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Updated Snowflake SPS-C01: Best Snowflake Certified SnowPro Specialty - Snowpark Study Material - Accurate DumpsFree Valid Exam SPS-C01 Preparation

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q213-Q218):

NEW QUESTION # 213
A UDTF (User-Defined Table Function) named 'split_sentences" takes a text string as input and returns a table with each row containing a single sentence from the input. You need to grant SELECT privilege on this UDTF to a specific role, 'DATA ANALYST'. Which of the following SQL statements will achieve this?

A. GRANT EXECUTE ON FUNCTION TO ROLE DATA_ANALYST;
B. GRANT OWNERSHIP ON FUNCTION TO ROLE DATA ANALYST,
C. GRANT SELECT ON TABLE FUNCTION TO ROLE DATA_ANALYST;
D. GRANT ALL PRIVILEGES ON FUNCTION TO ROLE DATA_ANALYST;
E. GRANT USAGE ON FUNCTION TO ROLE DATA_ANALYST;

Answer: C

Explanation:
Since UDTFs return a table, you need to grant the 'SELECT privilege on the table function to allow the role to query the results produced by the UDTF. The syntax is 'GRANT SELECT ON TABLE FUNCTION'. option A is wrong it's for UDE Options C and E provide too many privileges, potentially allowing unintended modifications. Option D is not the correct Privilege.

NEW QUESTION # 214
Consider the following Snowpark Python code snippet that defines and applies a UDF:

Which of the following modifications would MOST likely improve the performance of this code, assuming the DataFrame 'df contains a large number of rows?

A. Remove the 'input_types' argument from 'session.udf.register' . Snowflake can automatically infer the input types.
B. Specify a different warehouse size when creating the Snowpark session using 'session = Session.builder.config('warehouse', 'XLARGE').configs(connection_params).create()'.
C. Rewrite the 'apply_discount' function to use NumPy arrays internally for vectorized calculations, ensuring compatibility with vectorized UDF execution. The function signature will also need to accept arrays.
D. Change to in the "session.udf.register' call, ensuring the function is updated to handle batches of data.
E. Use and F.lit(0.2Y instead of 0.1 and 0.2 while creating the dataframe.

Answer: C,D

Explanation:
Setting tells Snowpark to treat the UDF as a vectorized UDE However, simply setting the flag is not sufficient. E is also required because Vectorized UDF requires functions that accept and process arrays as inputs, not single scalar values. The function needs to be modified to handle arrays using NumPy or similar libraries. The code also need to be rewritten to work with arrays. B has nothing to do with performance. Increasing Warehouse size might help but not on the scale of vectorization.

NEW QUESTION # 215
You are developing a Snowpark application that processes real-time streaming data'. The application needs to perform a complex calculation for each incoming event. To improve performance, you decide to leverage asynchronous execution and User-Defined Functions (UDFs). However, you are encountering issues with the order of results and ensuring that the processing order matches the arrival order of the events. Which of the following strategies MOST effectively addresses the challenge of maintaining processing order while leveraging asynchronous execution and UDFs in Snowpark?

A. Employ asynchronous UDF calls with 'block-False' and rely on Snowflake's internal optimization to maintain the processing order.
B. Use synchronous UDF calls with a large Snowflake warehouse to minimize processing time and guarantee order.
C. Use synchronous UDF calls with a small Snowflake warehouse to introduce artificial delays and ensure order.
D. Abandon the use of UDFs altogether and reimplement the complex calculation using only built-in Snowpark DataFrame transformations to ensure order.
E. Utilize asynchronous UDF calls with 'block-False' and implement a custom ordering mechanism based on a timestamp or sequence number associated with each event. Store the results in a temporary table and sort them based on the timestamp before further processing.

Answer: E

Explanation:
Asynchronous execution, by definition, doesn't guarantee the order of completion. To maintain order, a custom ordering mechanism based on a timestamp or sequence number is necessary. Storing the results in a temporary table and sorting them before further processing is a viable approach. Synchronous calls (A, D) negate the benefits of asynchronous execution. Snowflake's internal optimization (C) doesn't guarantee order in asynchronous scenarios. Reimplementing the calculation without UDFs (E) might not be feasible or efficient if the calculation is complex. The 'block' parameter is relevant to asynchronous behaviour and must be False' to truly utilize the asynchronous execution model. The UDFs will be executed asynchronously, and the order is maintained by using Timestamp.

NEW QUESTION # 216
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: D

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 # 217
You have a Snowpark DataFrame with columns 'order_id', 'product_id', 'sale_date' (DATE), and 'sale_amount'. You need to perform the following transformations: 1. Filter out sales records before January 1, 2023.2. Group the data by 'product_id' and calculate the total 'sale_amount' for each product. 3. Create a new column 'average_sale_amount' by dividing the total 'sale_amount' by the number of distinct 'order_id' for each product. You must alias the aggregate function. Which of the following Snowpark code snippets correctly implements these transformations?

Answer: E

Explanation:
Option E is the most appropriate and correct. It uses for explicit date conversion, ensures correct filtering, aggregates with aliases, and calculates the 'average_sale_amount' correctly using the aliased columns. Options A, B, C and D are all valid for Snowflake. Option B incorrectly passes the date string. 'as_' instead of 'alias' are not valid and will cause an issue.

NEW QUESTION # 218
......

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