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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q50-Q55):

NEW QUESTION # 50
You are working with a Snowpark DataFrame containing customer data'. One of the columns, 'phone number', contains phone numbers in various formats (e.g., '123-456-7890', '(123) 456-7890', '1234567890'). You need to standardize all phone numbers to the format '+1-123-456-7890' using Snowpark for Python. You also want to handle cases where the phone number is NULL gracefully, replacing them with '+1-000-000-0000'. Which of the following Snowpark code snippets is the most efficient and correct way to achieve this?

Answer: A

Explanation:
Option C is the most efficient because it uses built-in Snowpark functions (when, regexp_replace, substring, concat, length, and lit) to perform the transformation directly on the server-side. It first handles NULL values. It then removes non-numeric characters. Finally, it checks the length of the remaining digits before formatting, ensuring only valid 10-digit numbers are transformed, setting others to NULL. Options A, D, and E do not handle the case where after removing non-numeric characters, the length of phone number is not 10. Option B uses a UDF, which is generally less efficient than using built-in functions as it involves serialization/deserialization overhead .


NEW QUESTION # 51
You have a Snowpark DataFrame 'df that you want to persist as a Snowflake table. You need to ensure the following requirements are met: 1. The table should be created if it does not exist. 2. If the table exists, the new data should be merged with the existing data based on a 'primary_key' column. 3. If a row with a matching 'primary_key' already exists in the target table, update the existing row with the values from the 'df DataFrame. Otherwise, insert the row from 'df into the target table. Which of the following approaches can achieve this using Snowpark?

Answer: A,B

Explanation:
Options C and D are the correct answers. Snowpark's method does not directly support a merge operation. To achieve this, you typically need to use one of the following approaches: Create a stored procedure that executes a Snowflake MERGE statement. This allows you to perform the merge operation server-side within Snowflake using SQL. This stored procedure can then be called from the Snowpark application. Stage your dataframe in to a new table, then create merge statement to handle the rest. Option A is incorrect because Snowpark does not have a option for Option B is possible but very inefficient, it requires reading all of the existing data in the table. Option E is inefficient because converting DataFrame to Pandas will add another performance bottleneck when dealing with large datasets.


NEW QUESTION # 52
You are working with a data science team that needs to create Snowpark DataFrames from various file types (CSV, JSON, Parquet, and XML) stored in different locations (internal stages, external stages on AWS S3, and Azure Blob Storage). The team wants a unified and reusable function to create DataFrames, abstracting away the specific file format and location details. Which of the following approaches using Snowpark Python API will provide the MOST flexible and maintainable solution?

Answer: A

Explanation:
Option C provides the best balance of flexibility, maintainability, and conciseness. Using 'getattr(session.read, file_format)' allows dynamically calling the appropriate 'session.read' method (e.g., 'session.read.csv', 'session.read.json') based on a string parameter. Passing additional configuration through a dictionary allows customizing the read operation without modifying the core function. Options A, B, D, and E are less flexible, more verbose, or less efficient.


NEW QUESTION # 53
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?

Answer: E

Explanation:
Option C is the most effective strategy for improving performance with large datasets. Snowpark's vectorized UDFs allow you to leverage Snowflake's distributed processing capabilities to perform calculations in parallel, avoiding the overhead of transferring data to Pandas. Option B would bring the entire dataset into memory on a single node which defeats the purpose of Snowflakes distributed computing. Option A might help but wouldn't fundamentally address the distribution issue. Option D is a brute-force approach and might help, but vectorized UDFs are more efficient. Option E could work but it requires complicated coding of partitioning logic.


NEW QUESTION # 54
Consider the following Python code snippet using Snowpark:

Which of the following statements are true regarding this Snowpark code?

Answer: A,B,C,D

Explanation:
The code correctly connects to Snowflake, reads data from 'my_table', performs aggregation, and saves the result to 'agg_sales_table' with overwrite mode, and closes the session. While password authentication is discouraged, it is still supported. The other options accurately describe the code's functionality. Closing the session is a best practice to release resources.


NEW QUESTION # 55
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