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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: Performance Optimization and Best Practices	- Efficient Snowpark execution 1. Pushdown optimization concepts 2. Resource utilization tuning
Topic 2: User Defined Functions and Stored Procedures	- Extending Snowpark with custom logic 1. Python UDFs 2. Stored procedures in Snowpark
Topic 3: Testing, Debugging, and Deployment	- Production readiness 1. Debugging Snowpark applications 2. Deployment strategies
Topic 4: DataFrame Operations and Data Processing	- Data transformation workflows 1. Filtering, selecting, and aggregations 2. Joins and window functions
Topic 5: Data Engineering with Snowpark	- Pipeline development 1. Batch processing workflows 2. Integration with Snowflake data pipelines
Topic 6: Snowpark Fundamentals	- Snowpark architecture and concepts 1. Snowflake execution model overview 2. Snowpark APIs and supported languages

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q109-Q114):

NEW QUESTION # 109
When using key pair authentication with Snowpark, what security best practices should you implement to protect your private key?
(Select all that apply)

A. Store the private key directly in the Snowpark code repository.
B. Regularly rotate the key pair.
C. Grant broad access to the environment variable containing the private key to all developers.
D. Encrypt the private key at rest.
E. Store the private key in an environment variable or a secure secret management system (e.g., HashiCorp Vault, AWS Secrets Manager, Azure Key Vault).

Answer: B,D,E

Explanation:
Storing the private key directly in the code repository (A) is a major security risk. Encrypting the private key at rest (B) provides an additional layer of security. Storing the private key in an environment variable or secure secret management system (C) is the recommended approach. Granting broad access (D) increases the risk of compromise. Regularly rotating the key pair (E) limits the impact if a key is compromised. Options B,C and E are the most secure ones.

NEW QUESTION # 110
You are building a Snowpark application to process sensitive data'. To enhance security, you want to leverage ephemeral sessions. Which configurations, passed to 'snowpark.Session.builder.configS , are required and sufficient to create an ephemeral session? Assume your Snowflake environment is properly configured to allow ephemeral sessions.

A. Setting the 'role' parameter to a role with appropriate privileges and 'warehouse'.
B. Setting the 'role' parameter to a role with appropriate privileges and 'database' and 'schema'.
C. Only setting the 'role' parameter to a role with appropriate privileges.
D. Ephemeral sessions do not have specific configurations; they are automatically created by Snowflake when the application connects.
E. Setting the 'role' parameter to a role with appropriate privileges, 'database', 'schema', and 'warehouse'.

Answer: E

Explanation:
Ephemeral sessions in Snowflake require that you specify a 'role', 'database', 'schema', and 'warehouse'. These parameters define the context within which the session will operate. Without these parameters, the session cannot be properly established. Although option A set role which is important for setting the context but it is insufficient. Ephemeral sessions are not automatic and require proper configuration.

NEW QUESTION # 111
You have a Snowpark DataFrame named containing daily sales transactions. The DataFrame includes columns like 'transaction_id' , 'product id', 'sale_date', and 'sale_amount'. You need to perform the following transformations and persist the results: (1) Calculate the total sales amount for each product on a daily basis. (2) Store the aggregated results into a new table named , partitioned by 'sale_date'. (3) Ensure that if the table already exists, the new data is appended to the existing table. Which of the following code blocks achieve these requirements in the most efficient and correct manner?

Answer: D

Explanation:
Option A is the most efficient and correct. It first aggregates the data as required, then uses 'mode('append')' to add new data without overwriting existing data, and for partitioning. Option B will overwrite existing data. Option C is equivalent to option A, but is a less common coding style. Option D renames the default aggregate column name which is less readable but also works correctly. Option E, 'mergeSchema' isn't a valid option to be passed.

NEW QUESTION # 112
You're developing a Snowpark application that reads data from a Snowflake table, performs several transformations, and then writes the results back to a different table. You want to ensure that the entire process is executed as a single atomic transaction, even if it involves multiple Snowpark DataFrames and operations. Which of the following actions are required to achieve this transactional behavior?

A. All Snowpark operations within a single session are automatically executed as a single atomic transaction by default; no additional configuration is required.
B. Ensure that the target table for writing the results has the 'TRANSIENT' property set to 'TRUE'.
C. Configure the Snowpark session with the parameter set to ' FALSE
D. Explicitly start a transaction using 'session.beginTransaction()' at the beginning of the Snowpark application and commit it using 'session.commitTransaction(Y at the end.
E. Leverage the 'CREATE OR REPLACE TABLE AS SELECT statement within a Stored Procedure called from your Snowpark code. All DML operations done as part of stored proc is transactional

Answer: A

Explanation:
Snowflake inherently provides transactional consistency. All operations within a single Snowpark session are automatically executed as a single atomic transaction by default. This is a core feature of Snowflake and doesn't require explicit transaction management in most common scenarios. Options A, B and C are incorrect as Snowflake handles transaction automatically. E describes a possible solution, however, it isn't required.

NEW QUESTION # 113
You have two Snowpark DataFrames, 'dfl' and 'df2 , both containing customer data, but with slightly different schemas. 'dfl' has columns 'customer_id', 'name', and 'email'. 'df2' has columns 'id', 'customer name', and 'email_address'. You want to perform a set- based operation to find all unique customer IDs present in 'dfl but NOT in 'df2' , considering that 'customer_id' in 'dfl corresponds to 'id' in 'df2. Which of the following code snippets will achieve this, ensuring that column names are correctly aligned before the operation?

Answer: E

Explanation:
Option D is the correct solution. First, 'customer_id')' renames the 'id' column in 'df2 to 'customer_id', aligning it with the 'customer_id' column in 'dfl Then, 'cifl performs the set difference operation, returning only the 'customer_id' values present in 'dfl& but not in the modified 'df2. 'exceptAll' (Option A) will include duplicates. Option B uses 'minus' which does not exist on Snowpark DataFrame. Options C uses 'subtract which also does not exist. Option E will cause unexpected results because the column name of dfl and df2 would be different.

NEW QUESTION # 114
......

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