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| Section | Weight | Objectives |
|---|---|---|
| Snowpark Concepts | 15% | - Client-side vs. Server-side execution - Stored procedures and conditional logic - Snowpark DataFrames and query plans - Snowpark architecture and core concepts - Transformations vs. Actions - Snowpark Sessions and connection management |
| Performance Optimization and Best Practices | 20% | - Debugging and explain plans - Warehouse sizing for Snowpark - Minimizing data transfer - Caching strategies - Query pushdown and optimization - Vectorized UDFs |
| Data Transformations and DataFrame Operations | 35% | - Complex data pipelines - Window functions - Filtering, Aggregating, and Joining DataFrames - Persisting transformed data - Using built-in functions |
| Snowpark API for Python | 30% | - DataFrame creation and manipulation - User-Defined Functions (UDFs) and Stored Procedures - Reading and writing data - Working with Semi-structured data - Establishing connections and session management |
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NEW QUESTION # 115
You have a Snowpark DataFrame 'sales df with columns 'product_id' (INTEGER), 'sale date' (DATE), and 'sale_amount' (DOUBLE). You need to filter the DataFrame to include only sales that occurred in the year 2023 and where the 'sale_amount' is greater than the average 'sale_amount' for that specific 'product id'. Which of the following Snowpark Python code snippets will correctly achieve this?
Answer: A
Explanation:
Option C is the most efficient and correct. It calculates the average sale amount per product ID using a window function and then filters the DataFrame based on both the year and the comparison with the average. It avoids joins and 'collect()' which can be inefficient. Option A doesn't import Window or properly use avg over a window, causing an error. Option B requires a join, which is less efficient. Option D uses "mean' which is available but 'avg' is preferrable. Option E 'collects data to the client which is also generally avoided in Snowpark.
NEW QUESTION # 116
You have a SQL query stored in a file named 'query.sqr which contains several complex analytical calculations. The query depends on a Snowpark 'session' object already established. You want to create a Snowpark DataFrame from the result of this query. Which of the following code snippets achieves this with optimal performance and readability, assuming correct file access permissions?
Answer: D
Explanation:
Option A provides the most straightfomard and efficient approach. It reads the SQL query from the file and directly creates a Snowpark DataFrame using 'session.sql(sql_query)'. Option B introduces Pandas, which is unnecessary and less efficient. Option C uses the Snowflake Connector outside of Snowpark's API, which is generally not the preferred approach. Option D has a non-existent function create_dataframe' , and Option E reads lines separately requiring a join which might be erroneous.
NEW QUESTION # 117
Consider the following Snowpark Python code snippet designed to calculate a custom metric on financial data, using a vectorized UDF for performance. Identify potential performance bottlenecks and recommend optimization strategies.
Which of the following actions (may be more than one) would MOST likely improve the performance of this Snowpark application?
Answer: A,B,D
Explanation:
Clustering by 'ticker_symbol' significantly optimizes the 'groupBy' operation by organizing data physically for efficient retrieval. Caching the result before writing to a table is crucial if the intermediate result 'result' will be used multiple times, avoiding redundant computations. Repartitioning by 'ticker_symbol' before grouping helps distribute data evenly, improving parallelism and locality. While Pandas is generally available, explicitly declaring it might be necessary in certain isolated environments, so B can be considered a good practice, but not as impactful. Changing FloatType to DoubleType (C) will not lead to performnace improvement. It only changes precision level which is not required here.
NEW QUESTION # 118
You are developing a Snowpark application in Python to process financial transactions. You're using a Snowpark DataFrame named 'transactions_df which contains sensitive financial data, including customer account numbers and transaction amounts. To comply with data privacy regulations, you need to mask the customer account numbers before performing any aggregations. The 'transactions_df DataFrame has a column named 'ACCOUNT NUMBER' (VARCHAR). You decide to use a User-Defined Function (UDF) to mask the account numbers using a cryptographic hashing algorithm. Which of the following approaches is the most secure and efficient way to define and use the UDF in Snowpark, ensuring the masking occurs within the Snowflake environment and minimizes the risk of exposing sensitive data?
Answer: C
Explanation:
Option C is the most secure and efficient because it uses Snowflake's built-in 'SHA2 function within a SQL UDF. This approach keeps the sensitive data within the Snowflake environment, minimizing the risk of exposing the data during transfer to and from external systems. Snowflake's built-in functions are also optimized for performance. Option A is less secure because it uses Python's 'hashlib' , which may not be as robust as Snowflake's built-in cryptographic functions. It also potentially exposes data to the Python environment. Option B can be secure, but it requires more effort to manage dependencies and ensure compatibility. SQL UDFs are generally simpler and easier to maintain for this type of task. Option D is not secure because simple string manipulation is easily reversible and does not provide adequate data protection. Option E is highly insecure because it involves extracting the sensitive data from Snowflake and processing it in your application, which significantly increases the risk of data exposure.
NEW QUESTION # 119
You are developing a Snowpark application that needs to access data from a Snowflake table called 'EMPLOYEES. You want to create a Snowpark DataFrame representing this table. However, you are facing issues with the connection and believe that the database, schema, or warehouse attributes may not be set up correctly for the session. Which of the following code snippets, used in conjunction, BEST demonstrates how to create a Snowpark session with robust error handling to identify and address potential connection issues before attempting to create the DataFrame?
Answer: A,D
Explanation:
The combination of option C and E provides the most robust solution. Option C validates the critical database, schema, and warehouse parameters before attempting to create the DataFrame. If any of these parameters are incorrect, the 'USE statements will fail, and the 'try...except' block will catch the exception. Then Option E specifically catches 'snowflake.connector.errors.ProgrammingError' which would be raised if database/schema/warehouse is not set up correctly. This allows targeted debugging. A and B only catch general errors after the fact, and D is uses deprecated use_database and use_schema calls.
NEW QUESTION # 120
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