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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are developing a Snowpark Python application that performs advanced machine learning model training on a large dataset stored in Snowflake. You observe that the application is memory-intensive, causing frequent spilling to disk and slowing down the training process.
Which of the following strategies, when implemented in conjunction, is MOST likely to improve the performance of your Snowpark application in this memory-constrained scenario?

A) Enable caching for intermediate results and switch to a Snowpark-optimized warehouse with a smaller size to force early spilling to disk.
B) Reduce the batch size for data loading, utilize Snowpark's optimized data types for columns with small value ranges, and use memory profiling tools to identify memory leaks in your Python code and optimize the application.
C) Switch to a larger Snowflake warehouse size and increase the parameter to ' True'.
D) Increase the size of the Snowpark-optimized warehouse with 'MEMORY OPTIMIZED server type, use appropriate data types to minimize memory footprint, and optimize UDFs to minimize intermediate data creation.
E) Increase the 'MAX CONCURRENCY LEVEL' parameter at the session level and rewrite the model training code in SQL using stored procedures.

2. You are developing a Snowpark application that uses a Python UDF to perform geocoding operations. This UDF relies on a third-party geocoding library and a large dataset of geographical data stored in a file named 'geodata.db'. The UDF needs to be operationalized with minimal latency. Which of the following strategies will result in the FASTEST execution of the UDF and optimal resource utilization?

A) Use an external function that calls a geocoding service over the internet. Store 'geodata.db' in an S3 bucket and access it from the external function. Call the external service whenever it requires it.
B) Package the geocoding library and 'geodata.db' file into a ZIP file. Upload the ZIP file to a Snowflake stage and reference it using 'imports' in the UDF definition. Ensure 'geodata.db' is loaded only once into memory per worker process using global variable and proper caching for subsequent UDF invocations. Use a virtual environment to manage package dependencies.
C) Package the geocoding library and 'geodata.db' file into a ZIP file. Upload the ZIP file to a Snowflake stage and reference it using 'imports' in the UDF definition. Use a virtual environment to manage package dependencies.
D) Create a custom Anaconda channel containing the geocoding library and 'geodata.db'. Configure the Snowflake account to use this channel. No need to use virtual environment.
E) Create a Java UDF that performs the geocoding using a Java geocoding library. Upload the JAR file and 'geodata.db' to a stage and reference them using the 'imports' clause. Java UDFs always perform faster than Python UDFs.

3. You have a Snowpark DataFrame named 'transactions' containing transaction data'. You need to create a UDTF using Python to categorize transactions into 'High Value', 'Medium Value', and 'Low Value' based on the transaction amount and the customer's region. The categorization logic requires access to a dynamically updated lookup table stored in a Snowflake stage. Which approach would be MOST efficient and scalable, minimizing data transfer and maximizing Snowpark's vectorized operations?

A) Use a UDTF with the parameter, reading the lookup table directly into the UDTF using a Snowpark DataFrame and joining it with each batch of the 'transactions DataFrame. Materialize the result to a temporary table.
B) Create a vectorized UDTF that loads the lookup table into memory during the first call, and then caches it for subsequent calls. Implement a refresh mechanism using a Snowflake external function triggered by stage updates.
C) Define a scalar UDF that queries the lookup table directly from Snowflake using a Snowflake connector. This avoids data transfer to the UDF but introduces external dependency and connection management overhead for each row.
D) Use a scalar UDF, reading the lookup table from the stage for each transaction. This ensures data consistency but may incur significant overhead for each row processed.
E) Create a vectorized UDF. Load the lookup table from the stage into the UDF's environment once during initialization. Then, process transactions in batches using pandas DataFrames within the UDF.

4. You are developing a Snowpark application to process sales data. The application uses a UDF that calls an external Python library with a large memory footprint. After deploying the application, you observe that the Snowflake warehouse frequently runs out of memory, causing the application to fail. Which of the following strategies would be MOST effective in mitigating this issue, while minimizing cost and maintaining performance? Assume the data volume is relatively large and the UDF is computationally intensive.

A) Rewrite the UDF in Java using Snowpark API, which generally has a smaller memory footprint than Python. Deploy the UDF with the same warehouse size.
B) Modify the UDF to process data in smaller batches using a generator pattern, reducing memory consumption at any given time. Deploy the UDF with the same warehouse size.
C) Implement a caching mechanism within the UDF to store intermediate results and reduce the number of calls to the external library. Deploy the UDF with the same warehouse size.
D) Implement the Python UDF as a Snowpark Stored Procedure. Deploy the UDF with the same warehouse size.
E) Increase the warehouse size to the largest available option. This will provide more memory to the UDE

5. You are working with Snowpark DataFrames representing sales transactions. The 'transactions df DataFrame contains recent transactions, while the 'sales_table' in Snowflake holds the historical sales data'. You need to merge the new transactions into the 'sales table', but you want to track which rows were inserted, updated, or left unchanged by the 'merge' operation. How can you capture this information using Snowpark and persist it to a separate table?

A) The merge operation automatically creates a system table or view that logs the details of each row that was inserted, updated, or left unchanged.
B) After the merge, query the 'sales_table' and compare it to a copy of the 'sales_table' taken before the merge. Identify inserted rows as those present in the new version but not in the old, and updated rows as those with different values in specific columns between the two versions. This strategy should be implemented with the help of external functions and UDFs, as it is an expensive operation and should not be computed with normal SQL.
C) It's not possible to capture the merge operation details (inserted/updated/unchanged rows) directly using Snowpark alone. You would need to implement custom logic outside of Snowpark to compare the data before and after the merge.
D) You can combine the 'returning' clause along with a stored procedure to retrieve the inserted/updated rows with their status and then use the stored procedure to write to a different tracking table using insert statement.
E) You can use the 'returning' clause within the merge statement to retrieve the impacted rows and their change status (INSERTED, UPDATED) and then write this data to a tracking table using 'write.saveAsTable' .

Solutions: