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Snowflake SPS-C01 Exam Syllabus Topics:

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

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q213-Q218):

NEW QUESTION # 213
You are working with a Snowpark DataFrame representing sensor data. The DataFrame contains columns like 'timestamp', 'sensor id' , and 'value'. You need to perform a complex windowing operation to calculate the moving average of the 'value' for each 'sensor id' over a 5-minute window, but only for data points where the 'value' is greater than a threshold. The window should be defined based on the 'timestamp' column. What is the most efficient and correct approach to implement this using Snowpark DataFrames?

A. Use a loop to iterate over each 'sensor_id' , filter the DataFrame for that sensor, calculate the moving average using Pandas windowing functions, and then combine the results.
B. Use a combination of 'filter' to apply the threshold condition, 'Window.partitionBy' and 'Window.orderBy' to define the window, and 'avg' window function to calculate the moving average.
C. Create a UDF that takes a list of timestamps and values as input and returns the moving average. Apply this UDF to the entire DataFrame.
D. First, collect the entire DataFrame into a Pandas DataFrame, then use Pandas windowing functions to calculate the moving average.
E. First apply the moving average calculation to the DataFrame and then filter for rows with values exceeding the threshold, since calculations are performed in order.

Answer: B

Explanation:
The most efficient and correct approach is to use Snowpark's built-in windowing functions. Applying the threshold using 'filter' before the windowing operation reduces the amount of data processed by the window function, improving performance. Using 'Window.partitionBy' and 'Window.orderBy' correctly defines the window based on 'sensor_id' and 'timestamp', respectively. Using 'avg' window function calculates the moving average within the defined window. Options B, C, and D are less efficient because they involve transferring data to the client side (Pandas) or using UDFs, which can introduce overhead. Option E reverses the correct process.

NEW QUESTION # 214
A data engineering team wants to deploy a Snowpark Python stored procedure that aggregates sales data from a table 'SALES DATA and writes the results to a table 'AGGREGATED SALES. The stored procedure needs to be executed by various users with different roles. The team wants to ensure that users can only execute the stored procedure and cannot directly access the underlying 'SALES DATA' table. Which approach is most suitable for managing data access and security in this scenario, and what are the implications of using 'EXECUTE AS OWNER vs 'EXECUTE AS CALLER?

A. Create the stored procedure with 'EXECUTE AS OWNER and grant 'USAGE privilege on the database and schema containing the stored procedure to the roles that need to execute it. This hides the underlying table from the caller, and the stored procedure executes with the owner's privileges.
B. Create the stored procedure with 'EXECUTE AS CALLER and grant 'SELECT privilege on the 'SALES DATA' table to all roles that need to execute the stored procedure. This allows the stored procedure to execute with the caller's privileges, and the caller must have the necessary privileges to access the underlying tables.
C. Create the stored procedure with 'EXECUTE AS OWNER , grant 'USAGE on the database and schema. Grant 'EXECUTE TASK' on the stored procedure to the specific roles, while the Owner(Role with Execute Task permission) should have access to SALES DATA table.
D. Create a view on the 'SALES_DATX table that only exposes the necessary columns and grant 'SELECT privilege on the view to the roles that need to execute the stored procedure. Create the stored procedure with EXECUTE AS CALLER to leverage the view's column restrictions.
E. Create the stored procedure with 'EXECUTE AS CALLER and grant 'USAGE on the database and schema. The callers must have access to both the AGGREGATED SALES and SALES DATA tables. The stored procedure will use the caller's privileges for all operations.

Answer: A

Explanation:
'EXECUTE AS OWNER allows the stored procedure to run with the privileges of the stored procedure's owner, effectively hiding the underlying tables from the caller. This is crucial for security. 'EXECUTE AS CALLER requires the caller to have the necessary privileges on the underlying tables, which defeats the purpose of restricting direct access. While creating a view is an option, it adds complexity and may not be necessary if 'EXECUTE AS OWNER is used correctly. Option D mentions 'EXECUTE TASK which isn't the right privilege needed for stored procedures execution. 'EXECUTE AS CALLER in Option E opens up direct access to underlying tables.

