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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Snowpark Concepts	15%	- Snowpark DataFrames and query plans - Client-side vs. Server-side execution - Stored procedures and conditional logic - Snowpark architecture and core concepts - Transformations vs. Actions - Snowpark Sessions and connection management
Topic 2: Snowpark API for Python	30%	- DataFrame creation and manipulation - Reading and writing data - User-Defined Functions (UDFs) and Stored Procedures - Working with Semi-structured data - Establishing connections and session management
Topic 3: Data Transformations and DataFrame Operations	35%	- Using built-in functions - Persisting transformed data - Window functions - Filtering, Aggregating, and Joining DataFrames - Complex data pipelines
Topic 4: Performance Optimization and Best Practices	20%	- Warehouse sizing for Snowpark - Caching strategies - Debugging and explain plans - Minimizing data transfer - Vectorized UDFs - Query pushdown and optimization

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q48-Q53):

NEW QUESTION # 48
You have created a Python UDTF in Snowpark to process large volumes of image data'. This UDTF resizes each image and extracts certain features from it. The process is memory-intensive and sometimes fails due to Python process exceeding memory limits. You need to optimize this UDTF for memory usage. Which of the following strategies would be MOST effective? (Select TWO)

A. Utilize a smaller Snowflake warehouse size. Smaller warehouses have less memory per node, which will force the UDTF to process data in smaller chunks.
B. Increase the value of 'MAX BATCH SIZE in the Snowpark session configuration to allow the UDTF to process larger batches of images at once.
C. Implement lazy evaluation and iterators within the UDTF to process images one at a time instead of loading all images into memory at once.
D. Leverage external packages such as PILIPillow with appropriate image compression techniques to reduce the memory footprint of each image before processing.
E. Use a scalar UDF instead of a UDTF. Scalar UDFs are generally more memory efficient.

Answer: C,D

Explanation:
Lazy evaluation (B) and iterators allow processing data in smaller chunks, reducing memory consumption. Utilizing image compression techniques (D) reduces the memory footprint of each image, allowing more images to be processed within the available memory. Using scalar UDFs would not help and might perform worse. Decreasing the warehouse size would not solve the underlying problem and may make it worse. MAX_BATCH_SIZE determines how many records are sent over to UDF for each batch. It can help increasing performance but decreasing batch sizes might help with memory management, however its dependent on each case.

NEW QUESTION # 49
You are developing a Snowpark application to load data into a Snowflake table named 'SALES DATA. The DataFrame 'sales_df contains new sales records. You need to insert these records into 'SALES DATA. Which of the following Snowpark DataFrame methods will efficiently perform this operation, considering potential data type mismatches between the DataFrame and the target table? Assume no explicit schema definition is necessary.

A. Option A
B. Option C
C. Option D
D. Option E
E. Option B

Answer: E

Explanation:
The method directly inserts the DataFrame's data into the specified Snowflake table. Snowpark implicitly handles data type conversions where possible. is generally for creating new tables. is for loading data from files in stages, and 'write_pandas' method requires pandas DataFrame and session object. Using is inefficient as it collects all the data into the driver and then inserts and can cause memory issues.

NEW QUESTION # 50
You have a Snowpark Python stored procedure that reads data from a Snowflake table, performs a complex calculation using Pandas, and then writes the results back to another Snowflake table. You are experiencing performance issues, and you suspect the data transfer between Snowpark and Pandas is a bottleneck. Which of the following techniques could significantly improve the performance of this stored procedure? (Select two)

A. Use the 'TABLE function in Snowpark to directly access the source table instead of reading the entire table into a Snowpark DataFrame at once.
B. Convert the Pandas DataFrame to a Dask DataFrame for distributed computation.
C. Increase the warehouse size to the largest possible value before executing the stored procedure.
D. Leverage Snowpark's optimized functions and UDFs wherever possible to perform transformations within Snowflake's engine instead of transferring data to Pandas.
E. Utilize vectorized operations within Pandas to minimize explicit looping and improve calculation speed.

Answer: D,E

Explanation:
Options B and D are the most effective. Vectorized operations (B) significantly speed up Pandas calculations. Performing transformations within Snowflake (D) avoids unnecessary data transfer between Snowpark and Pandas, reducing the bottleneck. A is useful, but secondary. C only affects Snowflake side processing, but it may help. E would be useful, but not as helpful as pushing as much as possible down to Snowflake processing.

NEW QUESTION # 51
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. Convert the Snowpark DataFrame to Pandas DataFrame, perform the merge operation using Pandas, and then write the Pandas DataFrame back to Snowflake.
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. Create a stored procedure that performs a MERGE statement in Snowflake, and call this stored procedure from your Snowpark code.
D. Perform a full outer join between the existing table and the DataFrame, then use
E. Use the method, specifying 'primary_key' in the options.

Answer: B,C

Explanation:
Options C and D are the correct answers. Snowpark's method does not directly support a merge operation. To achieve this, you typically need to use one of the following approaches: Create a stored procedure that executes a Snowflake MERGE statement. This allows you to perform the merge operation server-side within Snowflake using SQL. This stored procedure can then be called from the Snowpark application. Stage your dataframe in to a new table, then create merge statement to handle the rest. Option A is incorrect because Snowpark does not have a option for Option B is possible but very inefficient, it requires reading all of the existing data in the table. Option E is inefficient because converting DataFrame to Pandas will add another performance bottleneck when dealing with large datasets.

NEW QUESTION # 52
You are building a Snowpark application that involves a UDF. Consider that you are creating UDF as follows:

A. UDF registration happens on the client side, before dataframe transformations are evaluated and sent to Snowflake.
B. UDF registration occurs on the server-side, only when a dataframe action such as collect or show is called .
C. The UDF is registered lazily; only when called the first time will it be registered and available in the session.
D. There will be no attempt to register UDF until .collect()' action is performed.
E. The behaviour of the UDF registration dependends on the size of code in it.

Answer: A

Explanation:
UDFs are registered client-side when created which happens before any dataframe is sent for processing. Lazy Evaluation applies to dataframe transformations and not to registration of UDFs.

NEW QUESTION # 53
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