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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are working with a Snowpark DataFrame called 'customer df that contains customer data, including a column named 'registration_date' of data type TIMESTAMP NTZ. You need to filter the DataFrame to only include customers who registered in the year 2023. Which of the following Snowpark code snippets represents the MOST efficient and correct way to accomplish this filtering, considering potential timezone issues?

A)

B)

C)

D)

E)

2. You are developing a Snowpark application to analyze website traffic data'. You have a DataFrame named 'website_logs' with columns 'user_id', 'page_url', and 'timestamp'. You need to create a new DataFrame that contains the count of distinct users who visited each page within a specific time window Consider the following (incomplete) Snowpark Python code:

Which of the following code lines, when inserted into the Complete the following line...' comment, will correctly calculate the approximate distinct user count for each page within the specified time window?

A) website_logs.groupBy('page_url', F.window('timestamp', '1 hour')).agg(F.countDistinct('user_id').alias('distinct_users'))
B) website_logs.groupBy('page_url').agg(F.countDistinct('user_id').alias('distinct_users'))
C) website_logs.with_column('distinct_users', F.approx_count_distinct('user_id').over(window_spec))
D) website_logs.with_column('distinct_users', F.count('user_id').over(window_spec))
E) _logs.with_column('distinct_users', F.countDistinct('user_id').over(window_spec)) website

3. You have a Snowflake table containing JSON data with nested arrays and objects representing website user interactions. You want to extract all 'product_id' values from within an array named 'viewed _ products' nested inside a 'session' object for each event, using Snowpark for Python. Assume the 'raw_events' table has a variant column called 'event_data". Which of the following Snowpark code snippets will correctly extract and flatten the 'product_id' values into a DataFrame?

A)

B)

C)

D)

E)

4. You are tasked with optimizing a Snowpark Python application that performs complex data transformations on a large dataset. The application is running slower than expected, and you suspect that data serialization and transfer between the Snowpark client and the Snowflake engine are bottlenecks. Which of the following strategies could you implement to improve performance? (Select all that apply.)

A) Convert all dataframes to Pandas dataframes locally and perform data manipulation with Pandas methods to take advantage of local resources.
B) Minimize the amount of data transferred between the client and the engine by pushing down as much computation as possible to Snowflake using Snowpark DataFrame operations.
C) Create and utilize temporary tables within Snowflake to store intermediate results of complex transformations.
D) Utilize smaller batch sizes when writing data back to Snowflake to reduce memory pressure on the client.
E) Increase the configuration parameter to maximize parallelism within the Snowpark engine without considering resources or potential bottleneck.

5. A data engineering team is developing a Snowpark stored procedure in Python to perform anomaly detection on time-series data stored in a Snowflake table named 'sensor_readingS. The stored procedure needs to efficiently process large volumes of data and return only the rows identified as anomalies. Which of the following approaches would provide the most performant and scalable solution for operationalizing this stored procedure?

A) Load the entire 'sensor_readings' table into a Pandas DataFrame within the stored procedure, perform anomaly detection using a Python library like 'scikit-learn' , and then create a Snowpark DataFrame from the filtered Pandas DataFrame to return the results.
B) Create a UDF with a Scala implementation and use it inside the Snowpark stored procedure to detect anomalies using the Scala implementation for increased processing power.
C) Use the Snowpark API to directly perform anomaly detection calculations (e.g., rolling statistics, z-score calculations) on the 'sensor_readings' table within the stored procedure, leveraging Snowpark's distributed processing capabilities, and then return the resulting Snowpark DataFrame containing only the anomalies.
D) Use the method to include a pre-trained anomaly detection model (pickled object) in the stored procedure's execution environment. Load the model, use it to predict on the data fetched using 'session.table(Y , and return a Snowpark DataFrame of anomalies.
E) Execute a SQL query from within the stored procedure using the Snowflake connector for Python to fetch the relevant data, then use a standard Python loop to iterate through the results and apply anomaly detection logic. Return the anomalous rows as a list of dictionaries.

Solutions: