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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. 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) 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.
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) Implement caching for the UDF's results to avoid recomputing the same values multiple times.
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.

2. A data scientist has developed a complex machine learning model in Python that needs to be operationalized within a Snowpark pipeline. This model depends on several custom Python packages not available in Snowflake's default environment. The data scientist wants to define a UDTF to apply this model to incoming data'. Which of the following steps are NECESSARY to successfully deploy and execute this UDTF in Snowflake? (Select three)

A) Package the virtual environment as a ZIP file.
B) Create a virtual environment and install all the required Python packages.
C) Upload the ZIP file to a Snowflake stage.
D) Specify the stage location in the 'imports' clause of the 'CREATE FUNCTION' statement when defining the UDTF.
E) Include only custom packages into the ZIP file and exclude common python library packages, as snowpark is pre-installed.

3. You have a Snowpark DataFrame with columns 'sale_date', 'product_id', and 'revenue'. You need to calculate the cumulative revenue for each product over time. Which of the following approaches will accomplish this in Snowpark using window functions?

A)

B)

C)

D)

E)

4. You have a Snowpark DataFrame 'df with a column 'transaction_date' of STRING type, containing dates in 'YYYY-MM-DD' format, and a 'transaction_amount' column of STRING type, containing currency values like '$1 ,234.56'. You need to create a new DataFrame 'df_transformed' that contains the 'transaction_date' as a DATE type and 'transaction_amount' as a DOUBLE type. What Snowpark Python code snippet will accomplish this transformation most efficiently and handle potential errors during the cast?

A) ...python df_transformed = df.with_column('transaction_date', to_date(col('transaction_date'))).with_column('transaction_amount', to_double(col('transaction_amount').replace('$',").replace(',',")))
B) ...python df_transformed = df.with_column('transaction_date', to_date(col('transaction_date') YYYY-MM-DD')).with_column('transaction_amount', col('transaction_amount').cast(DoubleType()))
C) ...python df_transformed = df.select(to_date(col('transaction_date')).alias('transaction_date'), to_double(col('transaction_amount')).alias('transaction_amount'))
D) ...python from snowflake.snowpark.functions import try_to_date, try_to_double df_transformed = df.with_column('transaction_date', try_to_date(col('transaction_date'))).with_column('transaction_amount', try_to_double(regexp_replace(col('transaction_amount'), '[$,]', ")))
E) ...python import snowflake.snowpark.functions as sf df_transformed = df.with_column('transaction_date', sf.to_date(sf.col('transaction_date'), format='YYYY- MM-DD')).with_column('transaction_amount', sf.to_double(sf.regexp_replace(sf.col('transaction_amount'), '[$,]', ")

5. You are tasked with automating the creation of Snowpark sessions using key pair authentication for multiple users. You have a function that retrieves connection parameters (account, user, private key, etc.) for each user from a secure configuration file. The private keys are stored in PEM format. However, some users' private keys are password-protected. Which of the following approaches ensures the secure and correct establishment of Snowpark sessions for all users, including those with password-protected private keys? Assume get_user config(username)' retrieves the user's configuration, including the private key and password (if any).

A) Attempt to establish a session without a password. If it fails, prompt the user for the password and retry the session creation using the provided password. Store the password temporarily in memory.
B) Store the password for each user's private key in a separate, encrypted file and retrieve it during session creation.
C)

D)

E) Require all users to remove the password protection from their private keys to simplify the session creation process.

Solutions: