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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowpark API for Python | 30% | - Reading and writing data - Establishing connections and session management - Working with Semi-structured data - User-Defined Functions (UDFs) and Stored Procedures - DataFrame creation and manipulation |
| Topic 2: Data Transformations and DataFrame Operations | 35% | - Persisting transformed data - Complex data pipelines - Using built-in functions - Filtering, Aggregating, and Joining DataFrames - Window functions |
| Topic 3: Performance Optimization and Best Practices | 20% | - Minimizing data transfer - Vectorized UDFs - Query pushdown and optimization - Caching strategies - Warehouse sizing for Snowpark - Debugging and explain plans |
| Topic 4: Snowpark Concepts | 15% | - Transformations vs. Actions - Client-side vs. Server-side execution - Snowpark architecture and core concepts - Stored procedures and conditional logic - Snowpark Sessions and connection management - Snowpark DataFrames and query plans |
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NEW QUESTION # 161
You are using Snowpark Python to create a DataFrame from an existing Snowflake table "SALES DATA'. You want to apply a user- defined function (UDF) to each row of the DataFrame to calculate a custom sales metric. The UDF requires access to the 'session' object. Which of the following approaches is correct for defining and applying the UDF in Snowpark?
Answer: E
Explanation:
Option E is the correct approach. To access the session object from within the UDF, you can import it when registering the UDF with the session. It should be imported inside the function and use the decorator. The input_types parameter should be set to 0, since this allows for session access.
NEW QUESTION # 162
You have a Snowpark DataFrame named 'products' with columns 'product_id' (INT), 'product_name' (STRING), and 'price' (DOUBLE). You want to apply a transformation to calculate a 'discounted_price' column, which is the 'price' reduced by 10% if the price is greater than $100.00. Which of the following code snippets is the most efficient way to achieve this using Snowpark Python?
Answer: D,E
Explanation:
The most efficient ways are B and C. Option B directly uses the 'when' and 'otherwise' functions in Snowpark, which are optimized for execution within Snowflake. Option C is similar to B but explicitly uses 'lit' to represent the numeric literal, ensuring proper type handling in Snowpark. IJDFs (Option A) are generally less efficient than built-in functions. Option D attempts to use RDDs, which are not part of the Snowpark API. Option E is not valid Snowpark python syntax. Therefore, B and C are the correct answers.
NEW QUESTION # 163
You are developing a Snowpark application that needs to connect to Snowflake using programmatic access. You want to use a secure method of authentication. Which of the following methods, when passed as parameters to the 'snowpark.Session.builder.configS method, would be MOST secure and appropriate for production environments?
Answer: A,E
Explanation:
Using 'oauth_access_token' and 'private_key' (especially when stored securely) are more secure than directly passing username and password. OAuth and Key Pair authentication are recommended for production environments because they avoid storing or transmitting passwords directly. Options A & B are vulnerable because they expose credentials directly in the code or configuration. Option E is incorrect because simply setting the authenticator does not ensure the user authentication will happen with secure methods. User must use Oauth or Key pair authentication for Production use case.
NEW QUESTION # 164
You are developing a Snowpark stored procedure to perform data transformation. The procedure needs to handle both integers and strings as input parameters and return a DataFrame. Which of the following is the MOST efficient and recommended way to define the data types for the input parameters and the return value, ensuring proper data handling and type safety during deployment and execution?
Answer: A
Explanation:
Explicitly registering the stored procedure with 'sproc.register' and defining the input types using Python type hints (e.g., 'x: int, y: str') and the return type using is the most robust approach. While type hints improve readability and allow for static analysis, registration with explicit return type schema enforces the structure of the returned DataFrame at deployment, ensuring type safety and preventing unexpected runtime errors. This is especially critical in production environments. Using implicit type inference or relying solely on Python hints can lead to unexpected issues if the data doesn't conform to the expected types.
NEW QUESTION # 165
You are working with a Snowpark DataFrame that contains product information including 'product_name' and 'description'. You need to create a new column named 'search_terms' that contains the first three words from the 'description' column, converted to lowercase. If the description has fewer than three words, the 'search_terms' column should contain all the words available. The words should be separated by a space. What is the MOST efficient way to achieve this using Snowpark?
Answer: E
Explanation:
Using a UDF (Option C) is a valid approach, but generally less efficient than leveraging built-in functions or existing Snowflake functions, especially for large datasets, because it involves serialization and deserialization between Python and Snowflake. Option A is not dynamic and will error on strings with fewer than three words. Option D will only correctly return the words if there are exactly three, otherwise it won 't match. E will just return the lowercased string and Snowflake UDF defined in SQL. Snowflake UDFs overhead, and are preferable for performance It is also less efficient than a UDF/Stored proc as it involves calling the Regex. Option is incorrect. Option B (UDF) is the most efficient, assuming GET FIRST THREE_WORDS is a are optimized for execution within the Snowflake environment, minimizing data transfer compared to Python UDFs in Snowpark.
NEW QUESTION # 166
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