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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Create and configure Snowpark sessions 2. Authentication and connection settings
Topic 2: Snowpark API and Development	30%	- Multi-language support 1. Java and Scala API basics 2. Environment setup and dependencies - Python API fundamentals 1. DataFrame creation from tables, views, SQL 2. Data persistence and writing results 3. Column operations and functions
Topic 3: Performance and Best Practices	10%	- Security and governance 1. Access control and permissions 2. Data protection and compliance - Optimization techniques 1. Minimizing data movement 2. Query pushdown and execution plans 3. Caching and warehouse sizing
Topic 4: Data Transformations and Operations	35%	- DataFrame manipulation 1. Joins, unions, set operations 2. Filtering, sorting, grouping, aggregation 3. Selection, projection, renaming, casting - User-defined logic 1. UDFs, UDAFs, UDTFs 2. Stored procedures with Snowpark - Advanced operations 1. Pivot and unpivot transformations 2. Window functions and analytics 3. Semi-structured data processing

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SPS-C01 Standard Answers - SPS-C01 New Study Materials

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q224-Q229):

NEW QUESTION # 224
You are building a Snowpark application to process sensitive data'. To enhance security, you want to leverage ephemeral sessions. Which configurations, passed to 'snowpark.Session.builder.configS , are required and sufficient to create an ephemeral session? Assume your Snowflake environment is properly configured to allow ephemeral sessions.

A. Setting the 'role' parameter to a role with appropriate privileges, 'database', 'schema', and 'warehouse'.
B. Setting the 'role' parameter to a role with appropriate privileges and 'database' and 'schema'.
C. Ephemeral sessions do not have specific configurations; they are automatically created by Snowflake when the application connects.
D. Only setting the 'role' parameter to a role with appropriate privileges.
E. Setting the 'role' parameter to a role with appropriate privileges and 'warehouse'.

Answer: A

Explanation:
Ephemeral sessions in Snowflake require that you specify a 'role', 'database', 'schema', and 'warehouse'. These parameters define the context within which the session will operate. Without these parameters, the session cannot be properly established. Although option A set role which is important for setting the context but it is insufficient. Ephemeral sessions are not automatic and require proper configuration.

NEW QUESTION # 225
You have created a Snowpark UDF that uses a custom Python module 'my_module.py', containing a function 'process data'. This module is not available through Anaconda'. You've packaged the module into a zip file named 'my module.zip'. What steps are necessary to deploy this UDF in Snowflake so that it can correctly use the 'my_module'?

A. Upload 'my_module.zip' to an external stage (e.g., AWS S3 or Azure Blob Storage). Configure Snowflake to access the external stage. Create the UDF, specifying the external stage path in the 'imports' argument.
B. Upload 'my_module.zip' to an internal stage. When creating the UDF, specify the stage path in the 'packages' argument. Within the UDF, modify 'sys.path' to include the path where Snowflake unpacks the zip file.
C. Upload 'my_module.zip' to an internal stage. When creating the UDF, specify the stage path in the 'imports' argument. No changes to sys.path are required within the UDF.
D. Upload 'my_module.zip' to an internal stage. When creating the UDF, specify the stage path in the 'imports' argument. Within the UDF, modify 'sys.path' to include the path where Snowflake unpacks the zip file.
E. Upload 'my_module.zip' to an internal stage, then create the UDF using 'session.add_import' within the UDF definition, specifying the stage path. No additional configuration is needed.

Answer: C

Explanation:
Option B is correct. You need to upload the zip file to an internal stage. The 'imports' argument during UDF creation tells Snowflake to unpack the zip and make the module available. Snowflake handles adding the necessary path to 'sys.path', so no manual modification is needed within the UDF. Specifying external stages is possible, but internal stages are preferred for security and performance. Option A is incomplete (doesn't specify how the import relates to the UDF), Option C is technically feasible with external stages but is less preferable. Option D is incorrect as it unnecessary to modify sys.path. Option E specifies 'packages' argument where you would normally include packages available through Anaconda.

NEW QUESTION # 226
You have a Snowpark DataFrame 'employees df representing employee data'. You need to update the 'salary' column for employees in the 'Sales' department by applying a 10% increase. Which of the following Snowpark code snippets correctly performs this update? Assume a Snowflake table named 'employees' exists and 'employees df' is correctly created from it.

A. Option D
B. Option B
C. Option C
D. Option E
E. Option A

Answer: C,D

Explanation:
Options C and E are correct. Snowpark does not have an 'update' method directly on DataFrames like some other Spark-based DataFrame implementations. The correct way to update the data is to use 'with_column' along with 'when' and 'otherwise' from 'snowflake.snowpark.functions' to conditionally update the 'salary' column based on the 'department'. The part is crucial to persist the changes back to the Snowflake table after the transformation. Option C imports the required functions using 'from snowflake.snowpark.functions import when, cor and Option E directly uses the dataframe to invoke function and column names. Option A and D are incorrect because Snowpark does not provide an 'update' or direct save after filtering like that for this type of update. Option B is also incorrect because the update function doesn't exist on dataframes.

NEW QUESTION # 227
You have two Snowpark DataFrames, 'dfl' and 'df2', representing customer data'. 'dfl' contains customer IDs and names, while 'df2' contains customer IDs and email addresses. You need to create a new DataFrame that contains all customer IDs, names, and email addresses, including customers present in only one of the DataFrames. Which Snowpark set operation and join type would be most appropriate for achieving this?

A. DISTINCT and CROSS JOIN
B. EXCEPT and RIGHT JOIN
C. INTERSECT and LEFT JOIN
D. UNION and FULL OUTER JOIN
E. UNION ALL and INNER JOIN

Answer: D

Explanation:
Option C is the correct answer. 'UNION' is used to combine the rows from both DataFrames, removing duplicate rows. T-ULL OUTER JOIN' is used to include all rows from both DataFrames, even if there is no matching customer ID in the other DataFrame. The combination of 'UNION' and FULL OUTER JOIN' ensures that all customers and their associated information are included in the resulting DataFrame. The other options would either result in only matching records, missing records, or incorrect combination.

NEW QUESTION # 228
A data engineering team has deployed a Snowpark Python application that reads data from a Snowflake table, performs several complex transformations using Snowpark DataFrames, and writes the results back to another Snowflake table. The team is concerned about the cost associated with the virtual warehouse used by the Snowpark application. Which of the following strategies would be MOST effective in minimizing the virtual warehouse costs while maintaining acceptable performance?

A. Implement a resource monitor to limit the credit consumption of the virtual warehouse used by the Snowpark application.
B. Use serverless compute service when possible to avoid managing warehouse.
C. Set the AUTO SUSPEND parameter of the virtual warehouse to the shortest possible duration (e.g., 60 seconds).
D. Optimize the Snowpark code to minimize data shuffling and reduce the amount of data processed.
E. Use the smallest possible virtual warehouse size (e.g., X-SMALL) and rely on Snowflake's automatic scaling capabilities to handle workload spikes.

Answer: A,B,D

Explanation:
Implementing a resource monitor (C) provides a hard limit on credit consumption, preventing unexpected cost overruns. Optimizing the Snowpark code (D) to reduce data shuffling and processed data directly reduces resource usage. Using serverless compute (E) can reduce warehouse management overhead and potentially lower costs, especially for intermittent workloads. Setting a very short AUTO_SUSPEND (A) can lead to frequent warehouse starts, increasing costs due to warm-up time. Using the smallest warehouse size (B) might not provide acceptable performance and could lead to longer processing times, potentially increasing costs overall. Code optimization and resource monitoring are critical for cost control.

NEW QUESTION # 229
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