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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are working with a Snowpark DataFrame that contains product information including 'product_name' and 'description'. You need to create a new column named 'search_terms' that contains the first three words from the 'description' column, converted to lowercase. If the description has fewer than three words, the 'search_terms' column should contain all the words available. The words should be separated by a space. What is the MOST efficient way to achieve this using Snowpark?

A)

B)

C)

D)

E)

2. A data engineer wants to create a Snowpark session using environment variables defined in a .env' file. The file contains the following: SNOWFLAKE ACCOUNT=myaccount.snowflakecomputing.com SNOWFLAKE USER=snowpark_user SNOWFLAKE SNOWFLAKE DATABASE=mydb SNOWFLAKE SCHEMA=myschema SNOWFLAKE WAREHOUSE=mywarehouse Which code snippet correctly establishes a Snowpark session using these environment variables?

A)

B)

C)

D)

E)

3. 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 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.
B) First, collect the entire DataFrame into a Pandas DataFrame, then use Pandas windowing functions 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) 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.
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.

4. You have a Snowpark DataFrame 'employees' with columns 'employee_id' (INT), 'name' (STRING), 'department' (STRING), and 'salary' (DOUBLE). You want to create a new DataFrame that contains the top 3 highest-paid employees within each department. Which of the following approaches is the MOST efficient and correct way to achieve this using Snowpark Python?

A)

B)

C)

D)

E)

5. You are developing a Snowpark application to process sensor data'. You need to define a UDF that converts temperature readings from Celsius to Fahrenheit. However, the conversion formula is computationally intensive and requires access to a pre-trained machine learning model stored as a resource in a stage. Given the following considerations, what is the most efficient and correct way to define this UDF? The model file is named 'temperature_model.pkl'.

A)

B) All of the above options will work.
C)

D)

E)

Solutions: