SPS-C01 Prüfungsunterlagen, SPS-C01 Fragen&Antworten

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Snowflake SPS-C01 Exam Syllabus Topics:

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

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SPS-C01 Fragen&Antworten & SPS-C01 Zertifikatsfragen

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Snowflake Certified SnowPro Specialty - Snowpark SPS-C01 Prüfungsfragen mit Lösungen (Q358-Q363):

358. Frage
You're tasked with creating a Snowpark UDF to calculate the Haversine distance between two sets of latitude and longitude coordinates (point A and point B). Which of the following statements about deploying and using this UDF is/are TRUE?

A. The UDF can only be written in Python and must be deployed as an inline UDF within the Snowpark session.
B. When defining the UDF with input types, the Python types must exactly match the corresponding Snowflake data types.
C. The UDF can be written in Python, Java, or Scala. Using a Java UDF will likely offer best performance, especially when dealing with very large datasets. You'll need to stage the compiled JAR file on an internal stage that Snowpark can access.
D. The UDF can only be called directly from within the Snowpark session and cannot be used in standard Snowflake SQL queries.
E. The UDF, once defined, can be used inside of any DataFrame operation like 'select', 'filter' , and 'withColumn'

Antwort: D,E

359. Frage
Consider the following Snowpark Python code snippet:

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

Antwort: C,D

Begründung:
' 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.

360. Frage
A data scientist has developed a Snowpark Python stored procedure named 'model_training'. This procedure utilizes a large machine learning model and requires significant compute resources. The data scientist wants to optimize the cost and performance of running this stored procedure. Which of the following strategies would be the MOST effective for achieving this goal?

A. Register the stored procedure with the '@sproc' decorator without specifying any warehouse size, letting Snowflake automatically manage the warehouse.
B. Convert the Python stored procedure to a SQL stored procedure to leverage Snowflake's SQL optimization engine.
C. Specify a warehouse size using the 'warehouse' parameter within the '@sproc' decorator and leverage auto-suspend and auto-resume features to minimize costs when the procedure is idle.
D. Run the stored procedure on a larger Snowflake warehouse to reduce execution time, regardless of potential idle time.
E. Split the stored procedure into multiple smaller procedures and execute them sequentially on a smaller warehouse.

Antwort: C

Begründung:
Specifying a warehouse size and using auto-suspend and auto-resume provides a balance between performance and cost. Option A might increase costs due to idle time. Option B relies on Snowflake's default warehouse, which might not be optimal. Option D could increase overall execution time due to overhead. Option E may not be feasible or efficient if the logic is heavily dependent on Python libraries. Therefore, specifically assigning an appropriate warehouse size with auto-suspend/resume is the most effective approach.

361. Frage
You have a Snowpark DataFrame named 'products' with columns 'product_id' (INT), 'product_name' (STRING), and 'price' (DOUBLE). You want to apply a transformation to calculate a 'discounted_price' column, which is the 'price' reduced by 10% if the price is greater than $100.00. Which of the following code snippets is the most efficient way to achieve this using Snowpark Python?

Antwort: B,E

Begründung:
The most efficient ways are B and C. Option B directly uses the 'when' and 'otherwise' functions in Snowpark, which are optimized for execution within Snowflake. Option C is similar to B but explicitly uses 'lit' to represent the numeric literal, ensuring proper type handling in Snowpark. IJDFs (Option A) are generally less efficient than built-in functions. Option D attempts to use RDDs, which are not part of the Snowpark API. Option E is not valid Snowpark python syntax. Therefore, B and C are the correct answers.

362. Frage
You are tasked with building a machine learning pipeline in Snowpark to predict customer churn. You plan to use the scikit-learn library for model training and want to deploy the trained model as a Snowpark UDF for real-time scoring. Consider the following code snippet:

A. The code will fail because the trained model needs to be saved to a stage and loaded within the UDF, rather than being passed directly.
B. The code will fail because the return type of the UDF is not explicitly defined. Snowpark requires explicit type hints for UDF return values.
C. The code will fail because the 'moder object cannot be directly serialized and passed to the UDE A different serialization method (e.g., pickle) is required.
D. The code will execute successfully and create a UDF that predicts churn using the trained model.
E. The code will fail because scikit-learn is not a supported library for Snowpark UDFs by default, and needs to be explicitly added to the session imports.

Antwort: C

Begründung:
The primary issue is that the 'model' object, which is a trained scikit-learn model, cannot be directly serialized and passed as an argument to the UDF. Snowpark's UDF serialization mechanism doesn't automatically handle complex Python objects like scikit-learn models. You'll need to use a serialization method like 'pickle' to serialize the model into a byte stream, and then deserialize it within the UDF. The other options are less likely to cause the code to fail if serialization is handled, assuming the snowpark library is imported and available, and a return type is correctly defined.

363. Frage
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