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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowflake table containing JSON data with nested arrays and objects representing website user interactions. You want to extract all 'product_id' values from within an array named 'viewed _ products' nested inside a 'session' object for each event, using Snowpark for Python. Assume the 'raw_events' table has a variant column called 'event_data". Which of the following Snowpark code snippets will correctly extract and flatten the 'product_id' values into a DataFrame?

A)

B)

C)

D)

E)

2. You have a Python UDTF that calculates a running average from a stream of numerical data'. The UDTF's 'process' method maintains state (the running sum and count) between calls. You need to ensure that the UDTF's state is properly initialized for each new group of data processed within a Snowpark DataFrame. What are the requirements?

A) The UDTF class must define a '___del___' method. This method will be called by Snowpark at the beginning of processing each group of rows.
B) The UDTF class must have an '__init____' method to initialize the state variables. This '___init____' method will be called once per UDTF instance.
C) The UDTF class must define a 'end_partition' method to finalize processing and avoid memory leaks.
D) The UDTF class must define a '___init___' method to initialize the state variables and also 'reset method. This '___init____' and 'reset methods will be called once per UDTF instance.
E) The UDTF class must define a 'reset' method. This method will be called by Snowpark at the beginning of processing each group of rows.

3. You are tasked with building a Snowpark function to perform an upsert operation on a Snowflake table using a DataFrame. The function should take the target table name, a staging DataFrame, a join key column, and a list of columns to update. The function needs to handle potential schema evolution (i.e., columns may be added or removed from either the target table or the staging DataFrame) gracefully without causing the entire upsert to fail. Which of the following approaches, or combinations of approaches, would best address this requirement?

A) Rely on Snowflake's automatic schema detection during the 'merge' operation to automatically adapt to schema changes.
B) Before the 'merge' operation, use 'DataFrame.select' on the staging DataFrame to project only the columns that exist in the target table.
C) Before the merge, create a temporary table with the exact schema of the target table, insert all the data from the DataFrame into it, and then use the temporary table as source for the merge. Handle the schema evolution with dynamic sql if required.
D) Dynamically generate the SQL 'MERGE' statement within the function, comparing the columns present in the target table and the staging DataFrame, and only including those columns that exist in both.
E) Use the 'exceptAll' to ensure that there are no schema evolution issues.

4. You're designing a Snowpark application to process large CSV files containing sensor data'. Each CSV file has millions of rows, and you need to calculate aggregate statistics (e.g., average, min, max) for specific sensor readings. The processing needs to be highly parallelized for performance. You have the following code snippet (incomplete):

Which of the following code snippets, when inserted at the ' ??? Add code here to calculate aggregate statistics ??? ' marker, would correctly calculate the average, minimum, and maximum readings for a column named 'sensor value' and return the result in a new Snowpark DataFrame?

A)

B)

C)

D)

E)

5. You have a Snowpark application that processes sensitive data'. To enhance security, you want to use key pair authentication and ensure that the private key is never exposed in plain text within the application or logs. Which of the following strategies offers the most robust protection against accidental key exposure, even in the event of a security breach of the application server itself?

A) Store the private key encrypted using AES encryption with a key derived from a passphrase. Prompt the user for the passphrase each time the application starts.
B) Store the private key encrypted with a symmetric key. Store the symmetric key in a separate environment variable.
C) Store the private key in an environment variable on the application server, ensuring that only the application user has read access.
D) Use a dedicated Hardware Security Module (HSM) or a cloud-based key management service (e.g., AWS KMS, Azure Key Vault) to store and manage the private key. Configure the Snowpark application to retrieve the key only when needed and never store it locally.
E) Store the private key in an encrypted file with restricted access, decrypting it only during session creation and overwriting the memory location immediately afterwards.

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