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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You need to create a Snowpark DataFrame using a SQL query. The query requires a user-defined variable (e.g., a date for filtering records). What are the correct and recommended ways to safely pass this variable into the SQL query when creating the DataFrame using 'session.sql()' to prevent SQL injection vulnerabilities?

A)

B)

C)

D)

E)

2. 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) Copy and paste the Python code between different Snowflake environments as needed, manually installing any required dependencies.
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) Package all Snowpark code into a single ZIP file and manually upload it to each environment.
D) Rely solely on Snowflake's built-in Python interpreter and avoid using any external libraries or dependencies to simplify versioning and deployment.
E) Store the Python code directly in Snowflake stages and use Snowflake's versioning capabilities to manage different versions.

3. You have a Snowpark DataFrame 'employees' with columns 'employee_id' (INT), 'name' (STRING), 'department' (STRING), and 'salary' (DOUBLE). You want to create a new DataFrame that contains the top 3 highest-paid employees within each department. Which of the following approaches is the MOST efficient and correct way to achieve this using Snowpark Python?

A)

B)

C)

D)

E)

4. You are tasked with optimizing a Snowpark Python application that performs complex geospatial calculations on a large dataset. The application experiences significant performance bottlenecks due to the computational intensity of the geospatial functions. Which of the following strategies would be MOST effective in improving performance?

A) Distribute the dataset into smaller chunks using partitioning strategies within the Snowpark DataFrame API and process them independently.
B) Disable automatic query optimization features in Snowflake to gain more control over query execution.
C) Rewrite the geospatial functions using native Python libraries within the Snowpark environment.
D) Utilize user-defined functions (UDFs) written in Java or Scala and leverage vectorized UDFs where possible.
E) Increase the size of the virtual warehouse to a larger instance (e.g., from X-SMALL to LARGE).

5. You're developing a Snowpark Python application to process log files stored in an external stage 's3_logs'. These logs are in plain text, with each line representing a log entry. You need to filter log entries based on a specific keyword and extract timestamps from the matching lines. Which of the following approaches, using and Snowpark DataFrames, will efficiently accomplish this, avoiding unnecessary data transfer to the client?

A) Create an external function that accesses the files directly from S3, performs the filtering and timestamp extraction, and returns the results. Then create a Snowpark DataFrame to invoke the external function.
B) Create a UDF that accepts a 'SnowflakeFile' object as input, reads the file line by line inside the UDF, filters lines based on the keyword, extracts timestamps, and returns a list of timestamps. Call this UDF on a Snowpark DataFrame created using 'session.read.option('PATTERN', ' .10g').format('CSV').load('@s3_logs'V.
C) Use 'session.read.option('PATTERN', ' to load all log files into a Snowpark DataFrame with a single column containing the log lines. Create a UDTF that reads a partition of the DataFrame, filters lines based on the keyword, extracts timestamps, and returns a table of timestamps.
D) Download each log file using 'SnowflakeFile.get' , read the file line by line in the client application, filter lines based on the keyword, extract timestamps, and then create a Snowpark DataFrame with the extracted timestamps.
E) Use pattern=' .log')' to load all log files into a Snowpark DataFrame with a single column containing the log lines. Then, filter the DataFrame using like' to find lines containing the keyword and extract timestamps using regular expressions in a subsequent select statement.

Solutions: