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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Performance Optimization and Best Practices	20%	- Minimizing data transfer - Caching strategies - Warehouse sizing for Snowpark - Debugging and explain plans - Query pushdown and optimization - Vectorized UDFs
Data Transformations and DataFrame Operations	35%	- Window functions - Filtering, Aggregating, and Joining DataFrames - Complex data pipelines - Persisting transformed data - Using built-in functions
Snowpark API for Python	30%	- Establishing connections and session management - Reading and writing data - DataFrame creation and manipulation - Working with Semi-structured data - User-Defined Functions (UDFs) and Stored Procedures
Snowpark Concepts	15%	- Client-side vs. Server-side execution - Snowpark DataFrames and query plans - Snowpark architecture and core concepts - Snowpark Sessions and connection management - Stored procedures and conditional logic - Transformations vs. Actions

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q355-Q360):

NEW QUESTION # 355
You are working with a Snowpark DataFrame 'df that contains user profile data'. A column named 'profile' stores user information as JSON, including 'age' (which can be a number or a string), 'is active' (which can be a boolean or a string 'true'/'false'), and registration date' (stored as a string in 'YYYY-MM-DD' format). You need to perform the following data transformations: 1. Cast the 'age' to an integer, defaulting to -1 if casting fails. 2. Cast 'is active' to a boolean, treating 'true' (case-insensitive) as true and any other string as false. 3. Convert 'registration_date' to a date object. Select the code snippets (multiple answers can be correct) that correctly accomplish these tasks using Snowpark DataFrame transformations.

Answer: D,E

Explanation:
Options A and D are both correct. Option A : Uses lit(-l for handling age casting failures. This is good because it can handle errors and provides null safe casting. try to cast to a number, and if it fails, return null, then coalesce use -1 instead. Option A : Correctly converts to a boolean using 'when' and 'lower' for case-insensitive matching. If it's the string 'true', returns 'true'. Otherwise, returns 'false'. Option A : Uses 'to_date' for 'registration_date' since the column stores date. Option D is not supported. It should be 'is_number' for the age field. Option D : Correctly converts to a boolean using 'when' and 'lower' for case-insensitive matching. If it's the string 'true , returns 'true. Otherwise, returns 'false. Option D : Uses 'to_date' for 'registration_date' since the column stores date. Option B 'try_cast' does not exist in Snowpark. Option C 'Trye' is never equal to 'True'. Option E: The 'registration_date' needs to be converted with 'to date' and not 'to_timestamp'.

NEW QUESTION # 356
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 # 357
You are tasked with processing a large number of PDF files stored in an external stage named Each PDF contains scanned receipts, and you need to extract the total amount from each receipt. You plan to use Snowpark Python, SnowflakeFile object, and an OCR (Optical Character Recognition) library for text extraction. Assuming you have already set up the connection and session, what is the most efficient and secure way to read and process these PDF files using Snowpark and SnowflakeFile, minimizing data transfer and maximizing parallelism?

A. Create a UDF that accepts a 'SnowflakeFile' object as input, performs OCR and amount extraction inside the UDF, and then call this UDF on a Snowpark DataFrame representing the files in the stage. Use 'session.read.option('PATTERN', ' to generate the initial DataFrame.
B. Use 'session.read.option('PATTERN', ' to load the PDF files as binary data into a Snowpark DataFrame. Then, define a UDF that takes the binary data as input, converts it to a 'SnowflakeFile' object within the UDF, performs OCR, and extracts the amount.
C. List all files in the stage using 'session.sql('LlST iterate through the results, create a 'SnowflakeFile' object for each file path, download each file using 'SnowflakeFile.get' into the client machine, perform OCR, extract the amount, and create a Snowpark DataFrame with the extracted amounts.
D. Define an external function that takes the path to a file on the stage as input, performs OCR using a cloud-based OCR service and stores the result in an intermediate database table, then create a Snowpark DataFrame to query extracted amounts. Use 'session-read-option('PATTERN', ' to generate the initial DataFrame.
E. Read all PDF files into a Pandas DataFrame, then iterate through the DataFrame, downloading each file using 'SnowflakeFile.gef , perform OCR, extract the amount, and finally create a Snowpark DataFrame with the extracted amounts.

