SPS-C01 Übungsfragen: Snowflake Certified SnowPro Specialty - Snowpark & SPS-C01 Dateien Prüfungsunterlagen

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Snowflake SPS-C01 Exam Syllabus Topics:

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

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Snowflake Certified SnowPro Specialty - Snowpark SPS-C01 Prüfungsfragen mit Lösungen (Q242-Q247):

242. Frage
You are tasked with creating a Snowpark session that utilizes a specific Snowflake warehouse for all operations. Which of the following code snippets BEST demonstrates how to correctly specify the 'warehouse' parameter when creating a session using snowpark.Session.builder.configs'?

Antwort: C

Begründung:
The correct parameter name for specifying the warehouse in the 'configs' dictionary is 'warehouse'. The other options either use incorrect key names (SNOWFLAKE_WAREHOUSE, snowflake.warehouse, WAREHOUSE_NAME) or an incorrect method call (.config instead of .configs). The code snippets provided demonstrate the correct and incorrect methods for specifying the warehouse parameter during Snowpark session creation. Option A correctly utilizes the 'warehouse' parameter within the 'configs' dictionary passed to the Session builder.

243. Frage
Consider the following Snowpark code snippet that defines and registers a UDF:

Which of the following statements about this code are TRUE?

A. The 'replace=True' argument ensures that any existing UDF with the same name ('ADD_SALUTATION') is overwritten.
B. The UDF is registered as a temporary UDF and will be removed when the session ends.
C. The 'input_types' parameter is redundant because Python's type hints are automatically used to determine the input types.
D. The default value of 'salutation' in the Python function will be used even when calling the UDF from SQL if the salutation parameter is omitted.
E. The UDF is registered as a permanent UDF and stored in the specified stage for future use.

Antwort: A,D,E

Begründung:
The correct answers are C, D, and E. makes the UDF permanent. 'replace=True' overwrites any existing UDF with the same name. Python's default parameter value IS used in the SQL call if the salutation is omitted. 'input_typeS are not redundant, they are required and Python's type hints are not automatically used. Option A is incorrect because 'is_permanent' is set to true.

244. Frage
You are working with sensor data in Snowpark. Your data contains (Integer), 'timestamp' (Timestamp), 'temperature' (Double), and 'status' (String). You need to create a Snowpark DataFrame named representing this data'. Which of the following is the most efficient and type-safe way to create the DataFrame from a list of Python tuples using an explicitly defined schema, assuming you need to maintain maximum precision for temperature readings and that all data types should map to the most efficient and appropriate Snowflake data type?

Antwort: C

Begründung:
Option B is the most efficient and type-safe way to create the DataFrame. It correctly maps each data element to its most appropriate Snowpark data type: Integer Type for 'sensor_id' , TimestampType for 'timestamp' , DoubleType for 'temperature' (as it offers more precision than FloatType for Snowflake), and StringType for 'status'. While DecimalType could be considered for representing exact numeric values, it's often used for currency or financial data where exact representation is critical. In this case, DoubleType is sufficient for temperature readings. Timestamp values are automatically converted from the string representation. FloatType has less precision than DoubleType. Using LongType for sensor_id might be overkill if the IDs are reasonably small integers. Using StringType for timestamp will require later conversion for time based calculation.

245. Frage
You are tasked with optimizing a Snowpark application that performs complex data transformations on a large dataset (1 TB) stored in Snowflake. The application currently uses Snowpark DataFrames and is experiencing slow performance. You suspect the issue might be related to data transfer overhead between the Snowflake engine and the Python environment. Which of the following strategies would be MOST effective in minimizing this overhead and improving performance?

A. Utilize User-Defined Functions (UDFs) written in Python to encapsulate the transformations and execute them within the Snowflake engine.
B. Increase the virtual warehouse size to the largest available option (e.g., X-Large) to improve processing power within Snowflake, regardless of data transfer costs.
C. Reduce the data volume by applying aggressive filtering and aggregation using Snowpark DataFrame operations before any other transformations, minimizing the amount of data transferred.
D. Convert the Snowpark DataFrame to a Pandas DataFrame and perform the transformations locally within the Python environment.
E. Implement vectorization techniques within the Snowpark DataFrame operations using built-in functions and optimized expressions where applicable.

Antwort: A,C,E

Begründung:
UDFs written in Python allow you to push the computation to the Snowflake engine, avoiding the need to transfer large amounts of data to the Python environment, improving performance by reducing data transfer overhead. Pre-filtering and aggregating reduces data before more complex transformations, also reducing data transfer. Vectorization through optimized Snowpark DataFrame methods also reduces data transfer and speeds computation in the Snowflake engine. Converting to Pandas DataFrames negates the benefit of Snowpark for large datasets. Increasing the virtual warehouse size only addresses processing power but does not minimize data transfer.

246. Frage
You have a Snowflake table named 'RAW EVENTS with a large number of events data, containing columns like 'EVENT ID', 'TIMESTAMP, 'USER ID, and 'EVENT_TYPE. The 'EVENT TYPE column contains string values representing different event categories. You want to create a Snowpark DataFrame, but due to the table's size, you only want to sample a small portion of the data for initial exploration and testing. Which of the following code snippets MOST accurately and efficiently creates a sampled Snowpark DataFrame named 'sampled_df containing approximately 1% of the rows from the 'RAW EVENTS table?

Antwort: D

Begründung:
Explanation:Option A provides the MOST accurate and efficient way to sample 1% of the data using , seed-42)'. The 'frae parameter directly specifies the fraction of rows to sample. 'seed' ensures consistent sampling across multiple runs. Option B is inefficient because it calculates the total count of rows before limiting, which scans the entire table. Option C, uses 'randomSplit' , but it splits the data into two new dataframes. Option D, utilizes Snowflake's SQL sampling feature via 'session.sqr , is a valid alternative, but less idiomatic when using Snowpark API directly. Option E, the parameter 'n' is deprecated and has been removed. 'sample' requires 'frae parameter.

247. Frage
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