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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowpark Concepts | 15% | - Stored procedures and conditional logic - Snowpark architecture and core concepts - Client-side vs. Server-side execution - Snowpark DataFrames and query plans - Snowpark Sessions and connection management - Transformations vs. Actions |
| Topic 2: Data Transformations and DataFrame Operations | 35% | - Complex data pipelines - Window functions - Filtering, Aggregating, and Joining DataFrames - Persisting transformed data - Using built-in functions |
| Topic 3: Performance Optimization and Best Practices | 20% | - Warehouse sizing for Snowpark - Minimizing data transfer - Vectorized UDFs - Query pushdown and optimization - Caching strategies - Debugging and explain plans |
| Topic 4: Snowpark API for Python | 30% | - DataFrame creation and manipulation - Establishing connections and session management - Reading and writing data - Working with Semi-structured data - User-Defined Functions (UDFs) and Stored Procedures |
>> Latest SPS-C01 Test Questions <<
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NEW QUESTION # 154
You are tasked with automating the creation of Snowpark sessions using key pair authentication for multiple users. You have a function that retrieves connection parameters (account, user, private key, etc.) for each user from a secure configuration file. The private keys are stored in PEM format. However, some users' private keys are password-protected. Which of the following approaches ensures the secure and correct establishment of Snowpark sessions for all users, including those with password-protected private keys? Assume get_user config(username)' retrieves the user's configuration, including the private key and password (if any).
Answer: D
Explanation:
Option C is the most secure and correct approach. It handles both password-protected and non-password-protected private keys gracefully using the 'cryptography' library, without storing passwords in memory or requiring users to compromise their security. It attempts to load the private key with the password (if provided), and if no password is provided, it defaults to 'None'. Options A and D have security vulnerabilities associated with storing or prompting for passwords. Option B forces users to weaken security. Option E doesn't consider password protected private keys.
NEW QUESTION # 155
A Snowpark application is configured to connect to Snowflake using environment variables for authentication. However, the application frequently encounters intermittent connection errors. You suspect that the environment variables are not being correctly accessed by the Snowpark session builder. Which of the following actions could help diagnose and resolve the issue? (Select TWO)
Answer: D,E
Explanation:
Options A and E are the most helpful for diagnosing the issue. Printing the environment variable values (Option A) confirms that they are indeed set and have the expected values within the application's context. Checking the scope and accessibility of the environment variables (Option E) ensures that the Python process can access them. Option B, while a valid way to connect, defeats the purpose of using environment variables for security. Option C is also important, but less directly related to the environment variable issue; permissions are checked after connection. Option D does not help debug the environment variable issue, and the provided arguments are unrelated to environment variables.
NEW QUESTION # 156
You are tasked with creating a Snowpark UDTF (User-Defined Table Function) in Python to process a large CSV file stored in a Snowflake stage. Each row in the CSV represents a transaction, and you need to parse each row and extract specific fields based on a complex set of rules. The UDTF should return a table with the extracted fields. Consider the following code snippet:
Answer: D
Explanation:
While the provided code snippet might function, it's fundamentally inefficient. UDTFs in Snowpark are most performant when leveraging Snowpark DataFrame operations. Using 'pandas' inside the UDTF serializes and deserializes data between the Snowflake engine and the Python environment, introducing significant overhead. Options B, D and E are all generally incorrect as the code snippet provided is syntactically okay and contains the session parameter. A is incorrect due to performance and lack of optimization.
NEW QUESTION # 157
You are developing a Snowpark application to process sales data. The application uses a UDF that calls an external Python library with a large memory footprint. After deploying the application, you observe that the Snowflake warehouse frequently runs out of memory, causing the application to fail. Which of the following strategies would be MOST effective in mitigating this issue, while minimizing cost and maintaining performance? Assume the data volume is relatively large and the UDF is computationally intensive.
Answer: D
Explanation:
Option B is the most effective and cost-efficient solution. Processing data in smaller batches using a generator pattern within the UDF will significantly reduce memory consumption without requiring a larger, more expensive warehouse. Option A is a brute-force approach that increases costs unnecessarily. Option C might help, but batching provides more reliable memory control. Option D involves significant code rewriting and might not guarantee a substantial memory reduction, especially if the Python library is inherently memory-intensive. Option E doesn't inherently solve the memory issue if the underlying UDF's memory usage remains high.
NEW QUESTION # 158
You have a Snowpark DataFrame named 'orders_df with columns 'order_id', 'customer_id', 'order_date', and 'order_total'. You need to perform the following data enrichment steps using Snowpark for Python: 1. Calculate the 'year' from the 'order_date' column. 2. Calculate the 'discounted_total' by applying a discount of 10% if the 'order_total' is greater than $100, otherwise, no discount. 3. Create a new column 'customer_tier' based on the total spend per customer for each year. Customers with total spend greater than $1000 are 'Gold', between $500 and $1000 are 'Silver', and below $500 are 'Bronze'. Which of the following code snippets correctly implements these data enrichment steps using Snowpark (Assume the existence of a customer total spend df DataFrame).
Answer: C
Explanation:
Option B is the most efficient and correct. It calculates 'year' and 'discounted_total' using built-in functions. It then groups by 'customer_id' and 'year' to calculate 'total_spend'. Critically, it then assigns the 'customer_tier' using a series of 'when' statements directly within Snowpark, avoiding the performance overhead of a UDE Finally, it joins the customer tier information back to the original 'orders df. Option A implements Customer Tier calculation using UDF, Option C introduces Windowing without need. Options D, E are incomplete.
NEW QUESTION # 159
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