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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. A Snowpark application is designed to process data residing in a Snowflake table called 'ORDERS'. The application needs to create a temporary view named 'TEMP ORDERS VIEW based on a filtered subset of this table. The view should only be accessible within the current Snowpark session and should be automatically dropped when the session ends. What is the MOST efficient and correct Python code snippet using Snowpark to achieve this?

A)

B)

C)

D)

E)

2. A Snowpark application needs to process large volumes of sensor data stored in a Snowflake table named , which includes columns , 'timestamp' , and The application must calculate a rolling average of for each over a 5-minute window. The data is not perfectly ordered by 'timestamp' within each 'sensor_id'. What is the MOST efficient and accurate way to implement this rolling average calculation using Snowpark?

A) Using a Window specification with 'orderBy('timestamp')' and 'rowsBetween(Window.unboundedPreceding, Window.currentRow)' in conjunction with and a UDF to manually calculate the rolling average within each group.
B) Using after applying a filter to select only the data within the 5-minute window, updating the filter for each new window.
C) Using a Window specification with 0)' and the 'avg()' window function. (Where 'to_seconds' converts a duration to seconds)
D) Implementing a Python UDTF (User-Defined Table Function) that iterates through the data for each calculates the rolling average manually, and emits the results as rows.
E) Using a Window specification with 'orderBy('timestamp')' and 'rowsBetween(Window.unboundedPreceding, Window.currentRow)' to calculate the cumulative average, then subtracting the average from 5 minutes ago. The query will then be grouped on the sensor id.

3. You have a Snowpark Python application that reads data from multiple Snowflake tables, performs complex transformations using UDFs, and writes the results to a new table. During peak hours, the application experiences performance bottlenecks. The Snowflake warehouse associated with the Snowpark session is already configured with the 'SNOWPARK OPTIMIZED warehouse type. Which of the following strategies, when implemented together, would BEST improve the application's performance?

A) Increase the size of the Snowpark-optimized warehouse and enable auto-scaling with a minimum of 1 node and a maximum of 2 nodes.
B) Enable query acceleration and increase the 'MAX CONCURRENCY LEVEL' session parameter.
C) Increase the size of the Snowpark-optimized warehouse, partition the input tables based on relevant join keys, and optimize UDFs for CPU efficiency.
D) Use the 'CACHE RESULT clause for frequently accessed data and rewrite UDFs in SQL instead of Python.
E) Increase the size of the Snowpark-optimized warehouse, utilize vectorized UDFs where applicable, and consider using Snowpark's optimized join operations (if available).

4. You are developing a Snowpark application that needs to connect to Snowflake using account identifiers. Your organization's Snowflake account is configured with federated authentication (Okta). Which of the following methods is the most secure and recommended way to establish a Snowpark session in this scenario, avoiding hardcoding credentials in your application and leveraging existing authentication mechanisms?

A) Pass username and password directly in the connection properties along with the account identifier.
B) Utilize Snowflake's support for OAuth and configure your application to acquire a token from Okta and use it to establish the Snowpark session using the 'authenticator parameter set to 'oauth'.
C) Use the connection parameter along with username and password directly in the connection properties.
D) Create a dedicated Snowflake user with restricted permissions and use its username and password directly in the connection string.
E) Store the username and password in environment variables and retrieve them in your Snowpark application to establish the session.

5. You are working with a Snowpark DataFrame 'transactions df that contains customer transaction data'. This data includes a 'transaction amount' column and a 'transaction date' column. You need to create a new feature called 'is weekend transaction' that indicates whether a transaction occurred on a weekend (Saturday or Sunday). Furthermore, some 'transaction_date' values are missing. You want to impute the missing dates with the mode (most frequent date) before determining if the transaction occurred on a weekend. Which of the following steps, when combined, provide the correct and most efficient approach to achieve this?

A) 1. Calculate the mode of the 'transaction_date' column using Snowpark functions. 2. Fill the missing values in the 'transaction_date' column with the calculated mode using 3. Create a UDF using datetime library that takes a date as input and returns True if it's a weekend (Saturday or Sunday), False otherwise. 4. Apply the UDF to the 'transaction_date' column to create the column.
B) 1. Calculate the mode of the 'transaction_date' column. 2. Filter all rows where 'transaction_date' is null and load that data into a temporary table. 3. Update all rows in original 'transactions_df from temporary table. 4. Create a UDF that takes a date as input and returns True if it's a weekend (Saturday or Sunday), False otherwise. 5. Apply the UDF to the 'transaction_date' column to create the column.
C) 1. Calculate the mode of the 'transaction_date' column. 2. Fill the missing values in the 'transaction_date' column with the calculated mode. 3. Create a UDF that takes a date as input and returns True if it's a weekend (Saturday or Sunday), False otherwise. 4. Apply the UDF to the 'transaction_date' column to create the 'is weekend transaction' column.
D) 1. Calculate the mode of the 'transaction_date' column using Snowpark functions. 2. Fill the missing values in the 'transaction_date' column with the calculated mode using 'fillna()'. 3. Use the 'dayofweek' function to determine the day of the week and create using a 'when' condition.
E) 1. Replace the null values in 'transaction_date' column with a constant string like '1900-01-01'.2. Create a UDF that takes a date as input and returns True if it's a weekend (Saturday or Sunday), False otherwise. 3. Apply the UDF to the 'transaction_dates column to create the column. 4. After applying the UDF convert back the replaced values in transaction_date to null.

Solutions: