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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are using Snowpark for Python to process a large dataset of website clickstream data'. The dataset contains columns such as 'session_id', 'user_id', 'timestamp', 'page_url', and 'event_type' (e.g., 'click', 'pageview', 'purchase'). You want to identify fraudulent user sessions based on the following criteria: A user session is considered fraudulent if it contains more than 100 clicks within a I-minute window. A user session is considered fraudulent if it contains more than 5 purchase events within a 5-minute window. Which of the following code snippets demonstrates the most efficient way to identify fraudulent sessions using Snowpark for Python? Select two that apply.

A)

B)

C)

D)

E)

2. You have a Snowpark Python application that performs complex data transformations and machine learning model training. The data is stored in Snowflake tables. You notice that model training jobs, specifically those involving large feature sets and iterative algorithms, are consistently slow. The warehouse is already scaled to a LARGE size. Which of the following techniques, when applied individually or in combination, would MOST likely improve the performance of model training in Snowpark?

A) Use the 'sproc' decorator to define user-defined functions (UDFs) directly within Snowflake, leveraging the platform's optimized execution engine for specific computations.
B) Leverage external functions (IJDFs) to offload computationally intensive operations to specialized hardware outside of Snowflake.
C) Implement data skipping and filtering strategies to reduce the amount of data read during feature extraction and model training. Pre-aggregate when possible.
D) Cache intermediate Snowpark DataFrames using to avoid recomputation of common data transformations across multiple training iterations.
E) Scale the virtual warehouse UP to an XLARGE or larger. This provides more computational resources.

3. 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) Copy the contents of 'my_utils.py' directly into the stored procedure's code.
B) Install 'my_utils.py' on the Snowflake warehouse where the stored procedure will be executed.
C) 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.
D) Create a Snowpark DataFrame from 'my_utils.py' and pass it as an argument to the stored procedure.
E) Upload 'my_utils.py' to a Snowflake stage and specify the stage path in the 'imports' argument of the 'session.sproc.register' method.

4. 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) 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.
B) Run the stored procedure on a larger Snowflake warehouse to reduce execution time, regardless of potential idle time.
C) Convert the Python stored procedure to a SQL stored procedure to leverage Snowflake's SQL optimization engine.
D) Split the stored procedure into multiple smaller procedures and execute them sequentially on a smaller warehouse.
E) Register the stored procedure with the '@sproc' decorator without specifying any warehouse size, letting Snowflake automatically manage the warehouse.

5. You have a Snowpark DataFrame named 'sales df containing sales data for different products. The DataFrame includes columns product_id' (INTEGER), 'sale_date' (DATE), 'quantity' (INTEGER), and 'price' (FLOAT). You need to calculate the total revenue for each product on a monthly basis and store the result in a new DataFrame named Which of the following Snowpark code snippets will correctly achieve this, while maximizing performance and minimizing data shuffling?

A) ...python from snowflake.snowpark.functions import date_format, sum monthly_revenue_df = sales_df.withColumn('sale_month', date_format(sales_df['sale_date'], 'yyyy-MM')).groupBy('product_id', 'sale_month').agg(sum(sales_df['quantity'] sales_df['price']).alias('total_revenue'))
B) ...python from snowflake.snowpark.functions import to_date, date_trunc, sum monthly_revenue_df = sales_df.withColumn('sale_month', date_trunc('MM', sales_df['sale_date'])).groupBy('product_id', 'sale_month').agg(sum(sales_df['quantity'] sales_df['price']).alias('total_revenue'))
C) ...python from snowflake.snowpark.functions import month, sum monthly_revenue_df = sales_df.groupBy('product_id', month('sale_date').alias('sale_month')).agg(sum(sales_df['quantity'l sales_df['price']).alias('total_revenue'))
D) ...python from snowflake.snowpark.functions import date_part, sum monthly_revenue_df = sales_df.groupBy('product_id', date_part('month', 'sale_date').alias('sale_month')).agg(sum(sales_df['quantity'] sales_df['price']).alias('total_revenue'))
E) ...python from snowflake.snowpark.functions import monthname, sum monthly_revenue_df = sales_df.groupBy('product_id', monthname('sale_date').alias('sale_month')).agg(sum(sales_dfl'quantity'] sales_dfl'price']).alias('total_revenue'))

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