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최신 Snowflake Certification SPS-C01 무료샘플문제:

1. You have a Snowpark DataFrame named and want to create a stored procedure that calculates the average purchase amount for each customer. The stored procedure should accept the DataFrame as input, perform the aggregation, and return a new DataFrame with the results. Which of the following code snippets BEST demonstrates how to correctly define and deploy this stored procedure?

A)

B)

C)

D)

E)

2. 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 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'))
B) ...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'))
C) ...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'))
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 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'))

3. You have a Snowpark DataFrame containing sensor data'. You need to write this data to a Snowflake stage 'sensor_stage' , creating a new set of files every hour based on the 'timestamp' column (data type: Timestamp). You also want to ensure that the file names include the hour of the timestamp and are written in Avro format with Zstandard compression. The directory structure on the stage should reflect the hourly partitioning. Which of the following approaches offers the most efficient and scalable way to achieve this, while minimizing the number of files written per hour?

A) Create a new DataFrame by adding an 'hour' column extracted from the 'timestamp' column. Then use 'df.write.partitionBy('hour').format('avro').option('compression', 'zstd').mode('append').save('@sensor_stage/')'.
B) Create a view on top of the data and schedule a task which create file in avro with zstd compression by running the select statment with group by hour.
C) Define a stored procedure that iterates through hourly intervals, filters the DataFrame based on the current hour, and writes the filtered DataFrame to the stage using 'df.write.format('avro').option('compression', 'zstd').mode('append').save(f'@sensor_stage/hour={current_hour}/')'
D) Write a Python script that connects to Snowflake, retrieves the entire DataFrame, iterates through each row, determines the hour from the 'timestamp', and writes each row to a separate Avro file named after the hour in the 'sensor_stage'
E) Using scala user defined function (UDF) for write dataframe into stage in avro file format partitioned by Hour and calling it in snowpark dataframe.

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

5. You have a DataFrame 'df containing user profile data'. A column named "profile" stores JSON objects with potentially missing fields. These objects might include 'name', 'age', 'location', and 'preferences'. You need to extract the user's name and age. If 'age' is missing, you want to default to 0. Furthermore, you want to filter out any rows where the 'location' field is an empty string. Which combination of Snowpark code snippets will achieve this? (Select all that apply)

A)

B)

C)

D)

E)

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