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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Snowpark Concepts and Architecture	25%	- Session management and connection 1. Create and configure Snowpark sessions 2. Authentication and connection settings - Snowpark architecture and execution model 1. Lazy evaluation and DAG execution 2. Transformations vs actions 3. Client-side vs server-side processing
Topic 2: Snowpark API and Development	30%	- Multi-language support 1. Environment setup and dependencies 2. Java and Scala API basics - Python API fundamentals 1. DataFrame creation from tables, views, SQL 2. Data persistence and writing results 3. Column operations and functions
Topic 3: Data Transformations and Operations	35%	- User-defined logic 1. Stored procedures with Snowpark 2. UDFs, UDAFs, UDTFs - DataFrame manipulation 1. Selection, projection, renaming, casting 2. Filtering, sorting, grouping, aggregation 3. Joins, unions, set operations - Advanced operations 1. Pivot and unpivot transformations 2. Window functions and analytics 3. Semi-structured data processing
Topic 4: Performance and Best Practices	10%	- Optimization techniques 1. Caching and warehouse sizing 2. Query pushdown and execution plans 3. Minimizing data movement - Security and governance 1. Access control and permissions 2. Data protection and compliance

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

NEW QUESTION # 109
You have a SQL query stored in a file named 'query.sqr which contains several complex analytical calculations. The query depends on a Snowpark 'session' object already established. You want to create a Snowpark DataFrame from the result of this query. Which of the following code snippets achieves this with optimal performance and readability, assuming correct file access permissions?

Answer: C

Explanation:
Option A provides the most straightfomard and efficient approach. It reads the SQL query from the file and directly creates a Snowpark DataFrame using 'session.sql(sql_query)'. Option B introduces Pandas, which is unnecessary and less efficient. Option C uses the Snowflake Connector outside of Snowpark's API, which is generally not the preferred approach. Option D has a non-existent function create_dataframe' , and Option E reads lines separately requiring a join which might be erroneous.

NEW QUESTION # 110
You have two Snowpark DataFrames, 'customers' and 'orders'. The 'customers' DataFrame has columns and 'customer name'. The 'orders' DataFrame has columns 'order id', 'customer id', and 'order amount'. You need to find all customers who have NOT placed any orders. Which of the following Snowpark set operations correctly implements this?

Answer: D

Explanation:
Option D is correct. It first selects the distinct 'customer_id' from both DataFrames. Then, it uses the 'minus' (or 'except_') set operation to find the difference between the customer IDs in the 'customers DataFrame and the customer IDs in the 'orders' DataFrame. This effectively returns the customer IDs of customers who have not placed any orders. The 'join' operation is not a set operation, and options B and C are not valid syntax for Snowpark. Option E is syntactically correct in Snowpark, and equivalent to Option D. However, since the question has to have one answer, Option D is kept.

NEW QUESTION # 111
A data science team wants to operationalize a Snowpark Python UDF that performs sentiment analysis on customer reviews. The UDF, 'analyze sentiment(review_text)', is currently defined within a Snowpark session. Which of the following approaches is the MOST efficient and scalable way to deploy this UDF for real-time scoring of incoming review data in a Snowflake table named 'CUSTOMER REVIEWS'?

A. Persist the Snowpark session with UDF definition using pickle and call it from another Snowpark session.
B. Register the 'analyze_sentiment' UDF as a persistent UDF in Snowflake and then call it from a SQL query that reads from 'CUSTOMER REVIEWS.
C. Create a Snowpark Dataframe that reads from 'CUSTOMER_REVIEWS , convert it to pandas dataframe and call analyze_sentiment function on pandas dataframe.
D. Call the 'analyze_sentiment' UDF directly within a Snowpark DataFrame transformation that reads from 'CUSTOMER_REVIEWS'.
E. Package the 'analyze_sentiment' function as a stored procedure and execute it using Snowpark session.execute.

Answer: B

Explanation:
Registering the UDF as a persistent UDF in Snowflake allows it to be called directly from SQL, leveraging Snowflake's query engine for optimal performance and scalability. Option A keeps the transformation entirely within Snowpark, which is a valid approach, but less scalable. Option C involves converting to a Pandas DataFrame, which moves data outside of Snowflake and negates its benefits. Option D creates a stored procedure that is correct but the question ask to perform in real time so it's not ideal. Option E is not the way to persist Snowpark with UDF.

NEW QUESTION # 112
You are developing a Snowpark application that utilizes a UDF. You need to ensure that the UDF runs with the privileges of the caller (the user executing the query). Which of the following steps are necessary to accomplish this while creating the Snowpark session?

A. No special steps are required when creating the Snowpark session; the UDF automatically inherits the caller's privileges.
B. When creating the Snowpark session, explicitly set the 'privilege' parameter to 'CALLER.
C. After creating the Snowpark session, execute the SQL command 'ALTER SESSION SET
D. When defining the UDF using Snowpark, ensure the argument is passed in the decorator. Create the Snowpark session as usual.
E. The account administrator needs to explicitly grant the 'CREATE FUNCTION' privilege to the user.

Answer: D

Explanation:
To ensure a UDF runs with the privileges of the caller, you need to explicitly specify the 'api_caller_identity=sf.Caller.CALLER when defining the UDF using Snowpark. This instructs Snowflake to execute the UDF with the caller's permissions. No special session configurations are needed. Option A is irrelevant for caller's identity. Option B is incorrect as it's not automatic. Option D does not exist. Option E is a valid SQL command but needs to be implemented in Python using 'session.sqr function

NEW QUESTION # 113
Consider a scenario where you're developing a Snowpark stored procedure that accesses sensitive data'. Which of the following strategies, when used together, provide a comprehensive approach to secure this stored procedure and protect the underlying data?
Select all that apply:

A. Masking sensitive data within the stored procedure using Snowflake's dynamic data masking policies.
B. Using 'EXECUTE AS CALLER and relying on the caller's privileges to access the data.
C. Encrypting the stored procedure's code using AES encryption before deployment.
D. Using 'EXECUTE AS OWNER and granting the 'SELECT privilege on the sensitive data tables to the stored procedure's owner role.
E. Implementing row-level security policies on the sensitive data tables.

Answer: A,B,E

Explanation:
Row-level security (RLS) ensures that users only see the data they are authorized to see, regardless of how they access it. EXECUTE AS CALLER ensures the procedure runs with the user's privileges, enforcing their existing access controls. Dynamic data masking provides an additional layer of security by masking sensitive data based on defined policies. 'EXECUTE AS OWNER grants the stored procedure access based on the procedure's owner's privileges, potentially bypassing individual user permissions. Stored procedure code encryption isn't supported within Snowflake.

NEW QUESTION # 114
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