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| Section | Weight | Objectives |
|---|---|---|
| Snowpark API and Development | 30% | - Python API fundamentals
|
| Data Transformations and Operations | 35% | - DataFrame manipulation
|
| Performance and Best Practices | 10% | - Optimization techniques
|
| Snowpark Concepts and Architecture | 25% | - Session management and connection
|
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NEW QUESTION # 241
A data engineering team has created several Snowpark Python UDFs and UDTFs in the 'TRANSFORMATIONS' schema of the 'ANALYTICS' database. A data science team needs to use these functions in their data analysis notebooks. What is the MINIMUM set of privileges that must be granted to the data science team's role ('DATA SCIENTIST') to allow them to discover and execute these UDFs and UDTFs?
Answer: E
Explanation:
The 'USAGE privilege on the database and schema is required for the role to discover (see) the UDFs and UDTFs. The 'EXECUTE privilege on the functions themselves is required to execute them. 'ALL PRIVILEGES' is an overly permissive grant and not the minimum required. Option D is missing the execute privilege. Option E is missing USAGE on Database and Schema.
NEW QUESTION # 242
You are using Snowpark in Python within a Jupyter Notebook environment to analyze sales data'. You've established a connection to Snowflake and loaded your data into a Snowpark DataFrame named 'sales ff. You need to calculate the cumulative sales for each product category over time. The 'sales_df' DataFrame has columns 'SALE DATE' (DATE), 'PRODUCT CATEGORY' (VARCHAR), and 'SALE AMOUNT' (NUMBER). Which of the following approaches, or combination of approaches, will correctly calculate the cumulative sales while optimizing for Snowflake's performance and scalability? (Select all that apply)
Answer: A,B,C
Explanation:
Options A, C, and E are correct. Option A utilizes Snowpark's DataFrame transformations with window functions, which are optimized for Snowflake's engine and can efficiently handle large datasets. This leverages Snowflake's parallel processing capabilities. Option C involves creating a stored procedure within Snowflake. This approach pushes the computation to the Snowflake server, potentially improving performance, especially if the cumulative sales calculation is complex. This also benefits from Snowflake's optimization capabilities. Option E executes a SQL query with a window function that's optimized. Option B is incorrect because iterating through rows in a Jupyter Notebook will be extremely slow and inefficient for large datasets. It defeats the purpose of using Snowpark for distributed data processing. Calculating cumulative sales this way doesn't leverage Snowflake's capabilities. Option D is partially correct in that it orders the data, but using a UDF for cumulative sum calculation will likely be less efficient than using native window functions within Snowflake.
NEW QUESTION # 243
You are developing a Snowpark application to analyze website traffic data'. You have a DataFrame named 'website_logs' with columns 'user_id', 'page_url', and 'timestamp'. You need to create a new DataFrame that contains the count of distinct users who visited each page within a specific time window Consider the following (incomplete) Snowpark Python code:
Which of the following code lines, when inserted into the Complete the following line...' comment, will correctly calculate the approximate distinct user count for each page within the specified time window?
Answer: C
Explanation:
The correct code line is 'website_logs.with_column('distinct_users', F.approx_count_distinct('user_id').over(window_spec))'. This uses the function to calculate the approximate distinct count of user IDs within the window defined by 'window_spec' . Option B uses exact count which is less performant. Option A performs an aggregation, which will give a different type of result. Option D uses F.window' which is used for tumbling windows, not sliding windows as requested by the problem.
NEW QUESTION # 244
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 # 245
You are developing a Snowpark application to process large datasets stored in Snowflake. You need to create a session using the 'snowflake.connector.connect' method. Which of the following code snippets correctly establishes a session with Snowflake, leveraging an external browser authentication mechanism, ensuring secure and reliable access to your data while minimizing exposed credentials in the code?
Answer: D
Explanation:
The correct answer is B. When using 'externalbrowser' authentication, providing the username and password in the connection string is not required, as the authentication is handled through the browser. Options A and C are incorrect because including a password with 'externalbrowser' is unnecessary and 'snowflake' is for Snowflake username/password authentication, respectively. Option D is incomplete because it doesn't specify an authenticator. Option E is missing the warehouse, database and schema, required for a functioning Snowpark session in most scenarios.
NEW QUESTION # 246
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