Snowflake SPS-C01 : Snowflake Certified SnowPro Specialty - Snowpark

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. A data engineering team is building a Snowpark pipeline to process IoT sensor data'. They want to create a UDF that uses a 3rd-party Python library (not available in Snowflake's Anaconda channel) to analyze the sensor readings. The UDF needs to be efficiently deployed and managed within Snowflake. Which of the following approaches represents the MOST robust and scalable way to register and deploy this UDF using Snowpark?

A) Create a Docker container with the Python library, push it to Snowflake Container Services, and call this container from the UDF.
B) Use 'functions.udf and directly embed the package code within the UDF definition. This approach handles package management automatically.
C) Use 'session.add_packages' to add the specific Python package directly from the Snowflake Anaconda channel (even if the required version isn't available) and then use 'session.udf.register' for the UDF definition.
D) Use 'session.udf.register' and directly include the library code as a string within the UDF definition. This avoids external dependencies.
E) Create a virtual environment with the necessary Python library, zip it, upload the zip file to a Snowflake stage, and use to register the UDF. Reference the stage location and virtual environment in the register call.

2. You are developing a Snowpark application in Python to perform sentiment analysis on customer reviews stored in a Snowflake table named 'CUSTOMER_REVIEWS. The table has columns 'REVIEW ONT), 'REVIEW TEXT (VARCHAR), and 'SENTIMENT SCORE (FLOAT). You want to define a UDF using Snowpark that leverages a pre-trained sentiment analysis model from the 'nltk' library (already uploaded to a stage). The UDF should take 'REVIEW TEXT' as input and return the sentiment score. Which of the following code snippets will correctly define and register the UDF, ensuring it's accessible for use in Snowpark DataFrames, taking into account potential serialization issues with 'nltk' models?

A)

B)

C)

D)

E)

3. You're tasked with loading data representing transactions from a legacy system into Snowflake using Snowpark. The legacy system exports the transaction data as a Python list of tuples, where each tuple contains transaction ID (integer), transaction amount (float), and transaction date (string in 'YYYY-MM-DD' format). The scale of data can be very high and need optimized way to load the data'. Your goal is to create a Snowpark DataFrame from this list of tuples, ensuring the date column is correctly interpreted as a Snowflake Date type. Which of the following approaches would be the most efficient and correct, minimizing data conversion overhead and maximizing Snowpark's capabilities?

A) Create a Snowpark DataFrame directly from the list of tuples using 'session.createDataFrame(data)' , relying on automatic schema inference. Then, use function to cast the date column to a DateType.
B) Define a Snowpark schema using 'StructType' and 'StructField' , explicitly setting the data type of the date column to 'DateType'. Then, create the Snowpark DataFrame using 'session.createDataFrame(data,
C) Convert the list of tuples to a Pandas DataFrame, explicitly specifying the column names and data types (including 'pd.datetime64[ns]' for the date column). Then, create a Snowpark DataFrame from the Pandas DataFrame using 'session.createDataFrame(pandas_df)'.
D) Create a Snowpark DataFrame directly from the list of tuples using 'session.createDataFrame(datay , relying on automatic schema inference. No need to explicitly convert to 'DateType' as Snowflake will take care of implicit conversion.
E) Create a list of dictionaries from the list of tuples with correct column names, and define a Snowpark schema using 'StructType' and 'StructField', explicitly setting the data type of the date column to 'DateType'. Then, create the Snowpark DataFrame using 'session.createDataFrame(data, schema=schema)'.

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 Python stored procedure that needs to interact with an external REST API. The API requires authentication using an API key, which you want to store securely and access within the stored procedure. What is the MOST secure and recommended way to store and retrieve the API key within the stored procedure?

A) Store the API key as a constant string within the stored procedure's code.
B) Store the API Key as a comment in the Store procedure code, and retrieve it using REGEX
C) Store the API key in a Snowflake Secret and access it using the 'secrets' module within the stored procedure.
D) Store the API key in a Snowflake table and query it within the stored procedure.
E) Store the API key as an environment variable within the Snowflake warehouse configuration.

Solutions: