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

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q143-Q148):

NEW QUESTION # 143
You are developing a secure UDF in Snowpark Python that needs to access sensitive data stored in an internal stage. The UDF should be accessible to users without granting them direct access to the stage. Which of the following security measures and code snippets are required to achieve this, assuming the stage is already created?

A. Create a secure UDF and use the function to access stage credentials within the UDF's handler function.
B. Create an external function and grant access to the API integration that provides the security context.
C. Create a UDF and grant USAGE on the stage to the role that owns the UDF.
D. Create a secure UDF and use a stored procedure owned by a role with access to the internal stage to retrieve data, passing the data to the UDF as an argument.
E. Create a secure UDF using the ' VOLATILE keyword, allowing it to access secured data with current user's permissions.

Answer: A

Explanation:
Secure UDFs are designed to execute with the privileges of the UDF owner, not the caller. allows secure access to stage credentials. Option B is incorrect because granting USAGE on the stage directly grants access to the data in the stage. Option C is a valid approach but unnecessary complex. Option D concerns external functions, not secure UDFs within Snowflake. Option E is incorrect because 'VOLATILE doesn't affect privilege handling.

NEW QUESTION # 144
A data engineering team has developed a Snowpark Python application to process customer orders, enrich them with external data (e.g., geo location, weather) and update the Customer360 table. The application is deployed to a production environment. The application's latency has significantly increased over the last week. Your investigation reveals that the Snowflake warehouse used by the application is constantly switching between the 'Scaling Up' and 'Scaling Down' states. The team has set the Auto Suspend time to 5 minutes and Auto Resume to True. Assuming that the team hasn't changed the code, the external API or any parameter related to data ingestion, which combination of the following actions would MOST likely fix the warehouse instability issue and improve the performance of this Snowpark application in production without substantial cost increases?

A. Increase the Auto Suspend value from 5 minutes to 30 minutes. This will ensure that the warehouse remains active for a longer period, preventing frequent auto- suspends and subsequent resume operations.
B. Increase the MIN_CLUSTER_COUNT of the warehouse. This pre-warms clusters and helps the warehouse to quickly adjust to workload changes.
C. Implement workload management and classification to ensure the Customer360 updates are prioritized over less important tasks and assigned to a dedicated resource pool.
D. Change the scaling policy of the warehouse to 'ECONOMY', prioritizing cost efficiency over performance responsiveness.
E. Reduce the MAX CLUSTER COIJNT to limit the potential peak capacity of the warehouse, preventing excessive resource allocation.

Answer: A,B

Explanation:
The 'Scaling Up' and 'Scaling Down' thrashing is likely caused by the warehouse suspending too quickly, leading to constant restarts as new requests arrive. Increasing the Auto Suspend time (Option A) prevents this frequent cycling. Increasing the MIN CLUSTER COUNT (Option B) makes more resources readily available, helping the warehouse respond faster to spikes in demand and reducing the need for scaling up. Workload management (Option C) is a good practice but may not directly address the root cause of the instability. Reducing MAX_CLUSTER_COUNT (Option D) could worsen the problem by limiting the warehouse's ability to handle peak loads. Changing to 'ECONOMY' scaling policy (Option E) would prioritize cost over performance, which is counter to improving performance.

NEW QUESTION # 145
You are developing a Snowpark stored procedure to perform sentiment analysis on customer reviews. You need to use the 'nltk' Python package, which is not a built-in package in Snowflake. You have already created a stage named 'my_stage' in Snowflake and uploaded the necessary nltk data files (e.g., 'vader_lexicon.zip') to the stage. Which of the following code snippets correctly configures the session and imports the required nltk components within the stored procedure?

Answer: C

Explanation:
Option E correctly adds the zipped nltk data file from the stage as an import, and then updates the NLTK_DATA environment variable to point to the /tmp directory where Snowflake unpacks the zip file. This ensures that nltk can find its data files. Option A and D attempt to use './nltk_data' which is incorrect as the file system is read-only, and Option B incorrectly uses sys.path.append and session.add_packages in wrong way as well as hard coded path. Option C fails as well with session.add_packages('snowflake-snowpark-python','nltk') incorrect syntax .

NEW QUESTION # 146
Consider the following Python code snippet using Snowpark:

Which of the following statements are true regarding this Snowpark code?

A. The code uses best practices by explicitly closing the Snowflake session, preventing resource leaks.
B. The code calculates the sum of the 'sales' column, grouped by 'category' , and saves the result to a new table named , overwriting it if it exists.
C. The code establishes a connection to Snowflake using the provided credentials.
D. The code will fail because password authentication is deprecated and replaced by Key Pair authentication.
E. The code reads data from a table named 'my_table' in Snowflake.

Answer: A,B,C,E

Explanation:
The code correctly connects to Snowflake, reads data from 'my_table', performs aggregation, and saves the result to 'agg_sales_table' with overwrite mode, and closes the session. While password authentication is discouraged, it is still supported. The other options accurately describe the code's functionality. Closing the session is a best practice to release resources.

NEW QUESTION # 147
You are developing a Snowpark stored procedure to perform data transformation. The procedure needs to handle both integers and strings as input parameters and return a DataFrame. Which of the following is the MOST efficient and recommended way to define the data types for the input parameters and the return value, ensuring proper data handling and type safety during deployment and execution?

A. Defining input parameter types using Python type hints (e.g., 'x: int, y: str') and the return type as 'List[Row]' without explicit registration. Snowpark automatically handles conversion.
B. Relying solely on Snowpark's implicit type inference for both input parameters and the DataFrame return value, avoiding explicit type hints or registration.
C. Using Python type hints for input parameters and relying on Snowpark's implicit type inference for the DataFrame return value.
D. Using Python type hints for input parameters and explicitly registering the stored procedure using 'sproc.register' and specifying the return type using with schema definition.
E. Explicitly registering the stored procedure with 'sproc.registeff and specifying the input types using 'Argument objects and the return type using with schema definition.

Answer: D

Explanation:
Explicitly registering the stored procedure with 'sproc.register' and defining the input types using Python type hints (e.g., 'x: int, y: str') and the return type using is the most robust approach. While type hints improve readability and allow for static analysis, registration with explicit return type schema enforces the structure of the returned DataFrame at deployment, ensuring type safety and preventing unexpected runtime errors. This is especially critical in production environments. Using implicit type inference or relying solely on Python hints can lead to unexpected issues if the data doesn't conform to the expected types.

NEW QUESTION # 148
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