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| Section | Weight | Objectives |
|---|---|---|
| Performance Optimization and Best Practices | 20% | - Debugging and explain plans - Vectorized UDFs - Warehouse sizing for Snowpark - Query pushdown and optimization - Minimizing data transfer - Caching strategies |
| Snowpark API for Python | 30% | - Establishing connections and session management - User-Defined Functions (UDFs) and Stored Procedures - DataFrame creation and manipulation - Reading and writing data - Working with Semi-structured data |
| Data Transformations and DataFrame Operations | 35% | - Persisting transformed data - Complex data pipelines - Using built-in functions - Window functions - Filtering, Aggregating, and Joining DataFrames |
| Snowpark Concepts | 15% | - Transformations vs. Actions - Snowpark architecture and core concepts - Snowpark Sessions and connection management - Client-side vs. Server-side execution - Snowpark DataFrames and query plans - Stored procedures and conditional logic |
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NEW QUESTION # 371
You are using Snowpark Python to process a large dataset. You need to persist a DataFrame to a Snowflake table but want to ensure the operation is as efficient as possible and minimizes the data transfer overhead. The table already exists with the appropriate schema. Which of the following strategies would be the MOST efficient way to write the DataFrame to the existing table?
Answer: E
Explanation:
is generally the most efficient method for appending data to an existing Snowflake table using Snowpark. It directly inserts the data into the table without the overhead of creating a new table or overwriting the existing one. with the default 'append' mode (A) might work, but 'insertlnto' is more explicit and potentially optimized for this specific scenario. (B) would replace the entire table, which is not efficient if you only want to add new data. Creating a temporary table and then using 'CREATE OR REPLACE TABLE AS SELECT (D) involves unnecessary steps and data transfer. Writing to a stage and then using 'COPY INTO' (E) is also less efficient than directly inserting the data using Snowpark.
NEW QUESTION # 372
You are tasked with processing a Snowpark DataFrame named 'orders df that contains order information. The DataFrame includes the following columns: 'order _ id' (INTEGER), 'customer_id' (INTEGER), 'order_date' (DATE), 'order_total' (STRING), and 'discount_code' (STRING). The 'order_total' column contains values with leading dollar signs and commas (e.g., '$1 ,234.56'). The column can contain codes like 'SAVEIO', 'SAVE20', or be NULL. Your goal is to create a new DataFrame 'transformed_df that includes the following transformations: 1 . Convert the 'order_total' column to a numeric value (DOUBLE) after removing the dollar signs and commas. 2. Apply a discount based on the 'discount_code'. If the 'discount_code' is 'SAVEIO', apply a 10% discount; if it's 'SAVE20', apply a 20% discount. If the 'discount_code' is NULL or any other value, apply no discount (0%). 3. Calculate the 'final_total' after applying the discount. Which of the following code snippets correctly and efficiently implements these transformations using Snowpark?
Answer: D
Explanation:
Option A correctly implements all transformations efficiently using Snowpark functions. It converts 'order_totar to a numeric value, applies the discount based on the using 'when' , and calculates the 'final_totar. It avoids using IJDFs or 'collect' operations, which can be less efficient. Using 'lit' with numeric values isn't necessary or best practice, so option B is less preferable. Option C attempts to use a IJDF, which is less efficient than using built-in Snowpark functions. Also 'to_number' and for IJDF is not required. Option D calculates the discount amount directly instead of the discount rate. Option E attempts to use 'rdd.map' which is not available and it's generally advised against as it removes parallelism.
NEW QUESTION # 373
A data engineering team is using Snowpark Python to build a complex ETL pipeline. They notice that certain transformations are not being executed despite being defined in the code. Which of the following are potential reasons why transformations in Snowpark might not be executed immediately, reflecting the principle of lazy evaluation? Select TWO correct answers.
Answer: C,D
Explanation:
Snowpark employs lazy evaluation, which means transformations are not executed until an action is performed on the DataFrame. This allows Snowflake to optimize the entire query plan before execution. Setting 'eager_execution' to True does NOT exist in Snowpark Python. Data size exceeding Snowflake's limits would result in an error, not skipped transformations.
NEW QUESTION # 374
You have a Snowpark DataFrame 'employees df representing employee data'. You need to update the 'salary' column for employees in the 'Sales' department by applying a 10% increase. Which of the following Snowpark code snippets correctly performs this update? Assume a Snowflake table named 'employees' exists and 'employees df' is correctly created from it.
Answer: A,B
Explanation:
Options C and E are correct. Snowpark does not have an 'update' method directly on DataFrames like some other Spark-based DataFrame implementations. The correct way to update the data is to use 'with_column' along with 'when' and 'otherwise' from 'snowflake.snowpark.functions' to conditionally update the 'salary' column based on the 'department'. The part is crucial to persist the changes back to the Snowflake table after the transformation. Option C imports the required functions using 'from snowflake.snowpark.functions import when, cor and Option E directly uses the dataframe to invoke function and column names. Option A and D are incorrect because Snowpark does not provide an 'update' or direct save after filtering like that for this type of update. Option B is also incorrect because the update function doesn't exist on dataframes.
NEW QUESTION # 375
A Snowpark application needs to manage multiple sessions concurrently, each connected to a different Snowflake warehouse for resource isolation. The application receives warehouse names dynamically at runtime. How can the application efficiently create and manage these sessions while ensuring proper resource cleanup?
Answer: B
Explanation:
Option B is the most reliable and recommended way to manage multiple sessions. It ensures resource isolation by creating separate sessions per warehouse. Closing each session properly, especially using a 'finally' block or context manager, prevents resource leaks and ensures sessions are closed even if errors occur. Option A can lead to unpredictable behavior if multiple threads or asynchronous tasks interact with the global session. Options C, D and E are generally incorrect or don't properly manage resource cleanup or isolation.
NEW QUESTION # 376
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