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Snowflake SPS-C01 Exam Syllabus Topics:

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

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q257-Q262):

NEW QUESTION # 257
A data engineering team is developing a Snowpark application that processes large volumes of JSON data'. They have created a UDF using Python that parses JSON strings and extracts specific fields. They need to deploy this UDF and ensure it can handle malformed JSON without causing the entire Snowpark job to fail. Which of the following strategies BEST addresses both the deployment and error handling requirements?

A. Create a Python UDF using the 'snowflake-snowpark-python' library and register it in Snowflake. Implement error handling using Snowflake's built-in 'ERROR HANDLING' clause in the 'CREATE FUNCTION' statement. Package the Python code as a ZIP file and upload it to a stage, using the 'imports' clause in the UDF definition.
B. Create a Python UDF and register it in Snowflake, using 'try-except' block within the UDF to catch 'json.JSONDecodeError' exceptions. Package the Python code with the 'snowflake-snowpark-python' library.
C. Create an external function pointing to an AWS Lambda function that handles the JSON parsing. Configure the Lambda function to retry on failure, using SNS for notifications. No need to use ZIP file or any 'imports' clause.
D. Create a Python UDF using the 'snowflake-snowpark-python' library and register it in Snowflake. Implement error handling using 'try-except block within the UDF to catch 'json.JSONDecodeError' exceptions and return NULL. Package the Python code as a ZIP file containing any necessary dependencies and upload it to a stage, using the 'imports' clause in the UDF definition.
E. Create a Java UDF instead of Python. Java has better JSON parsing libraries. Upload the JAR file to a stage and register the UDF with 'imports' clause referencing the JAR file. Use try-catch for error handling.

Answer: D

Explanation:
Option E is the best approach. Python UDFs are well-suited for JSON parsing, especially when using libraries like 'json' . The 'try- except' block provides robust error handling for malformed JSON, ensuring that errors are gracefully handled by returning NULL, preventing the entire job from failing. Packaging the Python code, including dependencies in a ZIP file uploaded to a stage, and using the 'imports' clause ensures that the UDF has access to all necessary resources. Options A, B, C and D do not provide the most suitable combination of error handling and dependency management or aren't the recommended/performant approach. Using external functions (D) introduces extra latency and cost.

NEW QUESTION # 258
You're working with a Snowpark DataFrame named 'sales_df' that contains sales transaction data'. You need to create a new DataFrame that includes only the rows where the 'order_date' is within the last 30 days. The 'order_date' column is currently stored as a string in 'YYYY-MM-DD' format. You want to create a schema and apply the schema to the dataframe. Choose the correct options that defines the schema in below code snippets:

Answer: A

Explanation:
Option C correctly defines the schema with DateType for 'order_date' , converts the string column to a DateType using 'to_date' , and then filters the DataFrame based on the date difference. Options A, B and D do not use StringType at right places and are therefore inefficient. Option E applies to_date without any need.

NEW QUESTION # 259
You are using Snowpark Python to process a DataFrame containing customer data,. One of the columns, 'phone_number' , contains phone numbers in various formats (e.g., '123-456-7890', '(123) 456-7890', '1234567890'). You need to standardize these phone numbers to the format 'XXX-XXX-XXXX' using a User-Defined Function (UDF). You want to create a UDF called 'standardize_phone_number' that takes a string as input and returns the standardized phone number. Which of the following code snippets correctly defines and registers this UDF in Snowpark, and applies it to the 'phone_number' column of the 'customer df DataFrame? Assume a Snowflake session object called 'session' is already available.

Answer: D

Explanation:
Option E is the most concise and correct way to define and use the UDF. It uses the '@F.udf decorator, which simplifies the UDF registration process. It also correctly imports 'snowflake.snowpark.functions as F to use the decorator. Options A and D are less efficient because they use 'call_udf instead of directly calling the UDF. Option B registers UDF but does not import required libraries. Option C doesn't import necessary library for the functions.

NEW QUESTION # 260
You are developing a Snowpark Python UDF to perform sentiment analysis on product reviews. The UDF takes a text review (STRING) as input and returns a sentiment score (FLOAT). You want to operationalize this UDF, ensuring type safety and performance. Which of the following approaches is MOST recommended, considering both ease of use and explicit type declaration?

A.
B. Casting the result of the UDF to FLOAT within the UDF definition.
C. Using gudf(return_type=FloatType(), decorator with explicit data type registration.
D. Using Python type hints alone: 'def sentiment_score(review: str) -> float: .... and relying on Snowpark to infer types.
E. Using only the function name without any explicit type declaration or registration.

Answer: A

Explanation:
Using a combination of type hints and the registration API provides the best balance of readability and explicit type safety. The @udf decorator with 'return_type' and ensures that Snowflake understands the data types, while the Python type hints improve code readability and help catch type errors during development. Option A relies on inference, which can be less explicit and potentially lead to unexpected behavior. Option B lacks the readability of Python type hints. Option D is not a valid way to register a UDF. Casting the result (Option E) doesn't define the data type beforehand and is less efficient than defining during registration.

NEW QUESTION # 261
You're working with Snowpark and want to load data from a Pandas DataFrame into a Snowpark DataFrame. The Pandas DataFrame, 'customer_data' , contains columns with mixed data types (integers, strings, dates). Some columns also contain NULL values. You need to ensure that the data types are correctly inferred by Snowpark, NULL values are handled appropriately, and the resulting Snowpark DataFrame 'snowpark_customers' can be used for further transformations. What is the best approach to achieve this with minimal code and maximum performance?

A. First, replace all NA/NaN values in Pandas DataFrame with None, then create Snowpark DataFrame using 'session.createDataFrame(customer_datay.
B. Use 'session.write_pandas' because its optimized for large pandas dataframe.
C. Use 'session.createDataFrame(customer_datay and rely on Snowpark to automatically infer the schema and handle NULL values implicitly. Convert any problematic columns after the Snowpark DataFrame is created.
D. Explicitly define the schema with StructType and StructField, specifying the column names and data types based on the Pandas DataFrame, converting null values to Snowflake's null representation during DataFrame creation.
E. Infer the schema explicitly before creating the Snowpark DataFrame using Pandas DataFrame column types. For the string columns, define them to be StringType().

Answer: B

Explanation:
Relying on schema inference (option C) might not always guarantee the correct data types, especially with dates or mixed-type columns. Explicitly defining the schema (option B) can be verbose and error-prone. Replacing NA/NaN with None and using 'createDataFrame' (option D) is a functional approach, but might not be as performant as the optimized method of 'write_pandaS. Inferring the schema (Option A) might not be fully accurate. Using session.write_pandas leverages internal Snowflake optimizations for data transfer and type handling from Pandas to Snowpark, making it the most efficient.

NEW QUESTION # 262
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