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Snowflake SPS-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Performance and Best Practices10%- Optimization techniques
  • 1. Query pushdown and execution plans
  • 2. Minimizing data movement
  • 3. Caching and warehouse sizing
- Security and governance
  • 1. Access control and permissions
  • 2. Data protection and compliance
Topic 2: Snowpark Concepts and Architecture25%- Session management and connection
  • 1. Authentication and connection settings
  • 2. Create and configure Snowpark sessions
- Snowpark architecture and execution model
  • 1. Lazy evaluation and DAG execution
  • 2. Transformations vs actions
  • 3. Client-side vs server-side processing
Topic 3: Data Transformations and Operations35%- Advanced operations
  • 1. Semi-structured data processing
  • 2. Pivot and unpivot transformations
  • 3. Window functions and analytics
- User-defined logic
  • 1. UDFs, UDAFs, UDTFs
  • 2. Stored procedures with Snowpark
- DataFrame manipulation
  • 1. Filtering, sorting, grouping, aggregation
  • 2. Selection, projection, renaming, casting
  • 3. Joins, unions, set operations
Topic 4: Snowpark API and Development30%- Python API fundamentals
  • 1. Column operations and functions
  • 2. DataFrame creation from tables, views, SQL
  • 3. Data persistence and writing results
- Multi-language support
  • 1. Environment setup and dependencies
  • 2. Java and Scala API basics

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question #1

You have developed a Snowpark Python application that needs to connect to an external REST API to enrich data during a transformation. The API requires authentication using an API key stored securely. Which of the following approaches is the MOST secure and recommended way to manage the API key within the Snowpark environment?

  • A. Store the API key in a Snowflake Secret Object and retrieve it within the Snowpark Python code using the function.
  • B. Store the API key as an environment variable within the Snowflake session.
  • C. Hardcode the API key directly into the Snowpark Python code.
  • D. Store the API key in a secure vault outside of Snowflake and retrieve it using a custom Snowflake external function.
  • E. Encrypt the API key using a third-party encryption library and store it in a Snowflake table.
Answer: A

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Question #2

You've written a Snowpark stored procedure in Python that utilizes a custom Python library named 'my utils.py' containing utility functions for data transformation. This library is not available in Anaconda'. You want to deploy the stored procedure and ensure that the custom library is accessible during execution. Which is the most appropriate way to operationalize your Snowpark stored procedure with the custom library?

  • A. Upload 'my_utils.py' to a Snowflake stage and specify the stage path in the 'imports' argument of the 'session.sproc.register' method.
  • B. Package 'my_utils.py' into a wheel file and upload it to a Snowflake stage. Then, add the stage path to the 'packages' argument of the 'session.sproc.register' method.
  • C. Create a Snowpark DataFrame from 'my_utils.py' and pass it as an argument to the stored procedure.
  • D. Install 'my_utils.py' on the Snowflake warehouse where the stored procedure will be executed.
  • E. Copy the contents of 'my_utils.py' directly into the stored procedure's code.
Answer: A

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Question #3

You have a Snowflake table 'orders_json' with a VARIANT column named "order details'. This column contains JSON objects, and one of the fields within these objects is an array called 'items'. You need to use Snowpark to flatten the 'items' array into rows, extracting the 'item_id' , 'item_name' , and 'quantity' from each item in the array. Which of the following Snowpark code snippets will correctly achieve this, assuming 'df is a DataFrame representing 'orders_json'?

  • A.
  • B.
  • C.
  • D.
  • E.
Answer: E

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Question #4

You have a Snowpark Python stored procedure named 'calculate_stats' that takes a table name as input and returns summary statistics. You need to modify the stored procedure to add a new optional parameter for specifying a filter condition. Which of the following SQL commands, used in conjunction with the Snowpark API for Python, is the MOST efficient way to alter the existing stored procedure without dropping and recreating it?

  • A.
  • B.
  • C.
  • D.
  • E.
Answer: C

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Question #5

You are optimizing a Snowpark application that performs complex data transformations on a large dataset. The transformation involves multiple joins and aggregations. You notice that the query execution time is excessive. Which of the following techniques would be MOST effective in improving the performance of this application, assuming you have the appropriate Snowflake role and privileges?

  • A. Increase the size of the Snowflake warehouse used for the computation. A larger warehouse provides more resources for parallel processing.
  • B. Disable result caching for the Snowflake session. This ensures that the most up-to-date data is always used, even if it slows down performance.
  • C. Rewrite the Snowpark code to use more UDFs (User-Defined Functions). UDFs are always more efficient than built-in functions.
  • D. Use the ' DataFrame.explain(Y method to analyze the query plan and identify potential bottlenecks. Optimize the code based on the query plan analysis, focusing on reducing data shuffling and improving join strategies.
  • E. Reduce the overall complexity of the query, but increase the number of queries being executed. This allows for a more incremental approach.
Answer: A,D

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