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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. A data science team wants to operationalize a Snowpark Python UDF that performs sentiment analysis on customer reviews. The UDF, 'analyze sentiment(review_text)', is currently defined within a Snowpark session. Which of the following approaches is the MOST efficient and scalable way to deploy this UDF for real-time scoring of incoming review data in a Snowflake table named 'CUSTOMER REVIEWS'?

A) Package the 'analyze_sentiment' function as a stored procedure and execute it using Snowpark session.execute.
B) Call the 'analyze_sentiment' UDF directly within a Snowpark DataFrame transformation that reads from 'CUSTOMER_REVIEWS'.
C) Persist the Snowpark session with UDF definition using pickle and call it from another Snowpark session.
D) Register the 'analyze_sentiment' UDF as a persistent UDF in Snowflake and then call it from a SQL query that reads from 'CUSTOMER REVIEWS.
E) Create a Snowpark Dataframe that reads from 'CUSTOMER_REVIEWS , convert it to pandas dataframe and call analyze_sentiment function on pandas dataframe.

2. You are developing a Snowpark application that performs complex data transformations on a large dataset using a UDF written in Scala.
After deploying the application, you observe that the performance is significantly slower than expected. Analyzing the query history in Snowflake, you identify that the UDF execution time is unusually high. Which of the following actions would be MOST effective in improving the performance of the UDF, considering Snowpark's execution context and Snowflake's query processing?

A) Rewrite the Scala UDF using SQL stored procedure for better performance.
B) Change the UDF definition to return smaller data types if applicable.
C) Modify the Scala UDF to leverage Snowpark's vectorized UDF functionality (using 'VectorizedUDF) to process data in batches instead of row-by-row.
D) Increase the warehouse size to improve overall query processing capacity, even if the UDF code itself remains unchanged.
E) Increase the amount of memory allocated to the Snowpark session in the application code.

3. 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) 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) 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.
D) 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.
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.

4. You are building a Snowpark Python application to perform complex data transformations and want to leverage external packages not pre-installed in the Snowflake environment. You need to ensure these packages are available within your Snowpark session. Which of the following methods are valid for deploying and using these third-party packages within your Snowpark Python environment? (Select TWO)

A) Create a 'conda' environment file ('environment.yml') specifying the required packages and use the method to upload the environment definition. Snowflake will automatically install the packages within the session's environment.
B) Utilize Snowflake's Anaconda channel integration and specify the package names as strings in the method. Snowflake will automatically resolve and install the packages from the Anaconda channel.
C) Manually install the packages on the Snowflake compute pool nodes before starting the Snowpark session.
D) Deploy the required packages using the SnowCLl package management commands, and then the Snowpark session will be able to automatically use the deployed packages.
E) Use the 'session.addDependency()' method to upload individual '.py' files containing the package code directly to the Snowflake internal stage.

5. You are tasked with optimizing the performance of a Snowpark application that uses a UDF to perform complex image processing. The UDF is currently registered using 'session.udf.registeff. You observe that the UDF execution is slow, particularly when processing large batches of images. What steps could you take to potentially improve the performance of this UDF execution? Select all that apply.

A) Convert the UDF into a UDTF (User-Defined Table Function) if the image processing can benefit from row-by-row processing and aggregation.
B) Ensure that the size of the zipped file containing the UDF's dependencies is as small as possible to speed up deployment.
C) Use session.clear_packages() to clear the session packages when deploying UDF.
D) Ensure that the stage location specified during UDF registration is in the same region as the Snowflake account to minimize data transfer latency.
E) Increase the warehouse size used for running the Snowpark application to provide more computational resources for the UDF execution.

Solutions: