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Snowflake SPS-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowpark Concepts and Architecture | 25% | - Session management and connection
|
| Topic 2: Data Transformations and Operations | 35% | - Advanced operations
|
| Topic 3: Snowpark API and Development | 30% | - Python API fundamentals
|
| Topic 4: Performance and Best Practices | 10% | - Security and governance
|
Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:
1. Consider two Snowpark DataFrames, 'employees' and 'departments' , with the following schemas: 'employees': (employee_id: Integer Type, employee_name: StringType, department_id: Integer Type, salary: IntegerType) 'departments': (department_id: Integer Type, department_name: StringType, location: StringType) You want to find the highest salary within each department, along with the department name and location, and display the results in a Snowpark DataFrame. Which of the following Snowpark Python code snippets correctly achieves this?
A)
B)
C)
D)
E) 
2. Consider a scenario where you're developing a Snowpark stored procedure that accesses sensitive data'. Which of the following strategies, when used together, provide a comprehensive approach to secure this stored procedure and protect the underlying data?
Select all that apply:
A) Using 'EXECUTE AS OWNER and granting the 'SELECT privilege on the sensitive data tables to the stored procedure's owner role.
B) Masking sensitive data within the stored procedure using Snowflake's dynamic data masking policies.
C) Using 'EXECUTE AS CALLER and relying on the caller's privileges to access the data.
D) Implementing row-level security policies on the sensitive data tables.
E) Encrypting the stored procedure's code using AES encryption before deployment.
3. A data engineering team is developing a Snowpark application to process large volumes of data'. They aim to leverage session parameters for fine-grained control over query execution and resource allocation. Which of the following methods is the MOST efficient and secure way to set session parameters, ensuring that sensitive information like warehouse size and query timeouts are dynamically adjusted based on the workload without hardcoding values in the application?
A) Leveraging Snowflake's parameter hierarchy by setting account-level parameters and inheriting them into the Snowpark session.
B) Using environment variables to store parameter values and accessing them via 'os.environ['WAREHOUSE SIZET within the Snowpark application.
C) Utilizing Snowpark session builder to set parameters using a dictionary read from a secure configuration file, then overriding defaults based on workload characteristics. Example: 'session = Session.builder.configs(config).config('warehouse', workload_optimized_warehouse).create()'
D) Using the 'snowsqr CLI tool to pre-configure session parameters before running the Snowpark application.
E) Directly using 'session.sql('ALTER SESSION SET QUERY _ TIMEOUT = for each session.
4. A Snowpark application needs to dynamically switch between different Snowflake accounts based on the environment (development, staging, production). Which of the following approaches provides the MOST secure and maintainable way to manage account credentials without hardcoding them in the application? Assume that deployment will occur via docker, Kubernetes or other modern deployment practices.
A) Store credentials in separate .env' files for each environment and load the appropriate file based on an environment variable indicating the current environment.
B) Store credentials in environment variables managed by the deployment platform (e.g., Kubernetes secrets) and access them using
C) Use the Snowflake CLI configuration file ('-/.snowflake/config') and switch between named profiles based on an environment variable.
D) Hardcode credentials in the Snowpark application code and rely on network security to prevent unauthorized access.
E) Encrypt the credentials and store them in a configuration file that is decrypted at runtime using a key stored in a secure vault.
5. You are working with a Snowpark DataFrame called 'customer df that contains customer data, including a column named 'registration_date' of data type TIMESTAMP NTZ. You need to filter the DataFrame to only include customers who registered in the year 2023. Which of the following Snowpark code snippets represents the MOST efficient and correct way to accomplish this filtering, considering potential timezone issues?
A)
B)
C)
D)
E) 
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B,C,D | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: E |
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