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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are using Snowpark to process a DataFrame 'employee df containing employee data, including 'employee_id', 'name' , 'department' , and 'salary'. You need to implement a complex data cleaning and transformation pipeline that involves the following steps: 1. Remove duplicate rows based on 'employee id'. 2. Fill missing 'salary' values with the average salary for the employee's department. 3. Standardize department names by converting them to uppercase. 4. Create a new column 'salary_range' based on the salary. if Salary less than 50k 'Low', greater than 50k and less than 100k 'Medium', greater than 100k 'High'. Which of the following code snippets MOST effectively combines these transformations into a single, readable, and efficient Snowpark pipeline? Assume you have a session object available named 'session' and import necessary modules from 'snowflake.snowpark.functions as F'

A)

B)

C)

D)

E)

2. Consider the following Snowpark Python code snippet designed to aggregate sales data by region:

During testing, you observe that the performance of this code is suboptimal, especially when dealing with very large 'SALES DATA tables. Using Snowflake's query history, you notice that a significant amount of time is spent on data shuffling during the operation. What optimization strategies could you employ within this Snowpark code to minimize data shuffling and improve the overall performance?

A) Utilize the 'hint' function in Snowpark to provide a join hint that suggests a specific join strategy to the Snowflake query optimizer.
B) store 'SALES_DATX as a clustered table using the 'REGION' column.
C) Repartition the 'sales_df DataFrame using 'repartitionBy' before the 'groupBy' operation, specifying the 'REGION' column to ensure that data for each region is co-located on the same node.
D) Increase the warehouse size to provide more resources for data processing, which will inherently reduce data shuffling.
E) Apply a more selective 'filter operation to the 'sales_df' DataFrame before the 'groupBy' operation, reducing the amount of data that needs to be shuffled.

3. You are working with Snowpark and need to persist the results of a DataFrame 'df to a Snowflake stage named 'my_stage'. You want to achieve the following: 1. Write the data in JSON format. 2. Use snappy compression. 3. Handle potential write errors gracefully. 4. Overwrite any existing files with the same name. Which of the following approaches can achieve these requirements? (Select all that apply)

A) Define a UDF to write the dataframe into stage along with exception handling logic.
B) Use 'df.write.option('compression', inside a 'try-except block.
C) Use compression='snappy', mode='overwrite')' and handle potential exceptions using a 'try-except' block.
D) Configure the stage 'my_stage' with FILE_FORMAT = (TYPE = 'JSON', COMPRESSION = 'SNAPPY') and then use within a 'try-except' block.
E) Wrap the entire write operation in a try-except block and implement retry logic with exponential backoff in case of transient errors.

4. You are tasked with optimizing a Snowpark application that uses a Python UDF to perform complex string manipulations on a large dataset. The current implementation uses a scalar UDF. You are considering converting it to a vectorized UDF. What are the key considerations and potential limitations you need to address during the conversion to ensure correctness and optimal performance? Choose all that apply:

A) The vectorized UDF should utilize libraries like NumPy or Pandas for efficient array processing, but it's important to be aware of the limitations on available Python packages in the Snowflake environment.
B) The vectorized UDF's return type must be compatible with Snowpark's data types, and the UDF should return an array of the appropriate type with the same length as the input arrays.
C) The vectorized UDF must be able to handle NULL values gracefully within the input arrays, as these can cause errors if not explicitly addressed.
D) The input and output data types of the vectorized UDF must exactly match the corresponding column data types in the Snowpark DataFrame.
E) Vectorized UDFs always perform better than scalar UDFs, regardless of the complexity of the string manipulations or the size of the dataset.

5. You are developing a Snowpark application that requires secure access to Snowflake. You need to establish a session using Key Pair authentication. You have stored your private key in an encrypted file and have the passphrase available. Assume you've correctly configured your Snowflake user with the public key. Which of the following methods can be used to load the private key, assuming that 'PRIVATE KEY PATH' stores path to encrypted private key and 'PRIVATE KEY PASSPHRASE stores passphrase?

A)

B)

C)

D)

E)

Solutions: