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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q280-Q285):

NEW QUESTION # 280
You have a Snowpark Python stored procedure that needs to access environment variables stored securely within Snowflake. Which of the following code snippets demonstrates the correct way to retrieve the value of an environment variable named 'API KEY' within your stored procedure?

Answer: C

Explanation:
Snowflake Snowpark sessions use the method to retrieve user-defined session parameters, including those that may be set to environment variables. The method allows access to parameters set at the account, user or session level. The 'os.envirorf (A) will not work in the Snowflake environment. B, D, and E are not valid Snowpark API methods for retrieving parameters.

NEW QUESTION # 281
You have created a Snowpark Python UDF named to apply discounts based on customer purchase history. You now need to modify the UDF to accept an additional parameter for promotional codes. However, direct modification of the code on stage is restricted. How can you alter this UDF using SQL, assuming the existing UDF definition resides in the 'mydb.public' schema?

A. Use ALTER FUNCTION mydb.public.calculate_discount ADD PARAMETER promo_code STRING;' followed by 'ALTER FUNCTION mydb.public.calculate_discount SET BODY = 'new python code'; '
B. Use ALTER FUNCTION mydb.public.calculate_discount MODIFY AS with the new Python code block.
C. Use 'ALTER FUNCTION mydb.public.calculate_discount RENAME TO followed by creating a new UDF with the updated code and original name.
D. Use 'CREATE OR REPLACE FUNCTION mydb.public.calculate_discount(order_total DOUBLE, customer_segment STRING, promo_code STRING) RETURNS DOUBLE LANGUAGE PYTHON ..: with the updated UDF definition.
E. Snowflake does not allow modifying UDFs directly using SQL. You must redeploy the entire Snowpark application.

Answer: D

Explanation:
'CREATE OR REPLACE FUNCTION' is the correct SQL command to modify an existing UDF. It replaces the old definition with the new one, effectively altering the UDF's parameters and code. Renaming (option A) requires creating a new function anyway. 'ALTER FUNCTION MODIFY AS' is invalid syntax (option C). While redeploying is an option, 'CREATE OR REPLACE FUNCTION' is more efficient (option D). There is no ' ALTER FUNCTION ADD PARAMETER or 'ALTER FUNCTION SET BODY (option E) SQL syntax available for UDF modification.

NEW QUESTION # 282
You are developing a Snowpark Python application that reads a large dataset (1 TB) from a Snowflake table 'TRANSACTIONS and performs complex aggregations. The application is experiencing significant performance issues, with query execution taking several hours. You have already verified that the warehouse size is appropriate and caching is enabled. You suspect the issue might be related to data skew and incorrect partitioning. Which of the following strategies would be MOST effective in identifying and mitigating this performance bottleneck?

A. Analyze the 'TRANSACTIONS' table's data distribution using and histograms on the join keys. Based on the analysis, use with the most skewed column to redistribute the data more evenly. Also, consider using bucketing if appropriate.
B. Increase the Snowflake warehouse size to the largest available option (e.g., X6-Large) to provide more resources for query execution, without analyzing data distribution.
C. Implement caching using after reading the data from the 'TRANSACTIONS' table and before performing any aggregations.
D. Use partition_expression=sf.rand())' to randomly repartition the DataFrame into 100 partitions, regardless of the data distribution in the ' TRANSACTIONS table.
E. Use to force a broadcast join, assuming the aggregated data is small enough to fit in memory. Monitor query profiles to confirm the broadcast occurs.

Answer: A

Explanation:
Option C is the most effective. Data skew is a common performance bottleneck. Analyzing the data distribution and using 'repartition' with the skewed column helps redistribute the data evenly across partitions. and histograms assist in identifying skewed columns. Option A might work if aggregation reduces the data size significantly, but it's not guaranteed and could lead to memory issues. Option B might not address the skew effectively if the random partitioning doesn't align with the data distribution. Option D caches the entire DataFrame, which might not fit in memory and doesn't address the skew. Option E is a brute-force approach and doesn't solve the underlying problem of data skew.

NEW QUESTION # 283
A data engineering team has developed a Snowpark Python application to process customer orders, enrich them with external data (e.g., geo location, weather) and update the Customer360 table. The application is deployed to a production environment. The application's latency has significantly increased over the last week. Your investigation reveals that the Snowflake warehouse used by the application is constantly switching between the 'Scaling Up' and 'Scaling Down' states. The team has set the Auto Suspend time to 5 minutes and Auto Resume to True. Assuming that the team hasn't changed the code, the external API or any parameter related to data ingestion, which combination of the following actions would MOST likely fix the warehouse instability issue and improve the performance of this Snowpark application in production without substantial cost increases?

A. Increase the Auto Suspend value from 5 minutes to 30 minutes. This will ensure that the warehouse remains active for a longer period, preventing frequent auto- suspends and subsequent resume operations.
B. Increase the MIN_CLUSTER_COUNT of the warehouse. This pre-warms clusters and helps the warehouse to quickly adjust to workload changes.
C. Reduce the MAX CLUSTER COIJNT to limit the potential peak capacity of the warehouse, preventing excessive resource allocation.
D. Change the scaling policy of the warehouse to 'ECONOMY', prioritizing cost efficiency over performance responsiveness.
E. Implement workload management and classification to ensure the Customer360 updates are prioritized over less important tasks and assigned to a dedicated resource pool.

Answer: A,B

Explanation:
The 'Scaling Up' and 'Scaling Down' thrashing is likely caused by the warehouse suspending too quickly, leading to constant restarts as new requests arrive. Increasing the Auto Suspend time (Option A) prevents this frequent cycling. Increasing the MIN CLUSTER COUNT (Option B) makes more resources readily available, helping the warehouse respond faster to spikes in demand and reducing the need for scaling up. Workload management (Option C) is a good practice but may not directly address the root cause of the instability. Reducing MAX_CLUSTER_COUNT (Option D) could worsen the problem by limiting the warehouse's ability to handle peak loads. Changing to 'ECONOMY' scaling policy (Option E) would prioritize cost over performance, which is counter to improving performance.

NEW QUESTION # 284
Consider the following Snowpark Python stored procedure:

What steps are necessary to register this Python code as a stored procedure named 'GET ROW COUNT in Snowflake and allow users with the 'ANALYST' role to execute it, assuming the stored procedure will be created with the 'EXECUTE AS OWNER clause, and the table name parameter will be passed dynamically during invocation?

A. 1. Create the stored procedure using 'CREATE OR REPLACE PROCEDURE GET ROW COUNT(VARCHAR) RETURNS VARCHAR LANGUAGE PYTHON IMPORTS-('/path/to/dependencies.zip') EXECUTE AS OWNER;' 2. Grant 'USAGE' privilege on the database and schema containing the stored procedure to the 'ANALYST' role. 3. Grant 'EXECUTE PROCEDURE privilege on the 'GET ROW COUNT' stored procedure to the 'ANALYST role.
B. 1. Create the stored procedure using 'CREATE OR REPLACE PROCEDURE GET ROW COUNT(VARCHAR) RETURNS VARCHAR LANGUAGE PYTHON EXECUTE AS OWNER AS $$ import snowflake.snowpark as snowpark def snowpark.Session, table_name: str) str: df = session.table(table_name) count = df.count() return f" Table {table_name} has {count} rows." $$; 2. Grant 'USAGE privilege on the database and schema containing the stored procedure to the 'ANALYST role. 3. Grant 'EXECUTE PROCEDURE privilege on the 'GET ROW COUNT stored procedure to the 'ANALYST role.
C. 1. create the stored procedure using 'CREATE OR REPLACE PROCEDURE RETURNS VARCHAR LANGUAGE PYTHON IMPORTS-('/path/to/dependencies.zip') EXECUTE AS OWNER;' 2. Grant 'USAGE-' privilege on the database and schema containing the stored procedure to the 'ANALYST role. 3. Grant ' EXECUTE privilege on the stored procedure to the 'ANALYST role.
D. 1. create the stored procedure using 'CREATE OR REPLACE PROCEDURE RETURNS VARCHAR LANGUAGE PYTHON RUNTIME_VERSlON=3.8 HANDLER='my_sproc' EXECUTE AS OWNER$ 2. Grant 'USAGE privilege on the database and schema containing the stored procedure to the 'ANALYST role. 3. The 'ANALYST' role does not need any additional privilege if EXECUTE AS OWNER is set, as it's using the owner's access rights.
E. 1. Create the stored procedure using 'CREATE OR REPLACE PROCEDURE GET ROW COUNT(VARCHAR) RETURNS VARCHAR LANGUAGE PYTHON EXECUTE AS CALLER;' 2. Grant 'USAGE privilege on the database and schema containing the stored procedure to the 'ANALYST role. 3. Grant 'SELECT' privilege on all possible tables referenced by 'table_name' to the 'ANALYST' role.

Answer: A

Explanation:
Option A correctly outlines the process. First, the stored procedure is created with the correct syntax, including specifying the runtime version and handler, and using 'EXECUTE AS OWNER. Second, 'USAGE privilege is granted on the database and schema. Third, EXECUTE PROCEDURE' privilege must be explicitly granted to the 'ANALYST role. Option B attempts to embed the python code inline but doesn't include IMPORTS section if needed and its more common now to upload the Python code using create or replace procedure syntax. Option C is incorrect because you need to grant ' EXECUTE PROCEDURE privilege to the role in addition to Usage. Option D uses 'EXECUTE AS CALLER , which is not what the question specifies and requires granting 'SELECT privileges on the underlying tables, defeating the purpose of owner rights. 'EXECUTE' is not a valid privilege to grant on stored procedures in Snowflake (Option E), it has to be 'EXECUTE PROCEDURE.

NEW QUESTION # 285
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