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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Performance Optimization and Best Practices	20%	- Debugging and explain plans - Minimizing data transfer - Query pushdown and optimization - Caching strategies - Warehouse sizing for Snowpark - Vectorized UDFs
Topic 2: Data Transformations and DataFrame Operations	35%	- Persisting transformed data - Complex data pipelines - Using built-in functions - Filtering, Aggregating, and Joining DataFrames - Window functions
Topic 3: Snowpark API for Python	30%	- Establishing connections and session management - Reading and writing data - User-Defined Functions (UDFs) and Stored Procedures - Working with Semi-structured data - DataFrame creation and manipulation
Topic 4: Snowpark Concepts	15%	- Snowpark Sessions and connection management - Client-side vs. Server-side execution - Snowpark architecture and core concepts - Stored procedures and conditional logic - Snowpark DataFrames and query plans - Transformations vs. Actions

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q120-Q125):

NEW QUESTION # 120
You're working with Snowpark and have a DataFrame 'df containing a column 'json_data' with JSON strings. Some of these JSON strings are invalid. You need to parse the valid JSON strings and extract a field named 'product_id' from them. Invalid JSON strings should result in a 'NULL' value for the extracted 'product_id'. Which of the following approaches is the MOST robust and efficient way to achieve this?

Answer: D

Explanation:
Option B is the most robust and efficient. handles invalid JSON strings gracefully by returning 'NULL'. The other options have drawbacks: Option A will throw an error if the JSON is invalid. Option C involves a UDF, which can be slower than built-in functions. Option D assumes valid json and uses the native notation which will error with invalid JSON data. Option E uses Regex which is not recommended and can have perfomance impact as well as not robust

NEW QUESTION # 121
You have a Snowpark Python stored procedure that needs to access environment variables stored securely within Snowflake. Which of the following code snippets demonstrates the correct way to retrieve the value of an environment variable named 'API KEY' within your stored procedure?

Answer: E

Explanation:
Snowflake Snowpark sessions use the method to retrieve user-defined session parameters, including those that may be set to environment variables. The method allows access to parameters set at the account, user or session level. The 'os.envirorf (A) will not work in the Snowflake environment. B, D, and E are not valid Snowpark API methods for retrieving parameters.

NEW QUESTION # 122
A data engineer is tasked with creating a Snowpark Python application that needs to access data from multiple Snowflake accounts and regions. All accounts are using Snowflake's Business Critical edition. Which of the following approaches would be the MOST efficient and maintainable for managing and switching between different Snowpark sessions in this scenario?

A. Create separate Python scripts for each account and region, hardcoding the account identifiers and credentials within each script.
B. Create a configuration file (e.g., YAML or JSON) that stores the account identifiers and other connection details for each Snowflake account and region. Load this configuration and create separate Snowpark session objects for each account, storing them in a dictionary or list.
C. Use the 'snowflake.connector.connect' method to directly establish connections without using the Snowpark Session object.
D. Use a single Snowpark session object and dynamically update the connection parameters (account identifier, username, password) whenever switching to a different account and region.
E. Create a new Anaconda environment for each Snowflake account and install the necessary packages in each environment.

Answer: B

Explanation:
Storing connection details in a configuration file and creating separate Snowpark session objects is the most maintainable and efficient approach. It allows for easy switching between accounts and regions without modifying the core application logic. Option A is not scalable. Option B is risky because changing connection parameters on a live session can lead to unexpected behavior. Option D bypasses Snowpark functionalities. Option E is an overkill.

NEW QUESTION # 123
You are developing a Snowpark stored procedure in Python that needs to access and modify a temporary table within the same session.
Which of the following approaches is the MOST efficient and recommended way to achieve this?

A. Using 'session.createOrReplaceTempView()' to create a temporary view based on a DataFrame, and then querying the view using 'session.sql('SELECT
B. Creating a global temporary table using 'CREATE GLOBAL TEMPORARY TABLE outside the stored procedure and then accessing it within the stored procedure using 'session.table()'.
C. Using to create a DataFrame representing the temporary table, and then performing all operations using DataFrame transformations.
D. Using the Python DB API (e.g., 'snowflake.connector' ) within the stored procedure to establish a separate connection to Snowflake and interact with the temporary table.
E. Using 'session.sql('CREATE TEMPORARY TABLE followed by subsequent 'session.sql('lNSERT INTO and 'session.sql('SELECT statements to interact with the temporary table.

Answer: C

Explanation:
Option B, using 'session.createDataFrame()' and DataFrame transformations, is the most efficient and recommended approach. Snowpark DataFrames are optimized for execution within the Snowflake engine. Using DataFrame transformations allows Snowpark to leverage its query optimization capabilities. Option A, using 'session.sql()' repeatedly, involves parsing and executing SQL statements for each operation, which is less efficient. Option C, using a separate connection, introduces unnecessary overhead and complexity. Option D, global temporary tables, are not session-specific. Option E, creating a temporary view and then querying it with SQL, is also less efficient than using DataFrame operations directly.

NEW QUESTION # 124
You have a Snowflake table 'raw_events' containing JSON data in a VARIANT column named 'event_data'. This column contains nested JSON objects representing user activity on a website. You need to extract specific nested values and load them into a new Snowflake table 'user_activity' with columns 'event_type' , and 'timestamp'. Which of the following Snowpark code snippets is the MOST efficient and correct way to achieve this, assuming you want to minimize data transfer and optimize performance? Consider the potential for null values within the JSON.

Answer: C

Explanation:
Option C is the most efficient because it uses Snowflake's native JSON path notation (using 'f) directly within the 'select' statement, which is optimized for Snowflake's internal JSON handling. The other options use function calls ('get' , 'col'), which can introduce overhead. Furthermore, the code uses try_to_timestamp() to avoid errors due to null values

NEW QUESTION # 125
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