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| Section | Weight | Objectives |
|---|---|---|
| Performance Optimization and Best Practices | 20% | - Caching strategies - Debugging and explain plans - Vectorized UDFs - Warehouse sizing for Snowpark - Minimizing data transfer - Query pushdown and optimization |
| Data Transformations and DataFrame Operations | 35% | - Filtering, Aggregating, and Joining DataFrames - Window functions - Using built-in functions - Persisting transformed data - Complex data pipelines |
| Snowpark API for Python | 30% | - Establishing connections and session management - User-Defined Functions (UDFs) and Stored Procedures - Working with Semi-structured data - Reading and writing data - DataFrame creation and manipulation |
| Snowpark Concepts | 15% | - Client-side vs. Server-side execution - Stored procedures and conditional logic - Snowpark DataFrames and query plans - Transformations vs. Actions - Snowpark architecture and core concepts - Snowpark Sessions and connection management |
1. A data scientist is developing a Snowpark application that needs to authenticate to Snowflake using Key Pair Authentication. Which of the following steps are essential for configuring the Snowflake CLI to enable Key Pair Authentication and then correctly create a Snowpark session? (Select TWO)
A) Set the 'AUTHENTICATOR parameter in the Snowflake CLI configuration to 'EXTERNALBROWSER.
B) Generate an RSA key pair using 'ssh-keygen' and store the private key securely.
C) Configure the user in Snowflake to use the public key by executing 'ALTER USER SET RSA_PUBLIC_KEY="'.
D) Set the 'PRIVATE KEY parameter in the Snowflake CLI configuration to the path of the private key file.
E) Set the 'AUTHENTICATOR parameter in the Snowflake CLI configuration to 'snowflake'.
2. A data scientist has developed a Snowpark Python stored procedure named 'model_training'. This procedure utilizes a large machine learning model and requires significant compute resources. The data scientist wants to optimize the cost and performance of running this stored procedure. Which of the following strategies would be the MOST effective for achieving this goal?
A) Run the stored procedure on a larger Snowflake warehouse to reduce execution time, regardless of potential idle time.
B) Register the stored procedure with the '@sproc' decorator without specifying any warehouse size, letting Snowflake automatically manage the warehouse.
C) Specify a warehouse size using the 'warehouse' parameter within the '@sproc' decorator and leverage auto-suspend and auto-resume features to minimize costs when the procedure is idle.
D) Split the stored procedure into multiple smaller procedures and execute them sequentially on a smaller warehouse.
E) Convert the Python stored procedure to a SQL stored procedure to leverage Snowflake's SQL optimization engine.
3. You have a Snowpark DataFrame containing sales data with columns 'sale_date', and 'sale_amount'. You need to calculate the cumulative sales amount for each product over time, ordered by 'sale_date'. Which of the following Snowpark code snippets correctly implements this using window functions?
A)
B)
C)
D)
E) 
4. You are using Snowflake Notebooks to develop a Snowpark application and want to leverage a custom Python library that is not available in the default environment. What steps are necessary to make this library available within your Snowflake Notebook?
A) Install the library using pip in the Snowflake Notebook's terminal and then restart the Snowflake Notebook.
B) Create a conda environment specification file ('environment.yml') that includes the custom library, upload it to a Snowflake stage, and then create a new environment based on that file when creating or modifying the Snowflake Notebook.
C) Upload the Python library's ' .py' file directly to the Snowflake stage and import it using 'import sys; sys.path.append("); import
D) Install the library directly within the Snowflake Notebook using '!pip install
E) Create a deployment file using setup.py, upload deployment file to stage, and create function
5. You've created a Snowpark Python stored procedure designed to perform sentiment analysis on customer reviews stored in a Snowflake table. This procedure utilizes a third-party Python library, 'transformers', for its sentiment analysis model. You need to operationalize this stored procedure for scheduled execution. Which of the following options represents the MOST efficient and reliable approach for deploying and managing the 'transformers' dependency within the Snowflake environment for your stored procedure, minimizing deployment complexity and potential runtime errors?
A) Install the 'transformers' library on the Snowflake compute warehouse nodes directly using a startup script.
B) Upload the 'transformers' library as a zip file to a Snowflake stage and reference it in the 'imports' parameter when creating the stored procedure.
C) Include the 'transformers' library as a part of the task definition and make it available for the stored procedure that the task is invoking.
D) Create a Snowflake Anaconda channel, upload the 'transformers' library to this channel, and specify the channel in the stored procedure definition using the packageS parameter.
E) Include the 'transformers' library directly within the stored procedure's Python code as a string literal and execute it using or 'eval()'.
Solutions:
| Question # 1 Answer: B,C | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: D |
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