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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowpark API for Python | 30% | - Establishing connections and session management - Reading and writing data - Working with Semi-structured data - User-Defined Functions (UDFs) and Stored Procedures - DataFrame creation and manipulation |
| Topic 2: Data Transformations and DataFrame Operations | 35% | - Persisting transformed data - Window functions - Filtering, Aggregating, and Joining DataFrames - Complex data pipelines - Using built-in functions |
| Topic 3: Performance Optimization and Best Practices | 20% | - Query pushdown and optimization - Warehouse sizing for Snowpark - Caching strategies - Minimizing data transfer - Vectorized UDFs - Debugging and explain plans |
| Topic 4: Snowpark Concepts | 15% | - Snowpark architecture and core concepts - Stored procedures and conditional logic - Transformations vs. Actions - Client-side vs. Server-side execution - Snowpark Sessions and connection management - Snowpark DataFrames and query plans |
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NEW QUESTION # 212
A data scientist is developing a Snowpark application that needs to authenticate to Snowflake using Key Pair Authentication. Which of the following steps are essential for configuring the Snowflake CLI to enable Key Pair Authentication and then correctly create a Snowpark session? (Select TWO)
Answer: D,E
Explanation:
Key Pair Authentication requires generating an RSA key pair (Option A) and configuring the Snowflake user with the public key (Option D). Setting the 'AUTHENTICATOR parameter to 'snowflake' is the correct approach. 'EXTERNALBROWSER uses the web-based browser authentication, while the private key is specified within the connection parameters directly, not the Snowflake CLI config.
NEW QUESTION # 213
You are building a Snowpark application that processes a large number of PDF files stored in a Snowflake stage. You need to extract text from each PDF file using a Python UDF and store the extracted text in a Snowflake table. You are considering different approaches for loading the PDF files into the UDE Which of the following approaches would provide the BEST performance and scalability, while minimizing network traffic and memory usage?
Answer: D
Explanation:
Option C is the most efficient approach. 'snowflake.snowpark.files.SnowflakeFile' allows the UDF to directly access the PDF files stored in the Snowflake stage without transferring the entire file to the client. This minimizes network traffic and memory usage. Option A requires loading all PDF files into a pandas DataFrame, which can consume a significant amount of memory. Option B has issues relating to the file size and content restrictions and isn't suitable for many files. Option D involves downloading all files to a local directory, which is not scalable and introduces unnecessary overhead. Option E using 'GET OBJECT is outside the scope of the python api.
NEW QUESTION # 214
A Snowpark application is designed to process data residing in a Snowflake table called 'ORDERS'. The application needs to create a temporary view named 'TEMP ORDERS VIEW based on a filtered subset of this table. The view should only be accessible within the current Snowpark session and should be automatically dropped when the session ends. What is the MOST efficient and correct Python code snippet using Snowpark to achieve this?
Answer: E
Explanation:
Option A, , is the most efficient and directly creates a temporary view scoped to the session. The 'createOrRepIaceTempView' method is the correct Snowpark API call for creating such views. Options B and D, 'createOrReplaceView' with or 'createView' with , are not valid Snowpark API calls. Option C, createOrReplaceGlobalTempViews , creates a global temporary view, accessible across sessions, which is not the requirement. Option E is adding a redundant step by creating a dataframe from another dataframe.
NEW QUESTION # 215
A data engineering team is developing a Snowpark stored procedure to perform complex data transformations and load the results into a target table. They want to operationalize this procedure by scheduling it to run daily. Which of the following is the MOST reliable and scalable way to schedule the execution of this Snowpark stored procedure within Snowflake?
Answer: C
Explanation:
Snowflake Tasks are the recommended way to schedule stored procedures within Snowflake. They are a native Snowflake feature, providing scalability, reliability, and integration with Snowflake's monitoring and management tools. Airflow is a valid option, but adds external dependencies.
NEW QUESTION # 216
You are working with a Snowpark DataFrame named 'sales df' containing sales data including columns 'product id', 'sale date', and 'sale_amount'. You want to create a new DataFrame 'filtered df that only includes rows where the 'product id' is present in a list of approved product IDs and the 'sale_amount' is greater than the average sale amount. You have already calculated the average sale amount and stored it in a variable named 'avg_sale amount'. Which of the following code snippets correctly achieves this?
Answer: A,B,D
Explanation:
Options B, C, and E are correct. Option B uses chained .filter() methods which is valid. Option C uses chained .where() methods which is functionally equivalent to filter. Option E correctly applies both conditions within the filter. Option A is incorrect because the '&' operator has different precedence compared to the bitwise & operator and can cause issues with Snowpark type checking; also, it does not have parentheses for the individual condition. Option D is incorrect because .contains() is typically for checking if a string contains a substring or if an array contains a specific value, not for checking if a value is in a list.
NEW QUESTION # 217
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