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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are tasked with optimizing a Snowpark application that performs sentiment analysis on customer reviews using a Python UDE The UDF uses a large pre-trained natural language processing (NLP) model stored in a file named 'sentiment_model.pkl'. The current implementation loads the model from the stage for each row of data processed, which is impacting performance. How can you optimize the application to load the model only once per worker process?

A) Define 'sentiment_model.pkl' as a parameter during UDF definition to load only once per worker process and send it to the UDF.
B) Use to import 'sentiment_model.pkl'. Use the decorator from the 'functools' module to cache the model loading function, initializing the model outside of the UDF definition.
C) Implement a custom initialization function that loads the model and is called only once per worker process. Utilize the to retrieve and cache model during session initialization. Upload 'sentiment_model.pkl' to a stage and reference it in the 'imports' clause.
D) Use a global variable to store the loaded model. Load the model from the stage into the global variable only if it is currently None. Upload 'sentiment_model.pkl' to a stage and reference it in the 'imports' clause.
E) Use the decorator from the 'functools' module to cache the model loading function. Upload 'sentiment_model.pkl' to a stage and reference it in the 'imports' clause.

2. You have a Python function, 'calculate metrics(df: snowpark.DataFrame, metric name: str) -> snowpark.DataFrame', that calculates various metrics on a Snowpark DataFrame. You want to deploy this function as a stored procedure in Snowflake. You need to ensure that the stored procedure has appropriate permissions to read data from a table named 'customer data' and write results to a table named 'metrics_table'. Which of the following steps are necessary to achieve this, assuming you are using the 'session.sproc.register' method?

A) Specify the 'imports' argument in 'session.sproc.register' with the list of packages which are needed to run 'calculate_metrics' function.
B) Grant the 'SELECT privilege on the 'customer_data' table and the 'INSERT privilege on the 'metrics_table' table to the role executing the stored procedure.
C) Grant the 'USAGE privilege on the database and schema containing the 'customer_data' and 'metrics_table' tables to the role executing the stored procedure.
D) Specify the 'packages' argument in 'session.sproc.register' to include any Python dependencies required by the 'calculate_metrics' function.
E) When registering the stored procedure using 'session.sproc.register' , specify the argument and provide a 'replace=True' if necessary. This will allow you to assign ownership of the stored procedure to a role with the necessary privileges.

3. Consider the following Snowpark Python code snippet that retrieves data and calculates aggregate values, however, the application performance is slow when you are fetching dataframe, given the 'block' parameter controls the synchronous/asynchronous behavior of the 'collect()' method. Choose ALL the statements about "session.create_dataframe([rowl ,row2],schema)' that are correct:

A) Consider replacing the call of 'collect()' with and converting Snowpark dataframe to Pandas dataframe. The dataframe operations could be efficient.
B) The 'block' parameter has no influence on performance when creating dataframes, and hence the performance is mainly based on query optimization and ware house size.
C) The method, when executed with default 'block=True' , will wait for the result to be available before proceeding to the other statements in the code and hence performance is low. However, makes it asynchronous.
D) Increasing warehouse size of Snowflake will increase performance irrespective of the fact whether block is True or False.
E) Snowflake warehouse size has no influence on dataframe creation, so if the dataframe is large and 'block=True' it will block the performance.

4. You are building a Snowpark application using the 'snowflake-cli' to manage Snowflake connections. You have configured multiple connection profiles using 'snowflake connection add'. Which of the following Python code snippets demonstrates the most efficient and idiomatic way to create a Snowpark session using a specific connection profile named 'my_profile' defined in your 'snowflake-cli' configuration? Assume snowflake-cli is correctly configured and authenticated.

A)

B)

C)

D)

E)

5. Consider the following Snowpark code snippet that aims to calculate the rank of each employee based on their salary within their respective department. What are potential issues with this code, and how can you improve it? (Select all that apply.)

A) The code is correct and will produce the desired output without any issues.
B) It is missing the 'col' function call in the orderBy clause. It should be 'orderBy(sf.col("salary").desc())'.
C) The 'rank()' function will produce dense ranks, which might be undesirable if there are ties in salary. Use for contiguous ranks instead.
D) There may be performance issues if the employee table is very large. Consider adding a filter to the DataFrame before applying the window function.
E) The code does not handle potential null values in the salary column. Consider using or before calculating the rank.

Solutions: