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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question 1

You have a Snowflake table 'PRODUCT REVIEWS' with columns 'product_id', 'review_text', 'review_date', and 'sentiment_score'. You are using Snowpark to build a data pipeline that analyzes these reviews and identifies potentially malicious or spam reviews. Based on a machine learning model deployed as a Snowflake UDF, you have identified a set of 'product_id' values that are associated with malicious reviews, and are stored in a python list called 'malicious_product_ids'. You need to delete all rows from the 'PRODUCT REVIEWS' table where the 'product_id' is present in the list. Assume 'malicious_product_ids' contains thousands of product IDs. Which of the following approaches is the MOST efficient and scalable for deleting these records using Snowpark?

A. Option A
B. Option C
C. Option B
D. Option E
E. Option D

Question 2

You have a Python function, 'calculate metrics(df: snowpark.DataFrame, metric name: str) -> snowpark.DataFrame', that calculates various metrics on a Snowpark DataFrame. You want to deploy this function as a stored procedure in Snowflake. You need to ensure that the stored procedure has appropriate permissions to read data from a table named 'customer data' and write results to a table named 'metrics_table'. Which of the following steps are necessary to achieve this, assuming you are using the 'session.sproc.register' method?

A. Grant the 'USAGE privilege on the database and schema containing the 'customer_data' and 'metrics_table' tables to the role executing the stored procedure.
B. Specify the 'imports' argument in 'session.sproc.register' with the list of packages which are needed to run 'calculate_metrics' function.
C. Specify the 'packages' argument in 'session.sproc.register' to include any Python dependencies required by the 'calculate_metrics' function.
D. Grant the 'SELECT privilege on the 'customer_data' table and the 'INSERT privilege on the 'metrics_table' table to the role executing the stored procedure.
E. When registering the stored procedure using 'session.sproc.register' , specify the argument and provide a 'replace=True' if necessary. This will allow you to assign ownership of the stored procedure to a role with the necessary privileges.

Question 3

You are tasked with building a Snowpark application that processes sensor data. The data arrives continuously and is ingested into a Snowflake table called 'RAW SENSOR DATA'. You need to create a Snowpark DataFrame that applies a user-defined function (UDF) to each row to enrich the data. The UDF, named 'ENRICH SENSOR DATA, is written in Python and resides in a stage called 'UDF STAGE. The UDF takes three arguments: 'timestamp', and 'raw_value', all of which are STRING type in Snowflake. Which of the following code snippets correctly defines and calls the UDF using Snowpark?

A.

B.

C.

D.

E.

Question 4

You are tasked with optimizing a Snowpark Python application that performs complex geospatial calculations on a large dataset. The application experiences significant performance bottlenecks due to the computational intensity of the geospatial functions. Which of the following strategies would be MOST effective in improving performance?

A. Distribute the dataset into smaller chunks using partitioning strategies within the Snowpark DataFrame API and process them independently.
B. Rewrite the geospatial functions using native Python libraries within the Snowpark environment.
C. Increase the size of the virtual warehouse to a larger instance (e.g., from X-SMALL to LARGE).
D. Disable automatic query optimization features in Snowflake to gain more control over query execution.
E. Utilize user-defined functions (UDFs) written in Java or Scala and leverage vectorized UDFs where possible.

Question 5

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

Solutions: