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| Section | Weight | Objectives |
|---|---|---|
| Data Transformations and Operations | 35% | - User-defined logic
|
| Performance and Best Practices | 10% | - Optimization techniques
|
| Snowpark Concepts and Architecture | 25% | - Snowpark architecture and execution model
|
| Snowpark API and Development | 30% | - Python API fundamentals
|
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NEW QUESTION # 17
You are tasked with creating a Snowpark stored procedure that needs to access a secret stored in Snowflake's Secret Managen The secret contains credentials required to connect to an external API. Which of the following steps are necessary to correctly and securely access and use the secret within your Snowpark stored procedure? (Select all that apply)
Answer: B,C,E
Explanation:
Options A, B, and D are the correct steps. First, the executing role needs 'USAGE on the secret. Second, session.get_secret('secret_name')' is the correct method to access the secret value within the procedure. The stored procedure must be created with EXECUTE AS CALLER for it to use the caller's permissions (which include access to the secret). Option C is incorrect because storing secrets directly in the code is a security risk. Option E is incorrect because Creating a UDF is unneccessary, stored procedures are capable of accessing secret manager directly with provided right access.
NEW QUESTION # 18
You have a Snowflake table containing JSON data with nested arrays and objects representing website user interactions. You want to extract all 'product_id' values from within an array named 'viewed _ products' nested inside a 'session' object for each event, using Snowpark for Python. Assume the 'raw_events' table has a variant column called 'event_data". Which of the following Snowpark code snippets will correctly extract and flatten the 'product_id' values into a DataFrame?
Answer: E
Explanation:
Option A correctly uses the 'flatten' function to unnest the 'viewed_products' array. It then accesses the 'product_id' within each flattened element using Tvalue']['product_idT. Options B, C, D and E either uses wrong function explode or does not handle nested structure properly or converts to varchar prematurely.
NEW QUESTION # 19
You have a Snowpark DataFrame 'customer df with a 'customer name' column. You need to create a new column 'initials' that contains the initials of each customer's name. For example, if 'customer name' is 'John Doe', 'initials' should be 'JD'. You must handle names with multiple words correctly. Which Snowpark SQL expression using the "col()' function is the most efficient and correct way to define the 'initials' column?
Answer: E
Explanation:
Option D is the most robust and efficient solution. It uses a regular expression to extract the first letter of each word in the name, handling multiple words correctly, and converting it to uppercase. Option A is incorrect because it only handles two-word names and relies on string concatenation, which can be less efficient than using the 'concat function. Option B is incorrect as 'array_accumulate' is not used in Snowpark to compute initials. Option C is incorrect as it is too basic and relies on two-word names only. Option E is incorrect as it extracts only the first two characters from the name.
NEW QUESTION # 20
You have a Snowpark DataFrame 'df_orders' containing order data'. You want to delete all records from the underlying Snowflake table 'ORDERS TABLE' where the 'order_date' is older than '2023-01-01' using a Snowpark DataFrame operation. Which of the following code snippets is the MOST efficient and recommended way to achieve this, assuming 'spark' is your Snowpark Session object?
Answer: B
Explanation:
Option B, < '2023-01-01 , is the most efficient and direct way to delete data from the underlying table using Snowpark's DataFrame API. Options A, C, D, and E are less efficient. A overwrites the entire table, C uses SQL directly (less Snowpark), D iterates row by row which is extremely slow and inefficient, and E uses a temp view and SQL, still not optimal. Option B leverages the power of Snowpark to perform the deletion operation directly.
NEW QUESTION # 21
You're working with Snowpark and want to load data from a Pandas DataFrame into a Snowpark DataFrame. The Pandas DataFrame, 'customer_data' , contains columns with mixed data types (integers, strings, dates). Some columns also contain NULL values. You need to ensure that the data types are correctly inferred by Snowpark, NULL values are handled appropriately, and the resulting Snowpark DataFrame 'snowpark_customers' can be used for further transformations. What is the best approach to achieve this with minimal code and maximum performance?
Answer: C
Explanation:
Relying on schema inference (option C) might not always guarantee the correct data types, especially with dates or mixed-type columns. Explicitly defining the schema (option B) can be verbose and error-prone. Replacing NA/NaN with None and using 'createDataFrame' (option D) is a functional approach, but might not be as performant as the optimized method of 'write_pandaS. Inferring the schema (Option A) might not be fully accurate. Using session.write_pandas leverages internal Snowflake optimizations for data transfer and type handling from Pandas to Snowpark, making it the most efficient.
NEW QUESTION # 22
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