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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are working with a Snowpark DataFrame 'products_df' that contains product information, including 'product_name', 'category', and 'price'. You need to perform several transformations: 1. Rename the 'product_name' column to 'item_name'. 2. Create a new column 'discounted_price' by applying a 10% discount to the 'price' column. 3. Filter the DataFrame to only include products in the 'Electronics' category where the 'discounted_price' is less than 100. Which of the following code sequences correctly and efficiently performs these transformations in Snowpark?

A)

B)

C)

D)

E)

2. You have a Snowflake view named 'SALES SUMMARY VW' that joins several large tables and performs complex aggregations. You need to create a Snowpark DataFrame from this view Which of the following considerations are MOST important to ensure optimal performance and resource utilization when working with this DataFrame?

A) Avoid calling 'collect()' or 'toPandas()' on the entire DataFrame unless absolutely necessary, as these operations bring all data into the client's memory.
B) Always specify the schema explicitly when creating the DataFrame from the view to prevent Snowpark from inferring it.
C) Leverage Snowpark's lazy evaluation and pushdown optimization capabilities by performing transformations and filtering on the DataFrame before executing actions.
D) Materialize the view into a temporary table using 'CREATE TEMPORARY TABLE AS SELECT FROM SALES SUMMARY VW before creating the Snowpark DataFrame.
E) Use 'session.sql()' with the view's definition to create the DataFrame, as this is generally faster than 'session.table()' for views.

3. You are developing a Snowpark stored procedure in Python that utilizes the 'requests' library to fetch data from an external API. Your Snowflake account is configured to use Anaconda packages. You encounter an error indicating that the 'requests' library is not found. Which of the following steps are MOST effective in ensuring the 'requests' library is available to your stored procedure?

A) Enable Anaconda integration for your Snowflake account, ensuring 'requests' is available in the Snowflake Anaconda channel, and then create the stored procedure using 'imports=['snowflake://packages/requests/']'.
B) Specify the 'requests library in the stored procedure's 'packages argument during creation: 'CREATE OR REPLACE PROCEDURE
C) Include the 'requests' library directly in the stored procedure code using a base64 encoded string.
D) Manually upload the 'requests' library's ' .py' files to an internal stage and import them within the stored procedure.
E) Install the 'requestS library directly onto the Snowflake compute nodes using SnowSQL's command.

4. You are building a Snowpark application that uses a Python UDF to perform sentiment analysis on customer reviews. The UDF relies on a large pre-trained machine learning model loaded from a file. During execution, you encounter 'Out of Memory' errors within the UDF. Considering the constraints of the Snowpark execution environment and the need to optimize resource usage, which of the following steps is the MOST effective in addressing this issue and ensuring the application's stability and performance?

A) Break down the customer reviews into smaller chunks and process them in batches within the UDF, clearing the model from memory after each batch to reduce overall memory consumption.
B) Increase the overall size of the Snowflake warehouse to provide more memory for UDF execution. The Snowflake environment will automatically allocate more memory to UDFs when available.
C) Use Snowpark's 'sproc' to register the UDF as a stored procedure instead ofa UDF, as stored procedures typically have more memory allocated to them.
D) Implement lazy loading of the machine learning model within the UDF, ensuring that the model is loaded only when it's first needed, and then cached for subsequent calls within the same UDF invocation.
E) Optimize the model itself by reducing it's size through quantization or distillation, and re-upload the smaller model to the Snowflake stage for the UDF to use.

5. You have developed a Snowpark application that processes a large volume of customer interaction data'. The application uses a vectorized UDF to classify the sentiment of text-based interactions. Initial tests show the application is performing slowly. Which of the following strategies would be MOST effective for optimizing the performance of sentiment analysis using a vectorized UDF?

A) Increase the number of cores allocated to the virtual warehouse.
B) Implement mini-batching within the vectorized UDF to further optimize processing.
C) Convert the vectorized UDF to a SQL UDTF
D) Optimize the vectorized UDF code itself by reducing complex computations, memory allocations, and unnecessary function calls within the UDF function (e.g., using optimized libraries, avoiding unnecessary object creation). Also consider upgrading your pandas, scikit-learn, or transformer libraries.
E) Ensure the input DataFrame is sorted by the interaction date before applying the UDF.

Solutions: