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| Section | Objectives |
|---|---|
| Data Engineering with Snowpark | - Pipeline development
|
| User Defined Functions and Stored Procedures | - Extending Snowpark with custom logic
|
| Performance Optimization and Best Practices | - Efficient Snowpark execution
|
| DataFrame Operations and Data Processing | - Data transformation workflows
|
| Testing, Debugging, and Deployment | - Production readiness
|
| Snowpark Fundamentals | - Snowpark architecture and concepts
|
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NEW QUESTION # 359
You have a Snowpark Python UDTF that splits a comma-separated string into individual elements and returns them as rows. The UDTF is defined as follows:
Which of the following SQL queries correctly calls and uses this UDTF?
Answer: C
Explanation:
UDTFs must be called using the 'TABLE()' function in SQL. The 'LATERAL' keyword is used because the UDTF depends on the data from the preceding 'VALUES' clause. The select statement is 'lateral (select from values ('a,b,c') as t(columnl))' , which provides the input to the 'splitter_udtf. Options A and B are incorrect because they lack the proper and 'LATERAL' syntax or fail to provide an appropriate input using 'VALUES. C and D are incorrect since they dont select all fields to pass as parameter.
NEW QUESTION # 360
You're working with Snowpark and have a DataFrame 'df containing a column 'json_data' with JSON strings. Some of these JSON strings are invalid. You need to parse the valid JSON strings and extract a field named 'product_id' from them. Invalid JSON strings should result in a 'NULL' value for the extracted 'product_id'. Which of the following approaches is the MOST robust and efficient way to achieve this?
Answer: D
Explanation:
Option B is the most robust and efficient. handles invalid JSON strings gracefully by returning 'NULL'. The other options have drawbacks: Option A will throw an error if the JSON is invalid. Option C involves a UDF, which can be slower than built-in functions. Option D assumes valid json and uses the native notation which will error with invalid JSON data. Option E uses Regex which is not recommended and can have perfomance impact as well as not robust
NEW QUESTION # 361
You are developing a Snowpark application to ingest a large dataset into Snowflake. You have a DataFrame with a schema that matches the target table 'TARGET TABLE. Due to network constraints, you need to optimize the insertion process to minimize the number of API calls. Which of the following approaches would provide the MOST efficient way to insert the data?
Answer: E
Explanation:
The most efficient approach is option C: 'data_df.insert_into('TARGET_TABLE')'. This method leverages Snowpark's optimized data transfer mechanisms and performs bulk insertion using the underlying Snowflake engine which minimizes api calls and faster to insert the data. Option A is highly inefficient due to the overhead of constructing and executing individual SQL statements. Option B introduces Pandas, which is slower. Option D involves additional staging steps. Option E is manual chunking which is also slower.
NEW QUESTION # 362
You are developing a data pipeline using Snowpark and want to optimize the execution of multiple DataFrame transformations. Which of the following strategies or techniques can you employ to improve performance and reduce execution time? (Select all that apply)
Answer: A,E
Explanation:
Options C and E are correct. Option C, pushdown optimization by ensuring filter operations are applied as early as possible, is a key optimization technique. UDFs written in Scala can also be optimized by the compiler and Snowflake's engine. Option E, using , is the correct way to cache intermediate DataFrames for reuse, preventing redundant computations. Option A is incorrect; eagerly evaluating DataFrames with 'collect()' defeats the purpose of lazy evaluation and can significantly degrade performance. Option B is not directly applicable to Snowpark DataFrame transformations; 'CACHE RESULT is primarily for SQL queries executed outside of Snowpark DataFrame operations. Option D, is not a valid function in Snowpark API.
NEW QUESTION # 363
You have a Snowpark DataFrame 'df containing customer data with columns 'customer_id', 'signup_date' (TIMESTAMP NTZ), and 'country'. You need to create a new DataFrame that calculates the number of days since each customer signed up, but only for customers in 'USA' and 'Canada'. Furthermore, you want to filter out records where the signup was more than 365 days ago. Which of the following Snowpark code snippets will achieve this most efficiently?
Answer: D
Explanation:
Option E is the most efficient. It first filters the DataFrame by country using 'isin' , which is optimized for multiple values. Then, it calculates 'days_since_signup' using 'datediff and finally filters based on the number of days. Option A is correct but not as efficient as using 'isin-. Option B calculates 'days_since_signup' before filtering, which is less efficient. Option C uses 'to_number' which would result in the difference being represented in milliseconds and would require further conversion. Also using 'to_number' may lead to data loss. Option D has incorrect operator precedence in the 'where' clause, making it functionally wrong.
NEW QUESTION # 364
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