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Snowflake SPS-C01 Exam Syllabus Topics:

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

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q136-Q141):

NEW QUESTION # 136
You have a Snowpark application that utilizes a vectorized Python UDF to perform complex calculations on a large dataset. You notice that the performance is still not optimal. You suspect that the bottleneck might be related to how the data is being partitioned and processed by Snowflake. Which of the following actions, when performed in conjunction with vectorization, would MOST likely improve performance?

A. Increase the number of UDF worker threads within the UDF definition.
B. Broadcast the DataFrame to all compute nodes before applying the UDF.
C. Repartition the Snowpark DataFrame using to align the data distribution with the computational needs of the UDF.
D. Ensure that the data is pre-sorted according to the primary key of the table before applying the UDF.
E. Convert the DataFrame to a Pandas DataFrame before applying the UDF.

Answer: C

Explanation:
Repartitioning the DataFrame using allows you to control how the data is distributed across compute nodes. This can improve performance by ensuring that related data is processed together, reducing data shuffling and improving data locality. Pre- sorting data (A) might help in some cases, but it doesn't guarantee optimal data distribution for parallel processing. Broadcasting the DataFrame (C) is suitable for smaller datasets, not large ones where it can lead to memory issues. Converting the DataFrame to a Pandas DataFrame (D) defeats the purpose of using Snowpark for distributed processing and introduces a single-node bottleneck. There's no direct control over the number of UDF worker threads in Snowflake.

NEW QUESTION # 137
Consider the following Snowpark Python code snippet:

A. The 'result_df DataFrame will be persisted to the 'AGGREGATED SALES table in the default schema of the user running the code.
B. This code will ovemrite the table if it already exists.
C.
D. The code will fail because there is no call to or on the 'result_df dataframe and Snowflake performs lazy evaluation.
E. This code will fail because is not a valid method for Snowpark DataFrames.

Answer: A,B

Explanation:
' will indeed overwrite the table if it exists. When a schema is not explicitly specified, Snowpark defaults to the schema of the user's current session for table creation. No explicit call to an action like 'collect()' or show()' is needed to trigger execution, as is an action itself. is a valid operation to persist DataFrames as tables in Snowpark. The table name does not strictly need to be fully qualified unless targeting a schema different from the user's default.

NEW QUESTION # 138
You have a Snowpark DataFrame named 'orders df with columns 'order id', 'customer id', 'order date', and 'total amount'. You need to create a new DataFrame that contains only the 'customer_id" and the total number of orders placed by each customer. However, you want to perform this aggregation in parallel using a user-defined function (UDF) to improve performance. Which approach is MOST efficient and CORRECT?

A. Create a UDF that calculates the mode of total_amount, then apply the UDF to the DataFrame using 'select'.
B. Create a UDF that takes an iterator of order IDs and returns the count. Apply this UDF to each partition of the DataFrame using 'maplnPartitions' .
C. Use 'groupBy' to group by 'customer_id' and then use the 'count' aggregate function. Do not use a UDF.
D. Create a UDF that takes a customer ID as input and returns the count of orders for that customer, then apply this UDF to each distinct customer ID using 'map'.
E. Create a UDF that performs the entire aggregation and call it with 'orders_df. The UDF uses a Pandas DataFrame internally to perform the count.

Answer: C

Explanation:
The most efficient and correct approach is to use the built-in 'groupBy' and 'count' functions. These functions are optimized for Snowflake's architecture and will generally outperform UDF-based solutions for simple aggregations. Using UDFs for simple tasks introduces overhead and can negate any potential performance benefits. While options A, B and D could achieve the result, they are less efficient. Option E is irrelevant to the problem.

NEW QUESTION # 139
You have a Snowpark DataFrame containing sales data with columns 'region' , and 'sales_amount'. You need to calculate the total sales amount for each region and then filter the results to only include regions where the total sales amount is greater than 10000. Which of the following Snowpark code snippets correctly implements this logic?

Answer: E

Explanation:
Option C is correct because it uses the correct Snowpark syntax for grouping by region, summing the sales amount with an alias, and then filtering based on the aliased column. Option A is incorrect as it omits the 'sf.' prefix for 'sum' and 'col' within the 'filter'. Option B uses where' instead of Tilters but correctly aggregates. Option D incorrectly compares a string to a number in the filter. Option E uses a non-standard way of referencing the aggregated column in the 'where' clause.

NEW QUESTION # 140
You have a Snowpark DataFrame containing customer data'. You need to create a stored procedure that accepts the DataFrame and a list of column names as input and returns a new DataFrame containing only the specified columns. Which of the following approaches correctly implement this functionality and handles data types effectively (Select all that apply)?

Answer: A,D

Explanation:
Options B and E are correct. Option B correctly registers the function 'select_columnS as a stored procedure using "session.sproc.register'. Option E properly constructs the DataFrame by dynamically selecting columns by using 'df[col]'. Option A although syntactically correct may not perform as expected. Option C is incorrect because it attempts to use 'ArrayType' for a standard Python List, which is incompatible. Option D uses columns: str' which makes column as Tuple object instead of List object.

NEW QUESTION # 141
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