Answer: A

Explanation:
Option B is the most efficient and secure approach. By creating a UDF that accepts a 'SnowflakeFile' object, the OCR processing happens within the Snowflake environment, close to the data. This minimizes data transfer out of Snowflake, improving performance and security. Using 'session-read-option('PATTERN', ' (with a dummy CSV format) is a clever way to create an initial DataFrame with file metadata for the UDF to operate on. Option A is inefficient as it transfers all PDFs to the client machine for processing. Option C requires converting binary data back to SnowflakeFile object inside UDF, which is not the intended use and might introduce complexity. Option D is highly inefficient and insecure as it involves downloading all files to the client, defeating the purpose of Snowpark. Option E, while using a cloud-based OCR service, adds complexity and dependency to external services.

NEW QUESTION # 358
You are working with Snowpark DataFrames representing sales transactions. The 'transactions df DataFrame contains recent transactions, while the 'sales_table' in Snowflake holds the historical sales data'. You need to merge the new transactions into the 'sales table', but you want to track which rows were inserted, updated, or left unchanged by the 'merge' operation. How can you capture this information using Snowpark and persist it to a separate table?

A. After the merge, query the 'sales_table' and compare it to a copy of the 'sales_table' taken before the merge. Identify inserted rows as those present in the new version but not in the old, and updated rows as those with different values in specific columns between the two versions. This strategy should be implemented with the help of external functions and UDFs, as it is an expensive operation and should not be computed with normal SQL.
B. You can use the 'returning' clause within the merge statement to retrieve the impacted rows and their change status (INSERTED, UPDATED) and then write this data to a tracking table using 'write.saveAsTable' .
C. You can combine the 'returning' clause along with a stored procedure to retrieve the inserted/updated rows with their status and then use the stored procedure to write to a different tracking table using insert statement.
D. It's not possible to capture the merge operation details (inserted/updated/unchanged rows) directly using Snowpark alone. You would need to implement custom logic outside of Snowpark to compare the data before and after the merge.
E. The merge operation automatically creates a system table or view that logs the details of each row that was inserted, updated, or left unchanged.

Answer: B

Explanation:
The 'returning' clause is a powerful feature of the 'merge' statement in Snowflake SQL. It allows you to capture the rows that were affected by the merge operation, along with details about the type of change (INSERTED, UPDATED, DELETED). In Snowpark, you can leverage this by including a 'returning' clause in your 'merge' statement and then use the returned DataFrame to write the data to a tracking table. This provides a direct and efficient way to monitor the impact of your merge operations. Therefore the correct answer is B.

NEW QUESTION # 359
You are tasked with building a Snowpark application to process sensor data from IoT devices. The data arrives as JSON strings and needs to be transformed into a tabular format before being stored in a Snowflake table. You decide to use a User-Defined Table Function (UDTF) written in Python to handle this transformation. Which of the following approaches is the MOST efficient and scalable way to deploy and execute this UDTF in a production Snowpark environment, considering the possibility of high data volumes and concurrency?

A. Package the Python UDTF into a zip file and upload it to a Snowflake stage. During UDTF registration, specify the stage location and use [...l, return_type=..., packages=['snowflake-snowpark-python', 'your_package'])'. Ensure that is appropriately tuned based on data size and complexity.
B. Deploy the UDTF as an inline Python function within the Snowpark DataFrame transformation code, relying on the client-side Python interpreter for execution.
C. Deploy the UDTF as an external function, leveraging an external API gateway to invoke the Python code running on a serverless compute platform.
D. Deploy the UDTF using the '@udtf decorator and explicitly specify a parameter when registering the function to control the size of input batches processed by each worker node.
E. Deploy the UDTF as a stored procedure that is then called from your Snowpark application. The UDTF processes the data serially, one record at a time.

Answer: A

Explanation:
Option E is the MOST efficient and scalable. Deploying the UDTF with explicit package management and optimized batch sizing allows Snowflake to distribute the processing across multiple worker nodes, leveraging the platform's parallel processing capabilities. Specifying dependencies using the 'packages' parameter ensures that the required Python libraries are available on each worker node. Properly tuning max_batch_size' prevents memory exhaustion and optimizes processing speed. Options A, C, and D are generally less scalable and efficient due to client-side execution, serial processing, or added external latency. Option B is correct in mentioning batch sizing, but is incomplete. Option E offers the best balance of performance, scalability, and maintainability.

NEW QUESTION # 360
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