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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark Python application that reads data from a Snowflake table, performs a complex transformation using a User- Defined Table Function (UDTF), and then writes the transformed data back to a new Snowflake table. The UDTF is defined as follows:

You need to optimize the performance of this application. Which of the following strategies would be MOST effective in reducing the execution time of the UDTF?

A) Replace the UDTF with a standard User-Defined Function (UDF) as UDFs are inherently faster.
B) Use a vectorized UDTF instead of a standard UDTF, processing data in batches. (Assume 'vectorized=True' is a valid parameter for 'udf .)
C) Reduce the number of rows in the input table by applying a filter before calling the UDTF.
D) Cache the input DataFrame before applying the UDTF using 'df.cache()'.
E) Increase the warehouse size to the largest available option before running the Snowpark application.

2. You have a Snowpark DataFrame named 'customer df containing customer data, including sensitive information like credit card numbers in a column named 'credit card'. You need to persist this data to a Snowflake table named 'secure_customers'. What is the MOST secure and efficient way to achieve this, ensuring that the 'credit card' column is never exposed in plain text during the persistence process and also optimized for subsequent analytical queries?

A) Create a Snowpark DataFrame that uses a Secure View to only select the required columns excluding credit_card, and persist that to 'secure_customers' using
B) Persist 'customer_df directly to 'secure_customers' using after dropping the 'credit_card' column using 'df.drop('credit_card')'.
C) Use a UDF to encrypt the 'credit_card' column before persisting the DataFrame to 'secure_customers' using
D) Persist the 'customer_df to a temporary table using 'df.write.mode('overwrite').save_as_table('temp_customers')'. Then, create a new table 'secure_customers' from 'temp_customers' excluding the 'credit_card' column.
E) Create a masking policy in SnoMlake and apply it to the 'credit_card' column in the'secure_customers' table after persisting the 'customer_df using

3. You are setting up a VS Code development environment for Snowpark with the Snowflake extension. You want to ensure that you can securely authenticate to Snowflake and execute Snowpark code. Which of the following steps are essential to configure secure authentication within VS Code for Snowpark?

A) Install the Snowflake VS Code extension and configure the connection settings to use OAuth. Ensure the OAuth client and secret are properly configured in Snowflake and referenced in the connection settings.
B) Install the Snowflake VS Code extension and configure the Snowflake connection settings to use Key Pair authentication. Ensure the private key is securely stored and referenced in the connection settings.
C) Install the Snowflake VS Code extension and configure the Snowflake connection settings to use MFA. Ensure the username and password is provided with a valid MFA token.
D) Install the Snowflake VS Code extension and configure the Snowflake connection settings to use Snowflake Native Authentication. Ensure that the user has the required permissions to authenticate using this method.
E) Install the Snowflake VS Code extension and configure the Snowflake connection settings in the extension's configuration file using username and password.

4. You're building a Snowpark Python application that processes sensor data from various devices. The data arrives as a stream of JSON objects, each containing the device ID, timestamp, and sensor readings. You want to use a Streamlit application to visualize near real- time aggregates on the data'. You're aiming to create a Snowpark DataFrame from this data, perform transformations, and then serve this DataFrame to Streamlit. Which of the following approaches concerning creating the initial DataFrame from JSON data is generally the MOST efficient and scalable for handling such a stream of data?

A) Utilize Snowpipe with auto-ingest configured to load the JSON data into a raw data Snowflake table, and subsequently, establish a Snowpark DataFrame using 'session.table('raw_data_table')'. You can then apply necessary transformations using Snowpark.
B) Write each incoming JSON object to a temporary file in cloud storage (e.g., AWS S3 or Azure Blob Storage) and then periodically use 'session.read.json()' to create a Snowpark DataFrame from the files.
C) Iteratively append each JSON object to a Python list, then create a Snowpark DataFrame from the list using 'session.createDataFrame(list_of_json_objectsy.
D) Use Snowflake's Kafka connector to ingest the JSON data directly into a Snowflake table, and then create a Snowpark DataFrame from that table using 'session.table()'.
E) Read the JSON data directly from the stream into a Pandas DataFrame using , then convert the Pandas DataFrame to a Snowpark DataFrame using 'session.createDataFrame(pandas_df)'.

5. A data engineering team has developed a Snowpark Python application to process customer orders, enrich them with external data (e.g., geo location, weather) and update the Customer360 table. The application is deployed to a production environment. The application's latency has significantly increased over the last week. Your investigation reveals that the Snowflake warehouse used by the application is constantly switching between the 'Scaling Up' and 'Scaling Down' states. The team has set the Auto Suspend time to 5 minutes and Auto Resume to True. Assuming that the team hasn't changed the code, the external API or any parameter related to data ingestion, which combination of the following actions would MOST likely fix the warehouse instability issue and improve the performance of this Snowpark application in production without substantial cost increases?

A) Implement workload management and classification to ensure the Customer360 updates are prioritized over less important tasks and assigned to a dedicated resource pool.
B) Increase the MIN_CLUSTER_COUNT of the warehouse. This pre-warms clusters and helps the warehouse to quickly adjust to workload changes.
C) Change the scaling policy of the warehouse to 'ECONOMY', prioritizing cost efficiency over performance responsiveness.
D) Increase the Auto Suspend value from 5 minutes to 30 minutes. This will ensure that the warehouse remains active for a longer period, preventing frequent auto- suspends and subsequent resume operations.
E) Reduce the MAX CLUSTER COIJNT to limit the potential peak capacity of the warehouse, preventing excessive resource allocation.

Solutions: