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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Transformations and Operations	35%	- Advanced operations 1. Window functions and analytics 2. Pivot and unpivot transformations 3. Semi-structured data processing - DataFrame manipulation 1. Selection, projection, renaming, casting 2. Joins, unions, set operations 3. Filtering, sorting, grouping, aggregation - User-defined logic 1. Stored procedures with Snowpark 2. UDFs, UDAFs, UDTFs
Topic 2: Snowpark API and Development	30%	- Python API fundamentals 1. Data persistence and writing results 2. Column operations and functions 3. DataFrame creation from tables, views, SQL - Multi-language support 1. Environment setup and dependencies 2. Java and Scala API basics
Topic 3: Snowpark Concepts and Architecture	25%	- Session management and connection 1. Authentication and connection settings 2. Create and configure Snowpark sessions - Snowpark architecture and execution model 1. Client-side vs server-side processing 2. Transformations vs actions 3. Lazy evaluation and DAG execution
Topic 4: Performance and Best Practices	10%	- Security and governance 1. Data protection and compliance 2. Access control and permissions - Optimization techniques 1. Query pushdown and execution plans 2. Minimizing data movement 3. Caching and warehouse sizing

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q122-Q127):

NEW QUESTION # 122
Consider the following Snowpark Python stored procedure:

What steps are necessary to register this Python code as a stored procedure named 'GET ROW COUNT in Snowflake and allow users with the 'ANALYST' role to execute it, assuming the stored procedure will be created with the 'EXECUTE AS OWNER clause, and the table name parameter will be passed dynamically during invocation?

A. 1. create the stored procedure using 'CREATE OR REPLACE PROCEDURE RETURNS VARCHAR LANGUAGE PYTHON RUNTIME_VERSlON=3.8 HANDLER='my_sproc' EXECUTE AS OWNER$ 2. Grant 'USAGE privilege on the database and schema containing the stored procedure to the 'ANALYST role. 3. The 'ANALYST' role does not need any additional privilege if EXECUTE AS OWNER is set, as it's using the owner's access rights.
B. 1. create the stored procedure using 'CREATE OR REPLACE PROCEDURE RETURNS VARCHAR LANGUAGE PYTHON IMPORTS-('/path/to/dependencies.zip') EXECUTE AS OWNER;' 2. Grant 'USAGE-' privilege on the database and schema containing the stored procedure to the 'ANALYST role. 3. Grant ' EXECUTE privilege on the stored procedure to the 'ANALYST role.
C. 1. Create the stored procedure using 'CREATE OR REPLACE PROCEDURE GET ROW COUNT(VARCHAR) RETURNS VARCHAR LANGUAGE PYTHON IMPORTS-('/path/to/dependencies.zip') EXECUTE AS OWNER;' 2. Grant 'USAGE' privilege on the database and schema containing the stored procedure to the 'ANALYST' role. 3. Grant 'EXECUTE PROCEDURE privilege on the 'GET ROW COUNT' stored procedure to the 'ANALYST role.
D. 1. Create the stored procedure using 'CREATE OR REPLACE PROCEDURE GET ROW COUNT(VARCHAR) RETURNS VARCHAR LANGUAGE PYTHON EXECUTE AS CALLER;' 2. Grant 'USAGE privilege on the database and schema containing the stored procedure to the 'ANALYST role. 3. Grant 'SELECT' privilege on all possible tables referenced by 'table_name' to the 'ANALYST' role.
E. 1. Create the stored procedure using 'CREATE OR REPLACE PROCEDURE GET ROW COUNT(VARCHAR) RETURNS VARCHAR LANGUAGE PYTHON EXECUTE AS OWNER AS $$ import snowflake.snowpark as snowpark def snowpark.Session, table_name: str) str: df = session.table(table_name) count = df.count() return f" Table {table_name} has {count} rows." $$; 2. Grant 'USAGE privilege on the database and schema containing the stored procedure to the 'ANALYST role. 3. Grant 'EXECUTE PROCEDURE privilege on the 'GET ROW COUNT stored procedure to the 'ANALYST role.

Answer: C

Explanation:
Option A correctly outlines the process. First, the stored procedure is created with the correct syntax, including specifying the runtime version and handler, and using 'EXECUTE AS OWNER. Second, 'USAGE privilege is granted on the database and schema. Third, EXECUTE PROCEDURE' privilege must be explicitly granted to the 'ANALYST role. Option B attempts to embed the python code inline but doesn't include IMPORTS section if needed and its more common now to upload the Python code using create or replace procedure syntax. Option C is incorrect because you need to grant ' EXECUTE PROCEDURE privilege to the role in addition to Usage. Option D uses 'EXECUTE AS CALLER , which is not what the question specifies and requires granting 'SELECT privileges on the underlying tables, defeating the purpose of owner rights. 'EXECUTE' is not a valid privilege to grant on stored procedures in Snowflake (Option E), it has to be 'EXECUTE PROCEDURE.

NEW QUESTION # 123
Which of the following statements are correct regarding the authentication methods available when creating a Snowpark session?

A. Using username/password authentication is the most secure and recommended approach for production environments.
B. Snowflake supports MFA(Multi-Factor Authentication) using Web Browser Authentication, which needs to be enabled at an account level.
C. OAuth authentication requires pre-configuration in Snowflake and typically involves client IDs, client secrets, and refresh/access tokens.
D. OAuth with external Oauth providers (like Okta or Azure AD) is not supported in snowpark, and only username password can be used.
E. Key pair authentication uses a private key to establish a secure connection without needing to store passwords directly in the code.

Answer: B,C,E

Explanation:
Options B, C, and D are correct. OAuth provides a secure delegation mechanism. Key pair authentication is also a secure method, eliminating password storage in code. Snowflake Browser-Based MFA relies on web browser redirection. Option A is incorrect because username/password is generally less secure than key pair or OAuth. Option E is incorrect because External Oauth providers like Okta are Azure AD is supported for authentication using a federated OAuth flow.

NEW QUESTION # 124
You have a Pandas DataFrame named containing employee information including 'name' , 'department, and You want to create a Snowpark DataFrame named from this Pandas DataFrame and register it as a temporary view named 'TEMP EMPLOYEES. However, you need to ensure that any NULL values in the Pandas DataFrame are handled correctly when creating the Snowpark DataFrame. Which of the following code snippets achieves this, minimizes data transfer and provides best performance considering dataset size is large?

Answer: B

Explanation:
Using 'session.write_pandas' with is most efficient for large datasets. It leverages internal optimization within Snowflake for transferring data from Pandas DataFrames, and creating the temporary view directly avoids intermediate steps. Options A, C, and D create Snowpark DataFrames in memory first before potentially creating a temporary view, which is less optimized. Option B creates a permanent table not a temp view.

NEW QUESTION # 125
You are using Snowpark Python to build a data pipeline. You need to version control your Snowpark application and ensure that it is compatible with different Snowflake environments (development, staging, production). Which strategies and tools would be most effective for managing the Snowpark application's code, dependencies, and deployment process?

A. Rely solely on Snowflake's built-in Python interpreter and avoid using any external libraries or dependencies to simplify versioning and deployment.
B. Use a Git repository to manage the Snowpark Python code, a dependency management tool like Poetry or pip to handle dependencies, and a CI/CD pipeline (e.g., using Jenkins or GitLab CI) to automate deployment to different Snowflake environments.
C. Copy and paste the Python code between different Snowflake environments as needed, manually installing any required dependencies.
D. Store the Python code directly in Snowflake stages and use Snowflake's versioning capabilities to manage different versions.
E. Package all Snowpark code into a single ZIP file and manually upload it to each environment.

Answer: B

Explanation:
Using a Git repository for version control, a dependency management tool like Poetry or pip, and a CI/CD pipeline is the recommended approach for managing Snowpark applications. This allows for proper version control, dependency management, and automated deployment across different environments. The other options represent less robust and error-prone approaches.

NEW QUESTION # 126
You are tasked with optimizing a Snowpark Python stored procedure that performs complex data transformations on a DataFrame. The procedure frequently encounters out-of-memory errors when processing large datasets. Which of the following strategies could you implement to mitigate these memory issues within the stored procedure's code ? Choose all that apply.

A. Leverage the 'sample()' function to work with a smaller subset of the data for testing and debugging.
B. Utilize the 'repartition()' or functions to control the number of partitions in the DataFrame and potentially reduce memory consumption per partition.
C. Implement data filtering and aggregation as early as possible in the transformation pipeline to reduce the size of the DataFrame.
D. Increase the warehouse size to provide more memory resources.
E. Use smaller data types (e.g., ' Int16' instead of ' Int64') where appropriate to minimize memory footprint.

Answer: B,C,E

Explanation:
Options B, C, and D directly address memory management within the stored procedure. Option B: 'repartition()' and allow you to control how the data is distributed across partitions. By adjusting the number of partitions, you can influence the amount of memory required to process each partition. Fewer, larger partitions can sometimes be problematic, whereas many smaller partitions might improve memory management but increase overhead. The best strategy depends on the specifics of the data and the transformations. Option C: Performing filtering and aggregation early reduces the volume of data that needs to be processed in subsequent steps, directly reducing memory consumption. This is a common optimization technique in data processing pipelines. Option D: Using smaller data types can significantly reduce memory footprint, especially when dealing with large datasets. Using 'Int16' when the range of values allows for it, instead of defaulting to Int64', can halve the memory usage for that column. Option A (increasing the warehouse size) provides more resources but doesn't address the underlying code inefficiencies that lead to memory errors. It's a valid approach, but should be considered after code-level optimizations. Option E(using 'sample()') is primarily for testing and debugging and does not solve the memory issue when processing the full dataset.

NEW QUESTION # 127
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