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| Section | Weight | Objectives |
|---|---|---|
| Performance Optimization and Best Practices | 20% | - Minimizing data transfer - Caching strategies - Vectorized UDFs - Warehouse sizing for Snowpark - Debugging and explain plans - Query pushdown and optimization |
| 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 |
| Data Transformations and DataFrame Operations | 35% | - Persisting transformed data - Complex data pipelines - Window functions - Filtering, Aggregating, and Joining DataFrames - Using built-in functions |
| Snowpark Concepts | 15% | - Transformations vs. Actions - Client-side vs. Server-side execution - Stored procedures and conditional logic - Snowpark architecture and core concepts - Snowpark Sessions and connection management - Snowpark DataFrames and query plans |
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NEW QUESTION # 30
You are working with a Snowpark DataFrame 'transactions df that contains customer transaction data'. This data includes a 'transaction amount' column and a 'transaction date' column. You need to create a new feature called 'is weekend transaction' that indicates whether a transaction occurred on a weekend (Saturday or Sunday). Furthermore, some 'transaction_date' values are missing. You want to impute the missing dates with the mode (most frequent date) before determining if the transaction occurred on a weekend. Which of the following steps, when combined, provide the correct and most efficient approach to achieve this?
Answer: E
Explanation:
Option B is the most efficient and utilizes Snowpark's built-in capabilities. It calculates the mode using Snowpark's aggregation functions, fills missing values using and leverages the function to determine weekend status without the need for a UDF. Option A creates a UDF which is less efficient than using a built-in function. Option C replaces with an arbitary string which is bad as its hardcoding and not efficient, after filling the value a UDF is made which is not efficient as well, Also after that the data has to converted back, thus option C is not correct. Options D is more complex as it utilizes temporary table which is not efficient. Option E create a UDF when snowpark provides readily available functions. so its less efficient.
NEW QUESTION # 31
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 # 32
A data engineering team is developing a Snowpark application to process large volumes of data'. They aim to leverage session parameters for fine-grained control over query execution and resource allocation. Which of the following methods is the MOST efficient and secure way to set session parameters, ensuring that sensitive information like warehouse size and query timeouts are dynamically adjusted based on the workload without hardcoding values in the application?
Answer: C
Explanation:
Option E is the most efficient and secure because it allows for a combination of pre-configured parameters from a secure source (like a configuration file) and dynamic overrides based on the specific workload. This ensures that the application can adapt to different processing needs without exposing sensitive information or hardcoding values. Account level parameters are too broad (D), Direct SQL execution is inefficient (A), Environment Variables are less secure (B), and CLI is not dynamic for in-application session settings (C).
NEW QUESTION # 33
You are developing a Snowpark Python application that connects to Snowflake using key pair authentication. You have the private key stored securely in an environment variable named 'SNOWFLAKE PRIVATE KEY. Which of the following code snippets correctly establishes a Snowpark session using this method, assuming all other necessary connection parameters (account, user, database, schema, warehouse) are also set as environment variables?
Answer: C
Explanation:
Option D is correct because it properly retrieves the private key from the environment variable, decodes the PEM format, and passes it to the connection parameters after converting the key object to a string. It uses the cryptography library to handle the private key securely, which is required for key pair authentication. The code snippet decodes the private key using cryptography library and creates a string, which then is passed to the connection parameters to create a session. Option A fails to use the cryptography library and just passes the private key directly. Option B is incorrect because it doesn't use the 'configs' method, which is the correct way to pass all connection parameters at once. Option C doesn't encode the private key properly, and does not convert to string after decoding using cryptography. Option E does convert to string, but is not secure as the private key is directly passed without decoding it using the cryptography library.
NEW QUESTION # 34
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: B,E
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 # 35
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