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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark DataFrame containing customer data'. You need to create a stored procedure that accepts the DataFrame and a list of column names as input and returns a new DataFrame containing only the specified columns. Which of the following approaches correctly implement this functionality and handles data types effectively (Select all that apply)?

A)

B)

C)

D)

E)

2. You are tasked with building a Snowpark application to process sensor data from IoT devices. The data arrives as JSON strings and needs to be transformed into a tabular format before being stored in a Snowflake table. You decide to use a User-Defined Table Function (UDTF) written in Python to handle this transformation. Which of the following approaches is the MOST efficient and scalable way to deploy and execute this UDTF in a production Snowpark environment, considering the possibility of high data volumes and concurrency?

A) Deploy the UDTF as an inline Python function within the Snowpark DataFrame transformation code, relying on the client-side Python interpreter for execution.
B) Package the Python UDTF into a zip file and upload it to a Snowflake stage. During UDTF registration, specify the stage location and use [...l, return_type=..., packages=['snowflake-snowpark-python', 'your_package'])'. Ensure that is appropriately tuned based on data size and complexity.
C) Deploy the UDTF as an external function, leveraging an external API gateway to invoke the Python code running on a serverless compute platform.
D) Deploy the UDTF using the '@udtf decorator and explicitly specify a parameter when registering the function to control the size of input batches processed by each worker node.
E) Deploy the UDTF as a stored procedure that is then called from your Snowpark application. The UDTF processes the data serially, one record at a time.

3. You have a Snowpark DataFrame 'df sales' containing sales data with columns like 'order id', 'product id', 'quantity', and 'sale_price' You want to persist this data into a Snowflake table named "SALES DATA'. You also want to create a dynamic table on top of this base table for faster analytics. You need to choose the appropriate persistence strategy and consider the implications of using a dynamic table. Which of the following options represents the BEST approach?

A) Option B
B) Option E
C) Option D
D) Option C
E) Option A

4. You are developing a Snowpark Python application that needs to process large datasets. You want to optimize performance by leveraging user-defined functions (UDFs) to perform complex calculations in parallel across the Snowflake data warehouse. Which of the following statements regarding Snowpark UDFs are TRUE?

A) Snowpark UDFs automatically distribute the data and computation across multiple nodes in the Snowflake warehouse, but the distribution strategy cannot be controlled by the developer.
B) Snowpark UDFs can be defined using either Python or Java, providing flexibility in choosing the programming language best suited for the task. The Java UDF creation method will allow faster execution speeds.
C) Snowpark Python UDFs are always executed in a single process on the Snowflake warehouse, limiting their parallel processing capabilities.
D) Snowpark UDFs can be defined as either scalar UDFs (processing one row at a time) or vectorized UDFs (processing batches of rows), offering different performance characteristics.
E) To ensure optimal performance, it is recommended to always use the default Snowflake Anaconda channel for UDF dependencies, as custom channels may introduce latency.

5. Consider the following Snowpark code snippet:

Which of the following statements are TRUE regarding the execution and performance of this code?

A) The 'countl' operation will trigger the materialization and caching of 'filtered_df.
B) The 'filter' operation Ccol('column_a') > 100') will be executed twice.
C) The 'count? operation will use the cached results of and apply an additional filter on the cached data.
D) Removing 'cached_df = line would significantly improve the overall performance because caching always adds overhead.
E) The 'filter' operation Ccol('column_a') > 100') will be executed only once because 'cached_df stores the materialized result.

Solutions: