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Snowflake SPS-C01 Exam Syllabus Topics:

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

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q206-Q211):

NEW QUESTION # 206
You have a Python function that calculates a complex statistical measure on a given row of a DataFrame. You want to apply this function to each row of a Snowpark DataFrame in a distributed manner. Which of the following is the MOST efficient way to achieve this?

A. Use the "map' method on the Snowpark DataFrame's underlying RDD (Resilient Distributed Dataset) and pass the Python function as an argument.
B. Use the 'apply' method on the Snowpark DataFrame, passing the Python function as an argument.
C. Create a Pandas UDF (User-Defined Function) using decorator and apply it to the Snowpark DataFrame.
D. Iterate through the rows of the Snowpark DataFrame and call the Python function on each row individually.
E. Use Snowpark's 'sprocs feature to create stored procedure and call the Python function.

Answer: C

Explanation:
Pandas UDFs (User-Defined Functions) are designed for efficient row-wise operations on Snowpark DataFrames. The @pandas_udf decorator enables Snowpark to execute the function in a distributed manner across Snowflake's compute resources, maximizing performance for row-by-row calculations. 'apply' method doesn't exist directly on Snowpark DataFrames. Iterating through rows (Option C) is extremely inefficient. Option D involves RDD which is not exposed directly with Snowpark DataFrames. While option E is an alternative it introduces unnecessary overhead.

NEW QUESTION # 207
Consider the following Snowpark Python code snippet that defines and applies a UDF:

Which of the following modifications would MOST likely improve the performance of this code, assuming the DataFrame 'df contains a large number of rows?

A. Remove the 'input_types' argument from 'session.udf.register' . Snowflake can automatically infer the input types.
B. Specify a different warehouse size when creating the Snowpark session using 'session = Session.builder.config('warehouse', 'XLARGE').configs(connection_params).create()'.
C. Change to in the "session.udf.register' call, ensuring the function is updated to handle batches of data.
D. Use and F.lit(0.2Y instead of 0.1 and 0.2 while creating the dataframe.
E. Rewrite the 'apply_discount' function to use NumPy arrays internally for vectorized calculations, ensuring compatibility with vectorized UDF execution. The function signature will also need to accept arrays.

Answer: C,E

Explanation:
Setting tells Snowpark to treat the UDF as a vectorized UDE However, simply setting the flag is not sufficient. E is also required because Vectorized UDF requires functions that accept and process arrays as inputs, not single scalar values. The function needs to be modified to handle arrays using NumPy or similar libraries. The code also need to be rewritten to work with arrays. B has nothing to do with performance. Increasing Warehouse size might help but not on the scale of vectorization.

NEW QUESTION # 208
You are building a Snowpark application to process sensitive data'. To enhance security, you want to leverage ephemeral sessions. Which configurations, passed to 'snowpark.Session.builder.configS , are required and sufficient to create an ephemeral session? Assume your Snowflake environment is properly configured to allow ephemeral sessions.

A. Only setting the 'role' parameter to a role with appropriate privileges.
B. Setting the 'role' parameter to a role with appropriate privileges and 'warehouse'.
C. Setting the 'role' parameter to a role with appropriate privileges and 'database' and 'schema'.
D. Setting the 'role' parameter to a role with appropriate privileges, 'database', 'schema', and 'warehouse'.
E. Ephemeral sessions do not have specific configurations; they are automatically created by Snowflake when the application connects.

Answer: D

Explanation:
Ephemeral sessions in Snowflake require that you specify a 'role', 'database', 'schema', and 'warehouse'. These parameters define the context within which the session will operate. Without these parameters, the session cannot be properly established. Although option A set role which is important for setting the context but it is insufficient. Ephemeral sessions are not automatic and require proper configuration.

NEW QUESTION # 209
You are developing a Snowpark stored procedure in Python that needs to access and modify a temporary table within the same session.
Which of the following approaches is the MOST efficient and recommended way to achieve this?

A. Creating a global temporary table using 'CREATE GLOBAL TEMPORARY TABLE outside the stored procedure and then accessing it within the stored procedure using 'session.table()'.
B. Using the Python DB API (e.g., 'snowflake.connector' ) within the stored procedure to establish a separate connection to Snowflake and interact with the temporary table.
C. Using to create a DataFrame representing the temporary table, and then performing all operations using DataFrame transformations.
D. Using 'session.createOrReplaceTempView()' to create a temporary view based on a DataFrame, and then querying the view using 'session.sql('SELECT
E. Using 'session.sql('CREATE TEMPORARY TABLE followed by subsequent 'session.sql('lNSERT INTO and 'session.sql('SELECT statements to interact with the temporary table.

Answer: C

Explanation:
Option B, using 'session.createDataFrame()' and DataFrame transformations, is the most efficient and recommended approach. Snowpark DataFrames are optimized for execution within the Snowflake engine. Using DataFrame transformations allows Snowpark to leverage its query optimization capabilities. Option A, using 'session.sql()' repeatedly, involves parsing and executing SQL statements for each operation, which is less efficient. Option C, using a separate connection, introduces unnecessary overhead and complexity. Option D, global temporary tables, are not session-specific. Option E, creating a temporary view and then querying it with SQL, is also less efficient than using DataFrame operations directly.

NEW QUESTION # 210
You are tasked with optimizing a Snowpark application that performs sentiment analysis on customer reviews using a Python UDE The UDF uses a large pre-trained natural language processing (NLP) model stored in a file named 'sentiment_model.pkl'. The current implementation loads the model from the stage for each row of data processed, which is impacting performance. How can you optimize the application to load the model only once per worker process?

A. Define 'sentiment_model.pkl' as a parameter during UDF definition to load only once per worker process and send it to the UDF.
B. Use a global variable to store the loaded model. Load the model from the stage into the global variable only if it is currently None. Upload 'sentiment_model.pkl' to a stage and reference it in the 'imports' clause.
C. Use to import 'sentiment_model.pkl'. Use the decorator from the 'functools' module to cache the model loading function, initializing the model outside of the UDF definition.
D. Implement a custom initialization function that loads the model and is called only once per worker process. Utilize the to retrieve and cache model during session initialization. Upload 'sentiment_model.pkl' to a stage and reference it in the 'imports' clause.
E. Use the decorator from the 'functools' module to cache the model loading function. Upload 'sentiment_model.pkl' to a stage and reference it in the 'imports' clause.

Answer: B

Explanation:
Option B offers the most straightforward and efficient solution. By utilizing a global variable and loading the model only if it's 'None' , the model is loaded only once per worker process. The 'imports' clause ensures the model file is accessible to the UDF. Caching via '@cache' (A) might not work correctly with serialization/deserialization across processes. (C) pre imports at the session level but cache control is still missing. passing as parameter (D) doesn't address model only loads once per worker. (E) This technique is not directly supported for achieving per-worker initialization in standard Snowpark UDFs, making B a better and more commonly used approach.

NEW QUESTION # 211
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