NEW QUESTION # 215
You have a Snowpark DataFrame 'sales df with columns 'product_id' (INTEGER), 'sale date' (DATE), and 'sale_amount' (DOUBLE). You need to filter the DataFrame to include only sales that occurred in the year 2023 and where the 'sale_amount' is greater than the average 'sale_amount' for that specific 'product id'. Which of the following Snowpark Python code snippets will correctly achieve this?

Answer: B

Explanation:
Option C is the most efficient and correct. It calculates the average sale amount per product ID using a window function and then filters the DataFrame based on both the year and the comparison with the average. It avoids joins and 'collect()' which can be inefficient. Option A doesn't import Window or properly use avg over a window, causing an error. Option B requires a join, which is less efficient. Option D uses "mean' which is available but 'avg' is preferrable. Option E 'collects data to the client which is also generally avoided in Snowpark.

NEW QUESTION # 216
You have a Snowpark application that utilizes a vectorized Python UDF to perform complex calculations on a large dataset. You notice that the performance is still not optimal. You suspect that the bottleneck might be related to how the data is being partitioned and processed by Snowflake. Which of the following actions, when performed in conjunction with vectorization, would MOST likely improve performance?

A. Broadcast the DataFrame to all compute nodes before applying the UDF.
B. Ensure that the data is pre-sorted according to the primary key of the table before applying the UDF.
C. Convert the DataFrame to a Pandas DataFrame before applying the UDF.
D. Repartition the Snowpark DataFrame using to align the data distribution with the computational needs of the UDF.
E. Increase the number of UDF worker threads within the UDF definition.

Answer: D

Explanation:
Repartitioning the DataFrame using allows you to control how the data is distributed across compute nodes. This can improve performance by ensuring that related data is processed together, reducing data shuffling and improving data locality. Pre- sorting data (A) might help in some cases, but it doesn't guarantee optimal data distribution for parallel processing. Broadcasting the DataFrame (C) is suitable for smaller datasets, not large ones where it can lead to memory issues. Converting the DataFrame to a Pandas DataFrame (D) defeats the purpose of using Snowpark for distributed processing and introduces a single-node bottleneck. There's no direct control over the number of UDF worker threads in Snowflake.

NEW QUESTION # 217
A data engineer has developed a Snowpark Python stored procedure, 'calculate daily summary', that processes sales data and generates a daily summary table. The procedure takes a date string as input and writes the summary to a table named 'DAILY SALES SUMMARY'. The engineer needs to operationalize this stored procedure to run automatically every day at midnight. Which of the following approaches is the MOST efficient and reliable way to schedule and execute this Snowpark stored procedure in Snowflake?

A. Create a Snowflake Task that calls the stored procedure with a cron schedule that triggers the task every day at midnight. Ensure the task has a warehouse associated with it.
B. Develop a Snowpark UDF that wraps the stored procedure call and create a scheduled job in a third-party orchestration tool (e.g., Airflow, Prefect) to call the UDF.
C. Create a Python script outside of Snowflake that uses the Snowflake Connector for Python to connect to Snowflake and execute the stored procedure using and schedule it using a system-level scheduler (e.g., cron or Windows Task Scheduler).
D. Use the Snowflake web interface to manually execute the stored procedure every day at midnight.
E. Use a Snowpipe to trigger the stored procedure when new data arrives in the staging area.

Answer: A

Explanation:
Snowflake Tasks provide a native and reliable way to schedule and execute stored procedures directly within Snowflake. Using a task eliminates the need for external schedulers and dependencies. Option A is manual and not scalable. Option B introduces external dependencies and potential security concerns. Option D adds an unnecessary layer of complexity with the UDF. Option E is triggered by data arrival and not a scheduled timeframe, also not intended for direct execution of stored procedures. Snowflake tasks are designed specifically for this purpose.

NEW QUESTION # 218
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