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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are developing a secure UDF in Snowpark Python that needs to access sensitive data stored in an internal stage. The UDF should be accessible to users without granting them direct access to the stage. Which of the following security measures and code snippets are required to achieve this, assuming the stage is already created?

A) Create a secure UDF and use a stored procedure owned by a role with access to the internal stage to retrieve data, passing the data to the UDF as an argument.
B) Create a secure UDF using the ' VOLATILE keyword, allowing it to access secured data with current user's permissions.
C) Create a secure UDF and use the function to access stage credentials within the UDF's handler function.
D) Create a UDF and grant USAGE on the stage to the role that owns the UDF.
E) Create an external function and grant access to the API integration that provides the security context.

2. You have a Pandas DataFrame 'products df containing product information that you want to upload to a Snowflake table named 'PRODUCTS' within the schema 'SALES'. The table might or might not exist. If it doesn't, you want to create it automatically. The products df DataFrame has a column 'product_name' that contains Unicode characters. Furthermore, you need to specify the column types directly as part of the operation. Which of the following options provides the most appropriate approach to achieve this with Snowpark?

A)

B)

C)

D)

E)

3. You have a Snowpark DataFrame 'products_df with columns 'product_id', 'category', and 'price'. You want to find the top 3 most expensive products within each category Which of the following Snowpark code snippets will accomplish this, using window functions?

A)

B)

C)

D)

E)

4. You are optimizing a Snowpark application that performs complex data transformations on a large dataset. The transformation involves multiple joins and aggregations. You notice that the query execution time is excessive. Which of the following techniques would be MOST effective in improving the performance of this application, assuming you have the appropriate Snowflake role and privileges?

A) Increase the size of the Snowflake warehouse used for the computation. A larger warehouse provides more resources for parallel processing.
B) Reduce the overall complexity of the query, but increase the number of queries being executed. This allows for a more incremental approach.
C) Disable result caching for the Snowflake session. This ensures that the most up-to-date data is always used, even if it slows down performance.
D) Use the ' DataFrame.explain(Y method to analyze the query plan and identify potential bottlenecks. Optimize the code based on the query plan analysis, focusing on reducing data shuffling and improving join strategies.
E) Rewrite the Snowpark code to use more UDFs (User-Defined Functions). UDFs are always more efficient than built-in functions.

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) 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.
B) 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.
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) 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.
E) 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.

Solutions: