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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are developing a Snowpark Python stored procedure for processing financial data'. The procedure uses the 'pandas' library for data manipulation and the 'scipy' library for statistical calculations. You want to optimize the execution of the stored procedure to leverage the available resources in your Snowflake environment. Which of the following strategies would be MOST effective in improving the performance of your stored procedure, considering the need to handle large datasets?

A) Partition the input DataFrame into smaller chunks and process each chunk sequentially using Pandas, then combine the results.
B) Increase the warehouse size to the largest available option (e.g., X-Large) to provide more memory and CPU resources.
C) Convert the Snowpark DataFrame to a Pandas DataFrame within the stored procedure and perform all calculations using Pandas and SciPy.
D) Use the 'cachetools' library within the stored procedure to cache intermediate results of calculations, reducing redundant computations.
E) Utilize Snowpark's vectorized UDFs to perform the calculations on the data in parallel, avoiding the need to transfer the data to Pandas.

2. You are developing a Snowpark application that processes large datasets stored in Snowflake. You need to implement custom User-Defined Functions (UDFs) written in Java. The UDF requires specific third-party libraries that are not available in the default Snowflake Java environment. What steps are necessary to package and deploy these UDFs correctly?

A) Create a separate Python UDF that imports the Java code using Jpype and then register the Python UDF with Snowflake
B) Utilize the Snowpark API to create a 'snowpark.functions.udf object, including the Java code and dependencies. The Snowpark runtime will handle the deployment.
C) Use the 'CREATE OR REPLACE JAVA FUNCTION' command directly in Snowsight and paste the Java code along with the dependencies' contents into the function body.
D) Package the Java code and dependencies into a JAR file and upload it to a Snowflake stage. Use the 'CREATE FUNCTION' command with the 'IMPORTS' clause to reference the JAR file.
E) Compile the Java code into a native library (e.g., a .so file), upload it to a Snowflake stage, and use the 'CREATE EXTERNAL FUNCTIONS command to invoke it.

3. You are using Snowpark Python to build a machine learning pipeline. One step in the pipeline involves feature engineering using a large dataset. This feature engineering step is computationally expensive and involves several transformations. You want to optimize the performance of this step by caching intermediate results. Given the following code snippet, which of the following strategies would be MOST effective for optimizing the performance, considering the use of

A) Avoid using altogether because it can introduce overhead and is not always beneficial.
B) Cache the final DataFrame only after all feature engineering steps are completed.
C) Cache the initial raw data DataFrame before applying any transformations.
D) Identify DataFrames that are reused multiple times and cache them using after the transformations that generate them.
E) Cache each intermediate DataFrame after each individual transformation step, even if the DataFrame is only used once.

4. Consider a scenario where you're developing a Snowpark stored procedure that accesses sensitive data'. Which of the following strategies, when used together, provide a comprehensive approach to secure this stored procedure and protect the underlying data?
Select all that apply:

A) Encrypting the stored procedure's code using AES encryption before deployment.
B) Masking sensitive data within the stored procedure using Snowflake's dynamic data masking policies.
C) Implementing row-level security policies on the sensitive data tables.
D) Using 'EXECUTE AS CALLER and relying on the caller's privileges to access the data.
E) Using 'EXECUTE AS OWNER and granting the 'SELECT privilege on the sensitive data tables to the stored procedure's owner role.

5. Consider a DataFrame 'products df loaded from a SnoMlake table. It contains a 'features' column of type VARIANT, where each row contains a JSON object representing product features. Your task is to create a new DataFrame where each feature becomes a separate column. You need to dynamically extract these features without knowing the specific feature names in advance. Which of the following approaches could achieve this using Snowpark, and what considerations are important? Choose all that apply:

A) Use the 'FLATTEN' function within a Snowpark DataFrame transformation. This allows you to transform the key-value pairs within the VARIANT column into separate rows, which can then be pivoted to create new columns.
B) Use the function on the VARIANT column to get an array of feature names. Then, use a loop to iterate over this array and dynamically create new columns using bracket notation (e.g.,
C) Use a User-Defined Function (UDF) to parse the JSON and return a dictionary. Then, use a loop to iterate over the keys in the dictionary and create new columns based on these keys.
D) The function can be used in conjunction with a Snowpark SQL query to dynamically extract the json into separate columns.
E) It's not possible to dynamically extract feature names and create columns in Snowpark without knowing the schema in advance.

Solutions: