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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:
1. 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 MIN_CLUSTER_COUNT of the warehouse. This pre-warms clusters and helps the warehouse to quickly adjust to workload changes.
B) Reduce the MAX CLUSTER COIJNT to limit the potential peak capacity of the warehouse, preventing excessive resource allocation.
C) 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.
D) Implement workload management and classification to ensure the Customer360 updates are prioritized over less important tasks and assigned to a dedicated resource pool.
E) Change the scaling policy of the warehouse to 'ECONOMY', prioritizing cost efficiency over performance responsiveness.
2. You have a DataFrame 'df in Snowpark containing order data, including a VARIANT column named 'order details'. The 'order detailS column contains a nested JSON structure with fields like 'customer id' (always a string), 'items' (an array of item IDs, sometimes numbers, sometimes strings), and 'total amount' (inconsistent data type - sometimes string, sometimes number). You need to perform the following transformations: 1. Extract the "customer_id' and cast it to an integer. 2. Extract the first item ID from the 'items' array, attempting to cast it to an integer, handling potential casting errors. 3. Extract the 'total_amount' and cast it to a decimal (precision 10, scale 2), handling potential casting errors. Which of the following code snippets correctly implements these transformations using Snowpark?
A)
B)
C)
D)
E) 
3. You have a Snowpark DataFrame containing sales data with columns 'region' , and 'sales_amount'. You need to calculate the total sales amount for each region and then filter the results to only include regions where the total sales amount is greater than 10000. Which of the following Snowpark code snippets correctly implements this logic?
A)
B)
C)
D)
E) 
4. A Snowpark application needs to authenticate to Snowflake using OAuth. The application is running on an Azure Function and uses a client ID, client secret, and refresh token obtained previously. Which of the following connection parameter dictionaries is correctly configured for OAuth authentication?
A)
B)
C)
D)
E) 
5. You are tasked with creating a Snowpark Python stored procedure that reads data from a Snowflake table, performs a complex data transformation using a 3rd party Python library (e.g., pandas, scikit-learn), and writes the transformed data to another Snowflake table.
The data transformation requires significant memory. You need to register this stored procedure in Snowflake. Which of the following approaches is the MOST appropriate for registering the stored procedure and managing the dependencies?
A) Create a conda environment file ('environment.yml') specifying the dependencies, upload it to a stage, and then use the '@sproc' decorator with the 'packages' argument referencing the conda environment.
B) Use the '@sproc' decorator without specifying any dependencies, assuming that the necessary libraries are pre-installed on the Snowflake worker nodes.
C) Create a Snowflake stage, upload the Python libraries as .zip files to the stage, and specify the stage path in the '@sproc' decorator's 'imports' parameter.
D) Install the required Python libraries directly on the Snowflake compute warehouse using a SQL command.
E) Use the function to add the required Python libraries before registering the stored procedure with the '@sproc' decorator.
Solutions:
| Question # 1 Answer: A,C | Question # 2 Answer: A | Question # 3 Answer: C | Question # 4 Answer: E | Question # 5 Answer: A |



