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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are designing a Snowpark application to process streaming data ingested into Snowflake using Snowpipe. The application needs to apply a complex set of transformations and aggregations to the incoming data in real-time. Which of the following approaches would be MOST suitable for this scenario, leveraging the strengths of Snowpark architecture?

A) Define a Snowpark Stored Procedure ('sproc') that is triggered automatically by a Snowflake Task whenever new data arrives via Snowpipe. The stored procedure performs the transformations and aggregations and stores the results in a new table.
B) Continuously query the incoming data from Snowpipe using a Snowpark DataFrame and perform the transformations and aggregations on the client-side in a loop.
C) Utilize Snowflake's Streams and Tasks feature and define views with complex SQL transformations that leverages Snowpipe.
D) Create a Snowpark DataFrame that represents the incoming data and use the 'write_pandaS function to write the transformed data to a separate Snowflake table after each micro-batch.
E) Use Snowpark to define a series of chained UDFs that perform the transformations and aggregations directly within the Snowpipe pipeline.

2. You have a Snowpark DataFrame named 'orders_df with columns 'order_id', 'customer_id', 'order_date', and 'order_total'. You need to perform the following data enrichment steps using Snowpark for Python: 1. Calculate the 'year' from the 'order_date' column. 2. Calculate the 'discounted_total' by applying a discount of 10% if the 'order_total' is greater than $100, otherwise, no discount. 3. Create a new column 'customer_tier' based on the total spend per customer for each year. Customers with total spend greater than $1000 are 'Gold', between $500 and $1000 are 'Silver', and below $500 are 'Bronze'. Which of the following code snippets correctly implements these data enrichment steps using Snowpark (Assume the existence of a customer total spend df DataFrame).

A)

B)

C)

D)

E)

3. You are developing a Snowpark application that processes large datasets stored in Snowflake. You need to implement custom User-Defined Functions (UDFs) written in Java. The UDF requires specific third-party libraries that are not available in the default Snowflake Java environment. What steps are necessary to package and deploy these UDFs correctly?

A) Compile the Java code into a native library (e.g., a .so file), upload it to a Snowflake stage, and use the 'CREATE EXTERNAL FUNCTIONS command to invoke it.
B) Package the Java code and dependencies into a JAR file and upload it to a Snowflake stage. Use the 'CREATE FUNCTION' command with the 'IMPORTS' clause to reference the JAR file.
C) Utilize the Snowpark API to create a 'snowpark.functions.udf object, including the Java code and dependencies. The Snowpark runtime will handle the deployment.
D) Use the 'CREATE OR REPLACE JAVA FUNCTION' command directly in Snowsight and paste the Java code along with the dependencies' contents into the function body.
E) Create a separate Python UDF that imports the Java code using Jpype and then register the Python UDF with Snowflake

4. You're working with a Snowpark DataFrame named 'sales_df' that contains sales transaction data'. You need to create a new DataFrame that includes only the rows where the 'order_date' is within the last 30 days. The 'order_date' column is currently stored as a string in 'YYYY-MM-DD' format. You want to create a schema and apply the schema to the dataframe. Choose the correct options that defines the schema in below code snippets:

A)

B)

C)

D)

E)

5. You have a Snowpark DataFrame 'product_df and you want to create a view named 'active_products' that only includes products with a 'status' column value equal to 'ACTIVE'. This view should be accessible to all users in the 'PUBLIC' role. Which of the following code snippets correctly creates the view and grants the necessary privileges?

A)

B)

C)

D)

E)

Solutions: