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Answers to the Most Common Questions About Snowflake Certified SnowPro Specialty - Snowpark

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question 1

You have a DataFrame 'df in Snowpark representing customer data'. One of the columns, 'customer_details', contains JSON objects with varying structures. Some objects contain 'address' and 'phone' fields, while others only contain 'email'. You need to write a Snowpark query to extract the 'city' from the 'address' field if it exists; otherwise, return NULL. What is the most efficient way to achieve this using the function?

A.

B.

C.

D.

E.

Question 2

You have a Snowpark DataFrame 'employees' with columns 'employee_id' (INT), 'name' (STRING), 'department' (STRING), and 'salary' (DOUBLE). You want to create a new DataFrame that contains the top 3 highest-paid employees within each department. Which of the following approaches is the MOST efficient and correct way to achieve this using Snowpark Python?

A.

B.

C.

D.

E.

Question 3

You have a Snowpark DataFrame containing data to be loaded into a Snowflake table You want to use the 'merge' operation to update existing records and insert new records. The has a column (BOOLEAN) that indicates whether a record should be updated (TRUE) or inserted (FALSE) if it doesn't already exist. Which of the following SQL expressions correctly implement this logic within the 'merge' operation's 'WHEN MATCHED' and 'WHEN NOT MATCHED' clauses?

A. WHEN MATCHED AND = TRUE THEN UPDATE SET columni = staging_df.columnl WHEN NOT MATCHED AND = FALSE THEN INSERT (columni) VALUES (staging_df.columnl)
B. WHEN MATCHED THEN UPDATE SET columni = staging_df.columnl WHEN NOT MATCHED THEN INSERT (columni) VALUES (staging_df.columnl)
C. WHEN MATCHED THEN UPDATE SET target_table.columnl = staging_df.columnl WHEN NOT MATCHED THEN INSERT (columni) VALUES (staging_df.columnl )
D. WHEN MATCHED AND THEN UPDATE SET columni = staging_df.columnl WHEN NOT MATCHED AND NOT THEN INSERT (columni) VALUES (staging_df.columnl)
E. WHEN MATCHED AND = TRUE THEN UPDATE SET target_table.columnl = staging_df.columnl WHEN NOT MATCHED AND = FALSE THEN INSERT (columni) VALUES (staging_df.columnl)

Question 4

You have a Snowpark DataFrame containing semi-structured data in a column named 'payload'. The 'payload' column contains JSON objects, and some of these objects contain nested arrays. You need to flatten all arrays, regardless of their level of nesting, and extract specific fields from the flattened data'. What is the MOST efficient approach using Snowpark to achieve this while minimizing the amount of code?

A. Use recursive UDFs to traverse and flatten the JSON structure, then create a new DataFrame from the flattened data.
B. Convert the DataFrame to an RDD, then use the RDD's 'flatMap' function to flatten the nested arrays before converting back to a DataFrame.
C. Use a single ' SELECT statement with multiple 'LATERAL FLATTEN' calls (using SQL syntax within 'session.sql') to flatten all nested arrays simultaneously.
D. Iteratively apply the 'explode' function to each array field within the 'payload' column, manually identifying and flattening each level of nesting.
E. Create a stored procedure in Snowflake that recursively flattens the JSON, then call this stored procedure from Snowpark to transform the DataFrame.

Question 5

You are developing a Snowpark Python application that needs to process large datasets. You want to optimize performance by leveraging user-defined functions (UDFs) to perform complex calculations in parallel across the Snowflake data warehouse. Which of the following statements regarding Snowpark UDFs are TRUE?

A. To ensure optimal performance, it is recommended to always use the default Snowflake Anaconda channel for UDF dependencies, as custom channels may introduce latency.
B. Snowpark UDFs automatically distribute the data and computation across multiple nodes in the Snowflake warehouse, but the distribution strategy cannot be controlled by the developer.
C. Snowpark UDFs can be defined using either Python or Java, providing flexibility in choosing the programming language best suited for the task. The Java UDF creation method will allow faster execution speeds.
D. Snowpark Python UDFs are always executed in a single process on the Snowflake warehouse, limiting their parallel processing capabilities.
E. Snowpark UDFs can be defined as either scalar UDFs (processing one row at a time) or vectorized UDFs (processing batches of rows), offering different performance characteristics.

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