Snowflake Certified SnowPro Specialty - Snowpark : SPS-C01

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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:

A)

B) This code will fail because is not a valid method for Snowpark DataFrames.
C) This code will ovemrite the table if it already exists.
D) The 'result_df DataFrame will be persisted to the 'AGGREGATED SALES table in the default schema of the user running the code.
E) The code will fail because there is no call to or on the 'result_df dataframe and Snowflake performs lazy evaluation.

2. You have a Snowpark DataFrame containing semi-structured data in a column named 'payload'. The 'payload' column contains JSON objects, and some of these objects contain nested arrays. You need to flatten all arrays, regardless of their level of nesting, and extract specific fields from the flattened data'. What is the MOST efficient approach using Snowpark to achieve this while minimizing the amount of code?

A) Use recursive UDFs to traverse and flatten the JSON structure, then create a new DataFrame from the flattened data.
B) Convert the DataFrame to an RDD, then use the RDD's 'flatMap' function to flatten the nested arrays before converting back to a DataFrame.
C) Iteratively apply the 'explode' function to each array field within the 'payload' column, manually identifying and flattening each level of nesting.
D) Use a single ' SELECT statement with multiple 'LATERAL FLATTEN' calls (using SQL syntax within 'session.sql') to flatten all nested arrays simultaneously.
E) Create a stored procedure in Snowflake that recursively flattens the JSON, then call this stored procedure from Snowpark to transform the DataFrame.

3. You are using Snowpark for Python to process a large dataset of website clickstream data'. The dataset contains columns such as 'session_id', 'user_id', 'timestamp', 'page_url', and 'event_type' (e.g., 'click', 'pageview', 'purchase'). You want to identify fraudulent user sessions based on the following criteria: A user session is considered fraudulent if it contains more than 100 clicks within a I-minute window. A user session is considered fraudulent if it contains more than 5 purchase events within a 5-minute window. Which of the following code snippets demonstrates the most efficient way to identify fraudulent sessions using Snowpark for Python? Select two that apply.

A)

B)

C)

D)

E)

4. You have a Snowflake table 'user_profiles' with a VARIANT column 'profile_data'. This column contains JSON objects, and one of the fields within these objects is an array called 'interests'. The 'interests' array contains JSON objects, each with 'name' and 'category' fields. You need to use Snowpark to flatten the 'interests' array and extract the 'name' and 'category' for all user profiles, but only for profiles where the user's 'status' is 'active'. You want to write this in the most efficient way possible. Which of the following code snippets will achieve this?

A)

B)

C)

D)

E)

5. You are working with a Snowpark DataFrame containing website traffic data'. The DataFrame has columns like 'date' , 'page_url', and 'visit_count'. You need to calculate the cumulative sum of visit counts for each 'page_url' over time (i.e., ordered by 'date'). However, you only want to consider data from the last 30 days for each calculation. Which of the following Snowpark code snippets will correctly achieve this using window functions with a frame specification?

A)

B)

C)

D)

E)

Solutions: