Snowflake SPS-C01 : Snowflake Certified SnowPro Specialty - Snowpark

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are tasked with optimizing a Snowpark Python application that performs complex data transformations on a large dataset. The application is running slower than expected, and you suspect that data serialization and transfer between the Snowpark client and the Snowflake engine are bottlenecks. Which of the following strategies could you implement to improve performance? (Select all that apply.)

A) Utilize smaller batch sizes when writing data back to Snowflake to reduce memory pressure on the client.
B) Create and utilize temporary tables within Snowflake to store intermediate results of complex transformations.
C) Minimize the amount of data transferred between the client and the engine by pushing down as much computation as possible to Snowflake using Snowpark DataFrame operations.
D) Convert all dataframes to Pandas dataframes locally and perform data manipulation with Pandas methods to take advantage of local resources.
E) Increase the configuration parameter to maximize parallelism within the Snowpark engine without considering resources or potential bottleneck.

2. You are tasked with optimizing the performance of a Snowpark Python application that performs complex data transformations on a large dataset of IoT sensor readings. The application uses a Snowpark-optimized warehouse. You notice that the application is consistently slow, with CPU utilization on the warehouse fluctuating significantly. Which of the following actions would be MOST effective in addressing this performance issue? Assume the dataset is partitioned on the 'sensor_id' column within Snowflake.

A) Increase the warehouse size to a larger instance (e.g., from X-Small to Small). This will provide more CPU and memory resources.
B) Enable auto-scaling on the warehouse with a minimum of 2 and maximum of 5 clusters. This will allow the warehouse to dynamically adjust capacity based on workload.
C) Ensure the Snowpark DataFrame transformations are pushed down to Snowflake as much as possible by avoiding actions like 'collect()' until absolutely necessary and leverage stored procedures.
D) Rewrite the Snowpark DataFrame transformations using only built-in Snowpark functions and avoid using User-Defined Functions (UDFs) written in Python.
E) Repartition the Snowpark DataFrame using partition_expression='sensor_id')' before applying transformations. Then, explicitly colocate similar operations.

3. You have a Snowpark DataFrame named containing customer data. You want to calculate the average order value for each customer and rank them based on this average. The 'customer_df contains columns 'customer_id' and 'order_value'. Which of the following Snowpark code snippets correctly performs this task, returning a new DataFrame with 'customer_id', 'average_order_value', and 'rank' columns?

A)

B)

C)

D)

E)

4. Which of the following statements are correct regarding the authentication methods available when creating a Snowpark session?

A) Using username/password authentication is the most secure and recommended approach for production environments.
B) OAuth authentication requires pre-configuration in Snowflake and typically involves client IDs, client secrets, and refresh/access tokens.
C) Snowflake supports MFA(Multi-Factor Authentication) using Web Browser Authentication, which needs to be enabled at an account level.
D) OAuth with external Oauth providers (like Okta or Azure AD) is not supported in snowpark, and only username password can be used.
E) Key pair authentication uses a private key to establish a secure connection without needing to store passwords directly in the code.

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

Solutions: