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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowpark API for Python | 30% | - DataFrame creation and manipulation - Working with Semi-structured data - Reading and writing data - User-Defined Functions (UDFs) and Stored Procedures - Establishing connections and session management |
| Topic 2: Data Transformations and DataFrame Operations | 35% | - Using built-in functions - Window functions - Persisting transformed data - Complex data pipelines - Filtering, Aggregating, and Joining DataFrames |
| Topic 3: Snowpark Concepts | 15% | - Snowpark DataFrames and query plans - Snowpark architecture and core concepts - Snowpark Sessions and connection management - Transformations vs. Actions - Client-side vs. Server-side execution - Stored procedures and conditional logic |
| Topic 4: Performance Optimization and Best Practices | 20% | - Query pushdown and optimization - Debugging and explain plans - Vectorized UDFs - Caching strategies - Minimizing data transfer - Warehouse sizing for Snowpark |
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NEW QUESTION # 215
A data engineer is tasked with calculating a 3-month rolling average of sales data using Snowpark Python. The sales data is stored in a table named 'SALES DATA' with columns 'sale_date' (DATE) and (NUMBER). They need to use a table function to accomplish this efficiently. Which of the following Snowpark Python code snippets correctly implements this rolling average calculation using a table function?
Answer: C
Explanation:
Option E correctly defines a table function that takes a DataFrame as input and returns a DataFrame with the rolling average calculated using the 'avg()' function and a window specification. It then uses 'session.table_function' to apply the table function to the DataFrame and selects the required columns. Option A attempts to call directly within the table function definition which is invalid. Option B passes 'session' as the first argument to table function that's wrong. Option C defines that aren't compatible with DataFrame operations, this approach is not how you should do. Option D is same as C, and incorrect because, it attempts to use window functions inside the table function where the input is a table function input types.
NEW QUESTION # 216
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: A
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 # 217
Consider the following Snowpark Python code snippet designed to perform a join operation between two large tables: 'transactions' and 'customers'. The 'transactions' table contains billions of rows and the 'customers' table contains millions of rows. You are experiencing performance bottlenecks during the join operation. The 'transactions' table has a 'customer id' column that references the 'customers' table's primary key 'id'. Which optimization techniques would be MOST effective in improving the join performance within a Snowpark- optimized warehouse?
Answer: E
Explanation:
Broadcasting the smaller table (customers) to all nodes allows for a more efficient join by avoiding the need to shuffle large amounts of data. Snowflake automatically handles index management and optimization; explicitly defining indexes is not necessary. While repartitioning can sometimes improve join performance, in this scenario broadcasting the smaller table is generally more effective. Increasing the warehouse size might help, but it's less targeted than broadcasting. 'how='broadcastr is not a valid parameter in Snowpark's 'join()' function.
NEW QUESTION # 218
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: C
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 # 219
Which of the following statements are correct regarding account identifiers and their usage when creating Snowpark sessions in Python?
Answer: C,D
Explanation:
Account identifiers provide a unique way to connect to Snowflake accounts. If the identifier includes the region ID, specifying it separately is redundant. Snowflake supports both 'Organization Name-Account Name' and legacy account locators. Option A is incorrect because account identifiers are case-insensitive. Option B is incorrect, account identifiers can be used across regions. Option D is incorrect.
NEW QUESTION # 220
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