Try Snowflake : SPS-C01 valid & accurate questions and answers

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark DataFrame 'df that you want to persist as a Snowflake table. You need to ensure the following requirements are met: 1. The table should be created if it does not exist. 2. If the table exists, the new data should be merged with the existing data based on a 'primary_key' column. 3. If a row with a matching 'primary_key' already exists in the target table, update the existing row with the values from the 'df DataFrame. Otherwise, insert the row from 'df into the target table. Which of the following approaches can achieve this using Snowpark?

A) Use the method, specifying 'primary_key' in the options.
B) There is no direct way to achieve a merge operation directly using Snowpark DataFrame API without either custom stored procedure or staging of dataframe to new table, then running merge query.
C) Convert the Snowpark DataFrame to Pandas DataFrame, perform the merge operation using Pandas, and then write the Pandas DataFrame back to Snowflake.
D) Create a stored procedure that performs a MERGE statement in Snowflake, and call this stored procedure from your Snowpark code.
E) Perform a full outer join between the existing table and the DataFrame, then use

2. You have a Snowpark DataFrame named 'employee_df with columns 'employee_id', 'department', and 'salary'. You want to calculate the average salary for each department and add it as a new column named 'avg_department_salary' to the original DataFrame. Additionally, you want to sort the resulting DataFrame by department and then by salary in descending order. Which of the following Snowpark code snippets correctly implements this requirement?

A)

B)

C)

D)

E)

3. You are developing a Snowpark application to process customer reviews. You need to use a third-party sentiment analysis library, 'SentimentAnalyzer', which is NOT available in the Anaconda repository. You have the library JAR file stored in an internal artifact repository accessible via HTTP. Which of the following steps are necessary to make this library available to your Snowpark session?

A) Upload the JAR file to a Snowflake stage. Then use 'session.add_import' to make the file available in your Snowpark session.
B) Upload the JAR file toa Snowflake stage and register it as a Java UDF using CREATE FUNCTION.
C) Configure the Snowflake account-level parameter to point to the HTTP location of the JAR file. Then use session.add_import' to use it.
D) Use 'session.add_dependency('/path/to/SentimentAnalyzer.jar')' in your Snowpark Python code after uploading the JAR to an internal stage.
E) Create a conda environment that includes the JAR, upload it to a stage, and use the environment in Snowpark.

4. You are working with a Snowpark DataFrame containing product review data'. The DataFrame has a 'review_text' column containing unstructured text reviews. Your task is to perform sentiment analysis on these reviews using Snowpark for Python. However, you are restricted to using only Snowpark built-in functions and UDFs; you cannot use external libraries like NLTK or TextBlob directly within your Snowpark code. Given this contraint, what is a valid approach to enrich your dataframe?

A) Build a Snowpark Python UDF that uses regular expressions to identify keywords and phrases indicative of positive, negative, or neutral sentiment. Assign a score based on the presence and frequency of these keywords. You can then apply 'when' statements to get .
B) Develop a sentiment analysis microservice deployed outside Snowflake, then use Snowpark's 'call_udf function to invoke this service, passing the 'review_text' as input and receiving the sentiment score as output.
C) Create a Java UDF within Snowflake that utilizes a Java-based sentiment analysis library (e.g., Stanford CoreNLP) to process the 'review_text' and return the sentiment score. Then, call this Java UDF from your Snowpark Python code.
D) Leverage Snowflake's external function capabilities to call a pre-trained sentiment analysis model hosted on a cloud ML platform (e.g., AWS SageMaker, Azure Machine Learning), passing the 'review_text' as input and receiving the sentiment score as output.
E) Use Snowpark's 'transform' function with a custom Python transformer that leverages an internal vocabulary of positive and negative words to assign a sentiment score to each review based on word frequency. Then store the result in the column.

5. A data engineering team has created several Snowpark Python UDFs and UDTFs in the 'TRANSFORMATIONS' schema of the 'ANALYTICS' database. A data science team needs to use these functions in their data analysis notebooks. What is the MINIMUM set of privileges that must be granted to the data science team's role ('DATA SCIENTIST') to allow them to discover and execute these UDFs and UDTFs?

A) GRANT USAGE ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT USAGE ON SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;
B) GRANT USAGE ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT USAGE ON SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST; GRANT ALL PRIVILEGES ON ALL FUNCTIONS IN SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;
C) GRANT USAGE ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT USAGE ON SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST; GRANT EXECUTE ON ALL FUNCTIONS IN SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST,
D) GRANT ALL PRIVILEGES ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT ALL PRIVILEGES ON SCHEMA ANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;
E) GRANT EXECUTE ON ALL FUNCTIONS IN SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;

Solutions: