azure Blob Storage) and then periodically use 'session.read.json()' to create a Snowpark DataFrame from the files.

C. Use Snowflake's Kafka connector to ingest the JSON data directly into a Snowflake table, and then create a Snowpark DataFrame from that table using 'session.table()'.

D. Utilize Snowpipe with auto-ingest configured to load the JSON data into a raw data Snowflake table, and subsequently, establish a Snowpark DataFrame using 'session.table('raw_data_table')'. You can then apply necessary transformations using Snowpark.

E. Iteratively append each JSON object to a Python list, then create a Snowpark DataFrame from the list using 'session.createDataFrame(list_of_json_objectsy.

Question 32/151

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