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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Performance and Best Practices	10%	- Security and governance 1. Access control and permissions 2. Data protection and compliance - Optimization techniques 1. Query pushdown and execution plans 2. Minimizing data movement 3. Caching and warehouse sizing
Snowpark Concepts and Architecture	25%	- Snowpark architecture and execution model 1. Client-side vs server-side processing 2. Lazy evaluation and DAG execution 3. Transformations vs actions - Session management and connection 1. Authentication and connection settings 2. Create and configure Snowpark sessions
Data Transformations and Operations	35%	- Advanced operations 1. Semi-structured data processing 2. Pivot and unpivot transformations 3. Window functions and analytics - User-defined logic 1. Stored procedures with Snowpark 2. UDFs, UDAFs, UDTFs - DataFrame manipulation 1. Selection, projection, renaming, casting 2. Joins, unions, set operations 3. Filtering, sorting, grouping, aggregation
Snowpark API and Development	30%	- Multi-language support 1. Environment setup and dependencies 2. Java and Scala API basics - Python API fundamentals 1. DataFrame creation from tables, views, SQL 2. Column operations and functions 3. Data persistence and writing results

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q188-Q193):

NEW QUESTION # 188
You have a Snowpark DataFrame 'products_df with columns 'product_id', 'category', and 'price'. You want to find the top 3 most expensive products within each category Which of the following Snowpark code snippets will accomplish this, using window functions?

Answer: C

Explanation:
Option D correctly partitions the data by category, orders by price in descending order (most expensive first), assigns a rank using , and then filters for ranks less than or equal to 3. Option A misses the snowflake.snowpark.functions import, but functionally same as D. Option B orders by price in ascending order (cheapest first). Option C does not partition by category and Option E filters where rank < 3 instead of less than or equal to. D is most correct because of syntax and concept implementation, and will pass the code check

NEW QUESTION # 189
You have a Snowpark application that performs machine learning inference on a large dataset of images stored in Snowflake. The inference logic is implemented within a Python UDF that utilizes a pre-trained deep learning model. You notice that the inference process is slow and consumes a significant amount of resources. Which of the following optimization techniques would be MOST effective in improving the performance and reducing the resource consumption of this application?

A. Leverage the Snowpark Session object to manage the lifecycle and scope of the pre-trained model within the UDF, ensuring it is loaded only once per session.
B. Ensure the virtual warehouse used by Snowpark is configured with auto-scaling to dynamically adjust the compute resources based on the workload.
C. Persist the pre-trained deep learning model in a database table or stage and load it into the UDF's memory during each invocation to avoid repeated loading.
D. Use external functions instead of UDFs.
E. Optimize the UDF code to use batch processing techniques to process multiple images in a single call, reducing the overhead of UDF invocation.

Answer: A,B,E

Explanation:
Auto-scaling helps manage resources dynamically. Batch processing reduces UDF overhead by processing multiple images in one call. Storing the model in a stage requires repeated loading. Managing the model's lifecycle within the Snowpark Session prevents reloading the model. External functions require data transfer out of Snowflake.

NEW QUESTION # 190
You are developing a Snowpark application that uses a UDTF written in Python to perform complex data transformations. The UDTF takes several input columns and returns multiple output columns. The data volume is very large. You observe performance bottlenecks during the UDTF execution. Which of the following strategies could you employ to optimize the performance of your UDTF? (Select TWO)

A. Employ vectorized operations within the UDTF using libraries like NumPy or pandas to process data in batches.
B. Avoid using UDTFs altogether and rewrite the transformation logic using built-in Snowpark DataFrame transformations, even if it makes the code significantly more complex.
C. Use a scalar UDF instead of a UDTF to simplify the code and reduce overhead.
D. Increase the warehouse size used for the Snowpark session to provide more computational resources.
E. Reduce the number of input columns passed to the UDTF by performing some pre-processing outside the UDTF.

Answer: A,D

Explanation:
Vectorized operations (B) allow the UDTF to process data in batches, significantly improving performance for large datasets. Increasing the warehouse size (C) provides more computational resources (CPU and memory) which directly benefit UDTF execution. Using scalar UDF is NOT a performance improvement strategy.

NEW QUESTION # 191
You have created a Snowpark stored procedure in Python that accesses a Snowflake stage to read configuration files. To enhance security, you want to grant the stored procedure specific permissions to only read files from that stage, without granting broader account- level access. Which of the following approaches is the MOST secure and granular way to achieve this?

A. Grant the 'USAGE privilege on the stage to the role that owns the stored procedure.
B. Grant the 'SELECT privilege on the stage to the role that owns the stored procedure.
C. Create a stored procedure with 'EXECUTE AS CALLER and ensure the caller (user executing the procedure) has the necessary privileges.
D. Create a Snowflake custom role and grant the 'READ' privilege on the stage to this role, then grant this role to the stored procedure's owner.
E. Grant the 'READ privilege directly to the stored procedure using the 'ALTER PROCEDURE command.

Answer: C

Explanation:
Using 'EXECUTE AS CALLER ensures the stored procedure executes with the privileges of the user calling it. This is the most secure and granular approach because you don't need to grant any specific privileges to the stored procedure itself. The user calling it must already have the necessary permissions to access the stage.

NEW QUESTION # 192
You are tasked with optimizing a Snowpark application that processes sensor data'. The data includes timestamp, sensor ID, and sensor reading. Your initial implementation uses a regular Python UDF to calculate the moving average for each sensor. However, the processing time is significantly slow due to the large volume of data'. Which of the following strategies would be MOST effective in improving the performance of this calculation using vectorization?

A. Replace the Python UDF with a SQL UDF as SQL UDFs are inherently faster.
B. Rewrite the calculation logic using Snowpark's built-in aggregation functions instead of a UDF.
C. Convert the existing Python UDF into a vectorized UDF using the '@vectorized' decorator, ensuring the input and output are Pandas Series.
D. Convert the Python UDF to a Java UDF.
E. Increase the warehouse size without modifying the UDF code.

Answer: C

Explanation:
Converting the Python UDF to a vectorized UDF allows it to process data in batches (as Pandas Series), which significantly reduces the overhead of transferring data between Snowflake and the UDE While increasing warehouse size (C) can provide some performance gain, vectorization (B) directly addresses the inefficiency of processing individual rows. Using built-in aggregation (D) is also a good option if feasible, but if a custom moving average calculation is required, vectorized UDF is the best fit. SQL UDFs aren't always faster and don't inherently vectorize. Java UDFs may provide some improvement but are more complex to implement than vectorized Python UDFs.

NEW QUESTION # 193
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