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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Performance Optimization and Best Practices | 20% | - Warehouse sizing for Snowpark - Minimizing data transfer - Query pushdown and optimization - Vectorized UDFs - Debugging and explain plans - Caching strategies |
| 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: Snowpark Concepts | 15% | - Snowpark DataFrames and query plans - Transformations vs. Actions - Snowpark Sessions and connection management - Stored procedures and conditional logic - Snowpark architecture and core concepts - Client-side vs. Server-side execution |
| Topic 4: Snowpark API for Python | 30% | - DataFrame creation and manipulation - User-Defined Functions (UDFs) and Stored Procedures - Working with Semi-structured data - Establishing connections and session management - Reading and writing data |
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NEW QUESTION # 174
You are using Snowpark Python to transform a large DataFrame containing customer transaction data'. You need to persist the resulting DataFrame as a new Snowflake table named 'CUSTOMER TRANSACTIONS AGGREGATED', replacing the existing table if it exists. You want to explicitly define the schema of the new table to ensure data types are correctly enforced. Which of the following code snippets achieves this most efficiently and correctly?
Answer: E
Explanation:
Option A is the simplest and most direct way to achieve the desired outcome using the method with the 'overwrite' mode. While defining the schema is important, Snowflake infers the schema from the DataFrame if not explicitly provided. If schema inference isn't working, it should be investigated as a separate issue. Option B requires an intermediary view, which is less efficient. Options C and D are not valid Snowpark options. While you can specify file format related options (e.g. CSV options when writing to cloud storage), 'table_type' isn't one of them. Option E introduces the concept of schema definition, which, while important in general, is unnecessary if Snowflake can infer the correct schema. The question asks for the most efficient and correct answer, which is A.
NEW QUESTION # 175
You have a Snowpark DataFrame with columns 'product_id', 'customer_id', and 'sale_amount'. Some values are negative, indicating returns, and others are null. You need to replace negative values with 0 and fill null values with the average 'sale_amount' for each 'product_id'. Which of the following approaches is the MOST efficient and correct way to achieve this using Snowpark?
Answer: C
Explanation:
Option E first replaces negative values with 0. Then, it calculates the average sales amount per product and joins it back to the original DataFrame. Finally, it fills null values with the calculated average sales amount and drops the temporary column. This is efficient because it utilizes Snowpark's DataFrame operations. other options does not handle nulls or gives errors.
NEW QUESTION # 176
A data engineering team is using Snowpark Python to build a data pipeline. They need to create a User-Defined Function (UDF) that transforms a JSON string column representing customer information into a STRUCT type containing flattened fields for 'name', 'age', and 'city'. The UDF should handle null values gracefully and return NULL if the input JSON is invalid or if the 'name' field is missing. Considering performance implications and error handling, which of the following approaches is MOST optimal for defining and registering this UDF?
Answer: D
Explanation:
Option B is the most optimal. Using allows Snowpark to understand the schema of the returned data, enabling efficient type checking and query optimization. 'snowflake.snowpark.functions.parse_json' leverages Snowflake's internal JSON parsing capabilities, leading to better performance. Returning None from UDF handles nulls gracefully. Other options either involve less efficient StringType return types, manual VARIANT object creation which is less type-safe, or suggest stored procedures when a simple UDF is sufficient.
NEW QUESTION # 177
You are developing a Snowpark Python application that needs to process large datasets. You want to optimize performance by leveraging user-defined functions (UDFs) to perform complex calculations in parallel across the Snowflake data warehouse. Which of the following statements regarding Snowpark UDFs are TRUE?
Answer: B,C
Explanation:
Snowpark UDFs can be either scalar or vectorized, offering different performance tradeoffs. Vectorized UDFs are generally more efficient for large datasets as they process batches of rows. Snowpark UDFs do distribute the data and computation across multiple nodes automatically; however, the distribution strategy, while not directly controlled, is influenced by how the UDF is applied to the data and the inherent distribution of the underlying data itself. Python is the primary UDF language. Option A is false because UDFs are designed for parallel processing. Option C is not always true; custom channels might be necessary for specific dependencies. Option E is partially correct in the older releases but Python is used primarily now.
NEW QUESTION # 178
You are working with a Snowpark DataFrame 'sales_data' containing sales transactions. The DataFrame includes columns 'transaction_id' (STRING), 'product_id' (IN T), 'sale_date' (DATE), and 'sale_amount' (DOUBLE). You need to calculate the total sales amount for each product on a daily basis. Furthermore, you want to filter out any days where the total sales amount for a specific product is less than $50. Which of the following code snippets correctly achieves this using Snowpark Python?
Answer: C,D
Explanation:
Options A and B are correct. Both first group the data by 'product_id' and 'sale_date' and calculate the sum of 'sale_amount' for each group. They then filter the results to include only those rows where 'total_sales' is greater than 50. 'filter' and 'where' are interchangable. C would be invalid snowpark as you use 'having' after group_by. Option D and E would also be valid if the prompt asked for all days with a sale amount equal to greater than $50 not greater.
NEW QUESTION # 179
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