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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Snowpark Concepts and Architecture	25%	- Snowpark architecture and execution model 1. Transformations vs actions 2. Lazy evaluation and DAG execution 3. Client-side vs server-side processing - Session management and connection 1. Authentication and connection settings 2. Create and configure Snowpark sessions
Topic 2: Data Transformations and Operations	35%	- User-defined logic 1. UDFs, UDAFs, UDTFs 2. Stored procedures with Snowpark - Advanced operations 1. Window functions and analytics 2. Pivot and unpivot transformations 3. Semi-structured data processing - DataFrame manipulation 1. Selection, projection, renaming, casting 2. Joins, unions, set operations 3. Filtering, sorting, grouping, aggregation
Topic 3: Snowpark API and Development	30%	- Python API fundamentals 1. Data persistence and writing results 2. DataFrame creation from tables, views, SQL 3. Column operations and functions - Multi-language support 1. Java and Scala API basics 2. Environment setup and dependencies
Topic 4: Performance and Best Practices	10%	- Security and governance 1. Data protection and compliance 2. Access control and permissions - Optimization techniques 1. Query pushdown and execution plans 2. Caching and warehouse sizing 3. Minimizing data movement

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q261-Q266):

NEW QUESTION # 261
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 'SELECT privilege on the 'customer_data' table and the 'INSERT privilege on the 'metrics_table' table 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. 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.
E. Grant the 'USAGE privilege on the database and schema containing the 'customer_data' and 'metrics_table' tables to the role executing the stored procedure.

Answer: A,C,D

Explanation:
Options B, C, and D are correct. B is required to give the stored procedure permissions to read and write data to the appropriate tables. C: Making the SP permanent allows you to grant ownership to a specific role. D: The packages argument is essential for including any external Python dependencies needed by the function. Option A provides access to the database and schemas but does not grant access to the tables themselves. Option E provides a way to pass files that need to be used inside the function.

NEW QUESTION # 262
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. 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.
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. 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.
E. 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.

Answer: A,C,D,E

Explanation:
Options A, B, D and E are valid approaches given the constraints, since the task says enrich your dataframe, so we need some final column containing a score. Option A: The microservice approach, using 'call_udf and external function is a robust and scalable solution. It enables to leverage complex sentiment analysis models without directly integrating them into Snowflake. Option B: The Java UDF allows leveraging existing Java-based NLP libraries, extending the capabilities of Snowpark for sentiment analysis while adhering to the constraint of not using external Python libraries directly. Option D: Snowflake's External Functions are designed for calling external services. Passing 'review_text as an argument to pre-trained sentiment analysis model, and receive a result. Option E: This solution utilizes regular expression and UDFs. To search for sentiment indicative keywords and phrases and assigning the score using 'when' statements. Option C: This approach is the least efficient because it requires maintaining an internal vocabulary, which can be less accurate and harder to update compared to using pre-trained models or external services and it also does not generate a final new column to enrich the dataframe.

NEW QUESTION # 263
You are developing a Snowpark application that uses a UDTF written in Python to perform complex data transformations. The UDTF takes several input columns and returns multiple output columns. The data volume is very large. You observe performance bottlenecks during the UDTF execution. Which of the following strategies could you employ to optimize the performance of your UDTF? (Select TWO)

A. Reduce the number of input columns passed to the UDTF by performing some pre-processing outside the UDTF.
B. Use a scalar UDF instead of a UDTF to simplify the code and reduce overhead.
C. Employ vectorized operations within the UDTF using libraries like NumPy or pandas to process data in batches.
D. Avoid using UDTFs altogether and rewrite the transformation logic using built-in Snowpark DataFrame transformations, even if it makes the code significantly more complex.
E. Increase the warehouse size used for the Snowpark session to provide more computational resources.

Answer: C,E

Explanation:
Vectorized operations (B) allow the UDTF to process data in batches, significantly improving performance for large datasets. Increasing the warehouse size (C) provides more computational resources (CPU and memory) which directly benefit UDTF execution. Using scalar UDF is NOT a performance improvement strategy.

NEW QUESTION # 264
A Snowpark application is designed to process data residing in a Snowflake table called 'ORDERS'. The application needs to create a temporary view named 'TEMP ORDERS VIEW based on a filtered subset of this table. The view should only be accessible within the current Snowpark session and should be automatically dropped when the session ends. What is the MOST efficient and correct Python code snippet using Snowpark to achieve this?

Answer: A

Explanation:
Option A, , is the most efficient and directly creates a temporary view scoped to the session. The 'createOrRepIaceTempView' method is the correct Snowpark API call for creating such views. Options B and D, 'createOrReplaceView' with or 'createView' with , are not valid Snowpark API calls. Option C, createOrReplaceGlobalTempViews , creates a global temporary view, accessible across sessions, which is not the requirement. Option E is adding a redundant step by creating a dataframe from another dataframe.

NEW QUESTION # 265
You are developing a Snowpark application that needs to connect to Snowflake using programmatic access. You want to use a secure method of authentication. Which of the following methods, when passed as parameters to the 'snowpark.Session.builder.configS method, would be MOST secure and appropriate for production environments?

A. Using 'private_key' stored securely and referencing it using 'private_key_file'.
B. Passing the 'user' and 'password' directly, but retrieving the 'account' from an environment variable.
C. Setting the 'authenticator' parameter to 'snowflake' and rely on default Snowflake authentication mechanism assuming it setup correctly
D. Using 'oauth_access_token' obtained from an external OAuth server.
E. Passing the 'user', 'password', and 'account' parameters directly as strings.

Answer: A,D

Explanation:
Using 'oauth_access_token' and 'private_key' (especially when stored securely) are more secure than directly passing username and password. OAuth and Key Pair authentication are recommended for production environments because they avoid storing or transmitting passwords directly. Options A & B are vulnerable because they expose credentials directly in the code or configuration. Option E is incorrect because simply setting the authenticator does not ensure the user authentication will happen with secure methods. User must use Oauth or Key pair authentication for Production use case.

NEW QUESTION # 266
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