Snowflake SPS-C01 : Snowflake Certified SnowPro Specialty - Snowpark

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. 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) 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.
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) 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 .
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.

2. You are working with a Snowpark application designed to process data from an event table. While testing a complex transformation involving several joins and window functions, you encounter the following error: 'java.lang.OutOfMemoryError: Java heap space'. The application uses Snowpark DataFrames and is running on a reasonably sized virtual warehouse. What is the MOST likely cause of this error in the context of Snowpark and Snowflake?

A) The virtual warehouse is undersized for the volume of data being processed, leading to excessive spilling to disk and eventual memory exhaustion on the driver node.
B) There's a circular dependency in the DataFrame transformations, causing an infinite loop and memory leak.
C) An inefficient UDF (User-Defined Function) is consuming excessive memory within the Java runtime.
D) The Snowpark driver process is attempting to load the entire result set into memory, exceeding the available heap space.
E) Snowflake's internal query optimizer has generated a suboptimal execution plan, leading to excessive intermediate data materialization.

3. You are developing a Snowpark application to process sales data. The application uses a UDF that calls an external Python library with a large memory footprint. After deploying the application, you observe that the Snowflake warehouse frequently runs out of memory, causing the application to fail. Which of the following strategies would be MOST effective in mitigating this issue, while minimizing cost and maintaining performance? Assume the data volume is relatively large and the UDF is computationally intensive.

A) Modify the UDF to process data in smaller batches using a generator pattern, reducing memory consumption at any given time. Deploy the UDF with the same warehouse size.
B) Implement a caching mechanism within the UDF to store intermediate results and reduce the number of calls to the external library. Deploy the UDF with the same warehouse size.
C) Implement the Python UDF as a Snowpark Stored Procedure. Deploy the UDF with the same warehouse size.
D) Rewrite the UDF in Java using Snowpark API, which generally has a smaller memory footprint than Python. Deploy the UDF with the same warehouse size.
E) Increase the warehouse size to the largest available option. This will provide more memory to the UDE

4. You are using Snowpark to process a DataFrame 'employee df containing employee data, including 'employee_id', 'name' , 'department' , and 'salary'. You need to implement a complex data cleaning and transformation pipeline that involves the following steps: 1. Remove duplicate rows based on 'employee id'. 2. Fill missing 'salary' values with the average salary for the employee's department. 3. Standardize department names by converting them to uppercase. 4. Create a new column 'salary_range' based on the salary. if Salary less than 50k 'Low', greater than 50k and less than 100k 'Medium', greater than 100k 'High'. Which of the following code snippets MOST effectively combines these transformations into a single, readable, and efficient Snowpark pipeline? Assume you have a session object available named 'session' and import necessary modules from 'snowflake.snowpark.functions as F'

A)

B)

C)

D)

E)

5. You are developing a Snowpark stored procedure to perform sentiment analysis on customer reviews. You need to use the 'nltk' Python package, which is not a built-in package in Snowflake. You have already created a stage named 'my_stage' in Snowflake and uploaded the necessary nltk data files (e.g., 'vader_lexicon.zip') to the stage. Which of the following code snippets correctly configures the session and imports the required nltk components within the stored procedure?

A)

B)

C)

D)

E)

Solutions: