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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question 1

You are developing a Snowpark application that requires secure access to Snowflake. You need to establish a session using Key Pair authentication. You have stored your private key in an encrypted file and have the passphrase available. Assume you've correctly configured your Snowflake user with the public key. Which of the following methods can be used to load the private key, assuming that 'PRIVATE KEY PATH' stores path to encrypted private key and 'PRIVATE KEY PASSPHRASE stores passphrase?

A.

B.

C.

D.

E.

Question 2

You are tasked with automating the creation of Snowpark sessions using key pair authentication for multiple users. You have a function that retrieves connection parameters (account, user, private key, etc.) for each user from a secure configuration file. The private keys are stored in PEM format. However, some users' private keys are password-protected. Which of the following approaches ensures the secure and correct establishment of Snowpark sessions for all users, including those with password-protected private keys? Assume get_user config(username)' retrieves the user's configuration, including the private key and password (if any).

A. Attempt to establish a session without a password. If it fails, prompt the user for the password and retry the session creation using the provided password. Store the password temporarily in memory.
B. Store the password for each user's private key in a separate, encrypted file and retrieve it during session creation.
C.

D. Require all users to remove the password protection from their private keys to simplify the session creation process.
E.

Question 3

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

Question 4

You are working with a Snowpark application designed to process data from an event table. While testing a complex transformation involving several joins and window functions, you encounter the following error: 'java.lang.OutOfMemoryError: Java heap space'. The application uses Snowpark DataFrames and is running on a reasonably sized virtual warehouse. What is the MOST likely cause of this error in the context of Snowpark and Snowflake?

A. There's a circular dependency in the DataFrame transformations, causing an infinite loop and memory leak.
B. The Snowpark driver process is attempting to load the entire result set into memory, exceeding the available heap space.
C. Snowflake's internal query optimizer has generated a suboptimal execution plan, leading to excessive intermediate data materialization.
D. An inefficient UDF (User-Defined Function) is consuming excessive memory within the Java runtime.
E. The virtual warehouse is undersized for the volume of data being processed, leading to excessive spilling to disk and eventual memory exhaustion on the driver node.

Question 5

Consider a Snowflake table 'sales_data' with a VARIANT column 'order_details' containing an array of JSON objects, where each object represents an item in an order. Each item object has fields like 'quantity', and 'price'. You need to calculate the total price for each order by summing the product of 'quantity' and 'price' for all items in the 'order_details' array. Which of the following Snowpark Python snippets correctly accomplishes this?

A.

B.

C.

D.

E.

Solutions: