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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are tasked with building a Snowpark application to perform sentiment analysis on customer reviews stored in a Snowflake table named 'CUSTOMER REVIEWS'. The application should be deployed as a UDF. The sentiment analysis is performed by a third-party Python library, 'sentiment_analyzer'. Due to security constraints, direct internet access is prohibited from within the Snowflake environment. What steps are necessary to ensure the 'sentiment_analyzer' library can be used by your Snowpark UDF?

A) Request Snowflake support to whitelist the 'sentiment_analyzer' library for direct download during UDF execution.
B) Package the 'sentiment_analyzer' library into a JAR file and upload it to a Snowflake stage, then specify the JAR in the 'imports' parameter of the UDF creation statement.
C) Package the 'sentiment_analyzer' library into a ZIP file and upload it to a Snowflake stage, then specify the ZIP in the 'imports' parameter of the UDF creation statement.
D) Install the 'sentiment_analyzer' library using 'conda install' directly within the Snowpark session before creating the UDF.
E) Use the 'packages' parameter in the UDF creation statement to specify the 'sentiment_analyzer' library from Anaconda.

2. You have two Snowpark DataFrames, 'dfl' and 'df2 , both containing customer data, but with slightly different schemas. 'dfl' has columns 'customer_id', 'name', and 'email'. 'df2' has columns 'id', 'customer name', and 'email_address'. You want to perform a set- based operation to find all unique customer IDs present in 'dfl but NOT in 'df2' , considering that 'customer_id' in 'dfl corresponds to 'id' in 'df2. Which of the following code snippets will achieve this, ensuring that column names are correctly aligned before the operation?

A)

B)

C)

D)

E)

3. 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)

C)

D)

E) All of the above options will work.

4. 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)

5. You are profiling a Snowpark application that uses a combination of SQL queries and Python UDFs. You observe that a particular stage involving a UDF is taking significantly longer than expected. You suspect that the UDF's performance is the bottleneck. Which of the following steps would be the MOST comprehensive approach to diagnose and address the performance issue?

A) Implement caching for the UDF's results to avoid recomputing the same values multiple times.
B) Use Snowflake's query profile to examine the execution plan and identify the UDF-related stages with the highest execution time. Then, analyze the UDF's code for inefficiencies, such as unnecessary loops or complex calculations.
C) Replace the Python UDF with an equivalent SQL query using Snowflake's built-in functions. If the SQL query performs better, the Python UDF was the bottleneck.
D) Convert the scalar UDF to a vectorized UDF, even without fully understanding the source of the performance bottleneck.
E) Increase the warehouse size and re-run the application. If the execution time improves significantly, the issue was resource contention.

Solutions: