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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q147-Q152):

NEW QUESTION # 147
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. Require all users to remove the password protection from their private keys to simplify the session creation process.
B.
C. Store the password for each user's private key in a separate, encrypted file and retrieve it during session creation.
D.
E. 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.

Answer: D

Explanation:
Option C is the most secure and correct approach. It handles both password-protected and non-password-protected private keys gracefully using the 'cryptography' library, without storing passwords in memory or requiring users to compromise their security. It attempts to load the private key with the password (if provided), and if no password is provided, it defaults to 'None'. Options A and D have security vulnerabilities associated with storing or prompting for passwords. Option B forces users to weaken security. Option E doesn't consider password protected private keys.

NEW QUESTION # 148
A data engineering team is developing a Snowpark stored procedure in Python to perform anomaly detection on time-series data stored in a Snowflake table named 'sensor_readingS. The stored procedure needs to efficiently process large volumes of data and return only the rows identified as anomalies. Which of the following approaches would provide the most performant and scalable solution for operationalizing this stored procedure?

A. Execute a SQL query from within the stored procedure using the Snowflake connector for Python to fetch the relevant data, then use a standard Python loop to iterate through the results and apply anomaly detection logic. Return the anomalous rows as a list of dictionaries.
B. Use the method to include a pre-trained anomaly detection model (pickled object) in the stored procedure's execution environment. Load the model, use it to predict on the data fetched using 'session.table(Y , and return a Snowpark DataFrame of anomalies.
C. Load the entire 'sensor_readings' table into a Pandas DataFrame within the stored procedure, perform anomaly detection using a Python library like 'scikit-learn' , and then create a Snowpark DataFrame from the filtered Pandas DataFrame to return the results.
D. Use the Snowpark API to directly perform anomaly detection calculations (e.g., rolling statistics, z-score calculations) on the 'sensor_readings' table within the stored procedure, leveraging Snowpark's distributed processing capabilities, and then return the resulting Snowpark DataFrame containing only the anomalies.
E. Create a UDF with a Scala implementation and use it inside the Snowpark stored procedure to detect anomalies using the Scala implementation for increased processing power.

Answer: D

Explanation:
Option B is the most performant and scalable. It leverages Snowpark's distributed processing to perform the anomaly detection calculations directly on the Snowflake data, avoiding the overhead of transferring large datasets to Pandas DataFrames or using inefficient Python loops. Using a SQL Query inside the stored procedure would work but not as efficient as Snowpark dataframes that are lazy executed. Transferring data into a pandas dataframe is also inefficient as it reduces Snowflake's ability to perform the computation inside Snowflake's distributed framework. Lastly a Scala UDF would still require data transfer between Snowpark and Scala, which makes it ineffecient.

NEW QUESTION # 149
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 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.
C. An inefficient UDF (User-Defined Function) is consuming excessive memory within the Java runtime.
D. The Snowpark driver process is attempting to load the entire result set into memory, exceeding the available heap space.
E. Snowflake's internal query optimizer has generated a suboptimal execution plan, leading to excessive intermediate data materialization.

Answer: D

Explanation:
OutOfMemoryError in Snowpark is most often due to the driver process attempting to load a large result set into memory. Snowpark is designed to push down computations to Snowflake, but certain operations can force data to be collected on the driver. The correct response highlight this. While the other options might contribute, they are less likely to be the direct cause of a Java heap space error specifically.

NEW QUESTION # 150
Consider the following Python code snippet using Snowpark:

Which of the following statements are true regarding this Snowpark code?

A. The code uses best practices by explicitly closing the Snowflake session, preventing resource leaks.
B. The code establishes a connection to Snowflake using the provided credentials.
C. The code reads data from a table named 'my_table' in Snowflake.
D. The code calculates the sum of the 'sales' column, grouped by 'category' , and saves the result to a new table named , overwriting it if it exists.
E. The code will fail because password authentication is deprecated and replaced by Key Pair authentication.

Answer: A,B,C,D

Explanation:
The code correctly connects to Snowflake, reads data from 'my_table', performs aggregation, and saves the result to 'agg_sales_table' with overwrite mode, and closes the session. While password authentication is discouraged, it is still supported. The other options accurately describe the code's functionality. Closing the session is a best practice to release resources.

NEW QUESTION # 151
A data engineering team is migrating a series of complex SQL queries into Snowpark Python to leverage vectorized UDFs and optimize performance. They currently use several Common Table Expressions (CTEs) within their SQL queries. What is the most efficient and Pythonic approach to create a Snowpark DataFrame representing the result of a complex SQL query with multiple CTEs, minimizing code redundancy and maintaining readability?

A. Concatenate the SQL statements representing each CTE and the final SELECT statement into a single long string, then use to create the DataFrame.
B. Create separate temporary tables in Snowflake for each CTE using SQL, then create Snowpark DataFrames from these temporary tables using session.table(table_name)'.
C. Use the method to create separate Snowpark DataFrames for each CTE and then use Snowpark DataFrame joins to combine them into the final DataFrame.
D. Utilize the method to create a single Snowpark DataFrame by executing the entire SQL query with CTEs. Then, use Snowpark's DataFrame API for further transformations if needed.
E. Re-write all CTEs using Snowpark's DataFrame API directly, avoiding the use of 'session.sql()' altogether.

Answer: D

Explanation:
Option D is the most efficient. Using with the complete SQL query, including CTEs, leverages Snowflake's query optimizer to handle the CTEs efficiently. While rewriting in Snowpark DataFrame API (Option E) might eventually be desirable for full Snowpark utilization, it's a more significant undertaking. Options A and B introduce inefficiencies (string manipulation, temporary tables) or unnecessary complexity (separate DataFrames and joins). Option C is also less performant than submitting the whole query in one go.

NEW QUESTION # 152
......

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SPS-C01 Latest Test Practice | Reliable SPS-C01 Exam Camp

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Snowflake SPS-C01 Exam Dumps - Preparation Material For Best Result [2026]

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q147-Q152):

Avail [Updated 2026]! Snowflake SPS-C01 Exam Questions | Alleviate Exam Stress

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