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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Objectives
Data Engineering with Snowpark	- Pipeline development 1. Batch processing workflows 2. Integration with Snowflake data pipelines
Performance Optimization and Best Practices	- Efficient Snowpark execution 1. Pushdown optimization concepts 2. Resource utilization tuning
Testing, Debugging, and Deployment	- Production readiness 1. Deployment strategies 2. Debugging Snowpark applications
User Defined Functions and Stored Procedures	- Extending Snowpark with custom logic 1. Stored procedures in Snowpark 2. Python UDFs
DataFrame Operations and Data Processing	- Data transformation workflows 1. Filtering, selecting, and aggregations 2. Joins and window functions
Snowpark Fundamentals	- Snowpark architecture and concepts 1. Snowpark APIs and supported languages 2. Snowflake execution model overview

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q28-Q33):

NEW QUESTION # 28
You are tasked with creating a Snowpark session that utilizes a specific Snowflake warehouse for all operations. Which of the following code snippets BEST demonstrates how to correctly specify the 'warehouse' parameter when creating a session using snowpark.Session.builder.configs'?

Answer: C

Explanation:
The correct parameter name for specifying the warehouse in the 'configs' dictionary is 'warehouse'. The other options either use incorrect key names (SNOWFLAKE_WAREHOUSE, snowflake.warehouse, WAREHOUSE_NAME) or an incorrect method call (.config instead of .configs). The code snippets provided demonstrate the correct and incorrect methods for specifying the warehouse parameter during Snowpark session creation. Option A correctly utilizes the 'warehouse' parameter within the 'configs' dictionary passed to the Session builder.

NEW QUESTION # 29
You are building a Snowpark application using the 'snowflake-cli' to manage Snowflake connections. You have configured multiple connection profiles using 'snowflake connection add'. Which of the following Python code snippets demonstrates the most efficient and idiomatic way to create a Snowpark session using a specific connection profile named 'my_profile' defined in your 'snowflake-cli' configuration? Assume snowflake-cli is correctly configured and authenticated.

Answer: E

Explanation:
The correct answer is D. is the most direct and idiomatic way to create a Snowpark session from a named connection profile defined in the 'snowflake-cli' configuration. This avoids manually parsing connection details or using environment variables. Option A uses , which is for loading connection parameters from a .env' file, not a snowflake-cli profile. Option B relies on environment variables, not the 'snowflake-cli' profile. Option C is overly complex, using 'subprocess' to execute the 'snowflake-cli' when Snowpark provides a direct method. Option E is incorrect because the 'profile' parameter is not a valid parameter for 'snowflake.connector.connect'

NEW QUESTION # 30
You are developing a Snowpark application that processes high-volume event data stored in a Snowflake table named 'raw events'. The application aggregates data by session ID. You observe significant performance degradation during peak hours. Analyzing Snowflake query history reveals that the 'session_id' column has high cardinality and data skew. Which of the following strategies, or combination of strategies, would be MOST effective in optimizing the aggregation performance?

A. Implement a custom UDF (User-Defined Function) in Python to perform the aggregation and then apply the 'GROUP clause in the Snowpark DataFrame.
B. Use a 'GROUP clause in the Snowpark DataFrame to perform the aggregation.
C. Use a 'GROUP BY' clause in the Snowpark DataFrame combined with a 'hint' to specify the ' for optimized parallel processing.
D. Increase the warehouse size to a larger tier (e.g., from X-Small to Small).
E. Pre-aggregate the raw event data into smaller batches using a scheduled task before the main Snowpark application runs, and then aggregate the pre- aggregated data in the Snowpark application.

Answer: C,E

Explanation:
Using BUCKET_ID hint improves parallel processing and mitigates data skew in Snowpark. Pre-aggregation reduces the amount of data processed by the Snowpark application, thus improving performance. Increasing the warehouse size (A) might help but doesn't address data skew. UDFs (D) can introduce overhead if not optimized. GROUP BY alone (B) will not address the data skew problem

NEW QUESTION # 31
You are working with Snowpark DataFrames representing sales transactions. The 'transactions df DataFrame contains recent transactions, while the 'sales_table' in Snowflake holds the historical sales data'. You need to merge the new transactions into the 'sales table', but you want to track which rows were inserted, updated, or left unchanged by the 'merge' operation. How can you capture this information using Snowpark and persist it to a separate table?

A. After the merge, query the 'sales_table' and compare it to a copy of the 'sales_table' taken before the merge. Identify inserted rows as those present in the new version but not in the old, and updated rows as those with different values in specific columns between the two versions. This strategy should be implemented with the help of external functions and UDFs, as it is an expensive operation and should not be computed with normal SQL.
B. It's not possible to capture the merge operation details (inserted/updated/unchanged rows) directly using Snowpark alone. You would need to implement custom logic outside of Snowpark to compare the data before and after the merge.
C. You can combine the 'returning' clause along with a stored procedure to retrieve the inserted/updated rows with their status and then use the stored procedure to write to a different tracking table using insert statement.
D. You can use the 'returning' clause within the merge statement to retrieve the impacted rows and their change status (INSERTED, UPDATED) and then write this data to a tracking table using 'write.saveAsTable' .
E. The merge operation automatically creates a system table or view that logs the details of each row that was inserted, updated, or left unchanged.

Answer: D

Explanation:
The 'returning' clause is a powerful feature of the 'merge' statement in Snowflake SQL. It allows you to capture the rows that were affected by the merge operation, along with details about the type of change (INSERTED, UPDATED, DELETED). In Snowpark, you can leverage this by including a 'returning' clause in your 'merge' statement and then use the returned DataFrame to write the data to a tracking table. This provides a direct and efficient way to monitor the impact of your merge operations. Therefore the correct answer is B.

NEW QUESTION # 32
You have a Snowpark Python application that performs several data transformations on a DataFrame representing customer transactions. The application is experiencing performance issues, and you suspect that some transformations are unnecessarily expensive. Which of the following techniques can MOST effectively optimize the performance of your Snowpark application, specifically focusing on minimizing data movement and leveraging Snowflake's query optimization capabilities?

A. Always use the largest available Snowflake warehouse size to ensure sufficient compute resources.
B. Explicitly call .cache()' on the DataFrame after each transformation to materialize intermediate results in memory.
C. Use User-Defined Functions (UDFs) written in Python for all transformations, regardless of their complexity.
D. Take the dataframe to Pandas dataframe as soon as possible in between transformations, since Pandas dataframes will be faster.
E. Leverage Snowpark's built-in DataFrame transformations (e.g., .groupBy()') to allow Snowflake to optimize the query execution plan. Avoid pulling large amounts of data into the client application for simple operations. Only call 'collect()' as the last and final option, as this is the most costly activity of all.

Answer: E

Explanation:
Snowpark is designed to push down computations to Snowflake, allowing Snowflake's query optimizer to handle the execution. Using Snowpark's built-in DataFrame transformations allows Snowflake to understand the intent and optimize the query accordingly. Materializing intermediate results using .cache()' (A) can lead to unnecessary data movement. Python UDFs (B) can be useful for complex logic but should be avoided for simple transformations as they bypass Snowflake's optimization capabilities and are generally slower than native SQL functions. Warehouse size (E) is a factor, but optimizing the query logic is more crucial. Using Pandas dataframe is also costly and performance heavy.

NEW QUESTION # 33
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