Snowflake SPS-C01 real exam prep : Snowflake Certified SnowPro Specialty - Snowpark

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark Python UDTF named that performs complex data transformations and you want to share it securely with another Snowflake account. Select ALL the necessary steps and considerations to properly share this UDTF using Snowflake Secure Data Sharing.

A) Create a share and grant usage on the database containing the 'process_data' UDTF to the share.
B) Ensure the UDTF doesn't rely on any external packages or dependencies not available in the target account.
C) Grant SELECT privilege on the UDTF to the share.
D) Grant OWNERSHIP privilege on the UDTF to the share.
E) Create a secure UDTF and add it to share.

2. 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) 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.
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) 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.
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) 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.

3. You have a Snowflake table named 'RAW EVENTS with a large number of events data, containing columns like 'EVENT ID', 'TIMESTAMP, 'USER ID, and 'EVENT_TYPE. The 'EVENT TYPE column contains string values representing different event categories. You want to create a Snowpark DataFrame, but due to the table's size, you only want to sample a small portion of the data for initial exploration and testing. Which of the following code snippets MOST accurately and efficiently creates a sampled Snowpark DataFrame named 'sampled_df containing approximately 1% of the rows from the 'RAW EVENTS table?

A)

B)

C)

D)

E)

4. You are working with sensor data in Snowpark. Your data contains (Integer), 'timestamp' (Timestamp), 'temperature' (Double), and 'status' (String). You need to create a Snowpark DataFrame named representing this data'. Which of the following is the most efficient and type-safe way to create the DataFrame from a list of Python tuples using an explicitly defined schema, assuming you need to maintain maximum precision for temperature readings and that all data types should map to the most efficient and appropriate Snowflake data type?

A)

B)

C)

D)

E)

5. You are working with a Snowpark DataFrame 'sales_data' containing sales transactions. The DataFrame includes columns 'transaction_id' (STRING), 'product_id' (IN T), 'sale_date' (DATE), and 'sale_amount' (DOUBLE). You need to calculate the total sales amount for each product on a daily basis. Furthermore, you want to filter out any days where the total sales amount for a specific product is less than $50. Which of the following code snippets correctly achieves this using Snowpark Python?

A)

B)

C)

D)

E)

Solutions: