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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are developing a Snowpark application that processes large volumes of JSON data from an external stage. Initial testing on a MEDIUM warehouse results in significant query queuing. You suspect the issue is CPU bound due to complex JSON parsing and UDF execution within Snowpark. Considering only warehouse sizing options and assuming cost is a secondary concern to performance during peak processing hours, which strategy is MOST effective for optimizing performance? Consider the impact on concurrency.

A) Implement query acceleration using materialized views to pre-compute JSON parsing results. Then, add warehouses as needed for concurrent requests
B) Scale out to multiple MEDIUM warehouses using auto-scaling. This increases concurrency, allowing more queries to run simultaneously, but might not address CPU-bound operations within a single query.
C) Upgrade to an X-LARGE or higher warehouse, leveraging the increased resources to handle complex parsing and UDF execution more efficiently. Monitor CPU utilization after the upgrade.
D) Upgrade the warehouse to a LARGE. This provides more CPU and memory for the existing workload, potentially resolving the bottleneck and improving overall throughput.
E) Scale down to a SMALL warehouse. Smaller warehouses are optimized for smaller operations and can process certain types of operations faster. This could improve latency.

2. You have a Snowpark DataFrame named with the following schema: 'product_id' (INTEGER), (STRING), 'category' (STRING), 'price' (FLOAT), and 'description' (STRING). You want to perform several data cleaning and transformation steps. Which of the following operations can be efficiently chained together using Snowpark DataFrames to clean null values in 'description', replace special characters in 'product_name' and standardize 'category' values? Select all that apply:

A) Using the method to replace null values in the 'description' column with a default string 'No description available'.
B) Using the function to remove special characters (e.g., '$', '#, '@') from the 'product_name' column using a regular expression.
C) Using a UDF (User-Defined Function) written in Python to standardize the 'category' column by converting all values to lowercase and removing leading/trailing spaces.
D) Manually iterating through each row of the DataFrame and applying Python string manipulation functions to clean the data. (e.g. row['description'] =
E) Using the 'coalesce' function to fill null values in 'description' with values from a separate 'backup_description' column (if available).

3. You are developing a Snowpark application that utilizes a DataFrame named 'transactions df containing transactional data. You need to apply a series of complex transformations, including window functions and joins with other DataFrames. To optimize performance and manage resources effectively, you want to control how Snowpark executes these operations within Snowflake. Which of the following actions or configurations would have the MOST significant impact on controlling the execution plan and resource utilization of your Snowpark application?

A) Specify the 'num_partitionS parameter when creating or transforming the 'transactions_df DataFrame. This controls the number of partitions used for parallel processing.
B) Explicitly cache the 'transactions_df DataFrame using before applying any transformations. This forces Snowpark to materialize the DataFrame in memory.
C) Implement iterative algorithms within your Snowpark application using imperative Python loops instead of declarative DataFrame operations. This provides finer-grained control over the execution flow.
D) Configure the 'net.snowflake.snowpark.use_native_execution' parameter to 'true' at the session level. This forces Snowpark to translate DataFrame operations into native Snowflake SQL queries.
E) Use the 'DataFrame.explain()' method to analyze the generated SQL query plan before executing the transformations. Then, manually optimize the code based on the query plan output.

4. You have a Snowflake table 'PRODUCT CATALOG' with columns 'PRODUCT ID, 'PRODUCT NAME, and 'CATEGORY ID. You also have a table 'CATEGORY' with 'CATEGORY ID' and 'CATEGORY NAME. You need to create a Snowpark DataFrame that joins these two tables and includes only 'PRODUCT NAME and 'CATEGORY NAME. Assume a Snowpark Session object named 'session' is available. Which code snippet demonstrates creating the DataFrame using Snowpark's join functionality and column selection while using the 'table' method?

A)

B)

C)

D)

E)

5. A Snowpark developer is using to create a Snowpark session. They want to ensure that the session uses a specific role and warehouse, but only if those parameters are not already defined in the Snowflake CLI configuration. Which of the following code snippets correctly implements this behavior?

A)

B)

C)

D)

E)