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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider the following Snowpark Python code snippet for creating a stored procedure:

What is the PRIMARY reason for explicitly defining 'input_types' and during the stored procedure registration?

A) To allow Snowflake to automatically generate documentation for the stored procedure's input and output types.
B) To allow Snowsight to correctly display the stored procedure's metadata, making it easier for users to understand its functionality.
C) To improve the performance of the stored procedure by enabling compile-time optimizations.
D) To enable the stored procedure to be called from other programming languages besides Python.
E) To ensure data type safety and schema validation during deployment and execution, preventing unexpected runtime errors due to type mismatches between the stored procedure and the calling environment.

2. When creating UDFs/UDTFs in Snowpark Python, what are the advantages of explicitly specifying data types (either via Python type hints or the registration API) compared to relying on implicit type inference?

A) Enhanced code readability and maintainability, making it easier to understand the expected data types.
B) Early detection of type-related errors during development, preventing runtime failures.
C) Reduced deployment time.
D) Automatic data type conversion by Snowflake, eliminating the need for explicit casting within the UDF/UDTF.
E) Improved performance due to reduced overhead in data type resolution at runtime.

3. You have a Snowpark DataFrame named 'products' with columns 'product_id' (INT), 'product_name' (STRING), and 'price' (DOUBLE). You want to apply a transformation to calculate a 'discounted_price' column, which is the 'price' reduced by 10% if the price is greater than $100.00. Which of the following code snippets is the most efficient way to achieve this using Snowpark Python?

A)

B)

C)

D)

E)

4. You are tasked with building a data pipeline that uses Snowpark to process customer data in a table called 'CUSTOMERS'. The table contains sensitive information, and you need to ensure that any Personally Identifiable Information (PII) is removed from the table after 30 days. You decide to implement a mechanism to automatically delete records older than 30 days. Which of the following approaches would you consider when designing the deletion process with consideration of cost, performance and security?

A) Schedule a daily Snowpark script to read the entire 'CUSTOMERS' table, filter out records older than 30 days based on the 'created_at' column, and then use DataFrame.delete()' to remove those records from the table.
B) Implement a dynamic data masking policy on the PII columns instead of deleting records. Set the policy to mask data older than 30 days.
C) Create a Snowpark Stored Procedure that executes the 'DataFrame.delete()' operation and schedule this stored procedure to run daily using a Snowflake Task.
D) Implement a Snowflake Stream on the 'CUSTOMERS' table. Create another table 'CUSTOMERS ARCHIVE' with the same schema and stream data older than 30 days using 'DataFrame.copy_into_table', then truncate data from original CUSTOMERS table
E) Implement a Snowflake Task that executes a SQL 'DELETE statement directly against the 'CUSTOMERS' table, filtering records based on the 'created_at' column.

5. 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) Create a Snowpark Dataframe that reads from 'CUSTOMER_REVIEWS , convert it to pandas dataframe and call analyze_sentiment function on pandas dataframe.
B) Persist the Snowpark session with UDF definition using pickle and call it from another Snowpark session.
C) Register the 'analyze_sentiment' UDF as a persistent UDF in Snowflake and then call it from a SQL query that reads from 'CUSTOMER REVIEWS.
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.

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