SPS-C01 German & SPS-C01 Vorbereitungsfragen

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Snowflake SPS-C01 Exam Syllabus Topics:

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

>> SPS-C01 German <<

SPS-C01 Prüfungsfragen, SPS-C01 Fragen und Antworten, Snowflake Certified SnowPro Specialty - Snowpark

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Snowflake Certified SnowPro Specialty - Snowpark SPS-C01 Prüfungsfragen mit Lösungen (Q248-Q253):

248. Frage
You're working with a Snowpark DataFrame named 'sales_df' that contains sales transaction data'. You need to create a new DataFrame that includes only the rows where the 'order_date' is within the last 30 days. The 'order_date' column is currently stored as a string in 'YYYY-MM-DD' format. You want to create a schema and apply the schema to the dataframe. Choose the correct options that defines the schema in below code snippets:

Antwort: C

Begründung:
Option C correctly defines the schema with DateType for 'order_date' , converts the string column to a DateType using 'to_date' , and then filters the DataFrame based on the date difference. Options A, B and D do not use StringType at right places and are therefore inefficient. Option E applies to_date without any need.

249. Frage
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. 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.
B. Define 'sentiment_model.pkl' as a parameter during UDF definition to load only once per worker process and send it to the UDF.
C. 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.
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 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.

Antwort: E

Begründung:
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.

250. Frage
Consider the following Snowpark Python code snippet designed to create a DataFrame and then register a custom function (UDF):

This code runs successfully. However, you need to deploy this as a stored procedure. What minimal changes are required to make this code runnable as a Snowpark Python stored procedure and callable from SQL?

A. The 'return df.collect()' line must be replaced with 'return and 'return_type' and 'input_typeS arguments of udf must be removed. The rest of the code remains unchanged.
B. The 'return df.collect()' line must be replaced with 'return and the 'return_type' and 'input_typeS arguments of udf must be removed to allow inference. The rest of the code remains unchanged.
C. The 'return df.collect()' line must be replaced with 'return and the Snowflake session object must be explicitly passed to the UDF when it is called.
D. No changes are required; the code will function as a stored procedure as is.
E. The 'return df.collect()' line must be replaced with 'return df and create a DataFrame. The rest of the code remains unchanged.

Antwort: B

Begründung:
When deploying as a stored procedure, 'return will cause an error since stored procedures cannot return Snowpark DataFrames directly to SQL. You need to return the DataFrame to pandas using 'return and remove 'return_type' and 'input_types' arguments of udf to allow inference.

251. Frage
You are tasked with building a Snowpark application to process sensor data from IoT devices. The data arrives as JSON strings and needs to be transformed into a tabular format before being stored in a Snowflake table. You decide to use a User-Defined Table Function (UDTF) written in Python to handle this transformation. Which of the following approaches is the MOST efficient and scalable way to deploy and execute this UDTF in a production Snowpark environment, considering the possibility of high data volumes and concurrency?

A. Package the Python UDTF into a zip file and upload it to a Snowflake stage. During UDTF registration, specify the stage location and use [...l, return_type=..., packages=['snowflake-snowpark-python', 'your_package'])'. Ensure that is appropriately tuned based on data size and complexity.
B. Deploy the UDTF as a stored procedure that is then called from your Snowpark application. The UDTF processes the data serially, one record at a time.
C. Deploy the UDTF as an inline Python function within the Snowpark DataFrame transformation code, relying on the client-side Python interpreter for execution.
D. Deploy the UDTF as an external function, leveraging an external API gateway to invoke the Python code running on a serverless compute platform.
E. Deploy the UDTF using the '@udtf decorator and explicitly specify a parameter when registering the function to control the size of input batches processed by each worker node.

Antwort: A

Begründung:
Option E is the MOST efficient and scalable. Deploying the UDTF with explicit package management and optimized batch sizing allows Snowflake to distribute the processing across multiple worker nodes, leveraging the platform's parallel processing capabilities. Specifying dependencies using the 'packages' parameter ensures that the required Python libraries are available on each worker node. Properly tuning max_batch_size' prevents memory exhaustion and optimizes processing speed. Options A, C, and D are generally less scalable and efficient due to client-side execution, serial processing, or added external latency. Option B is correct in mentioning batch sizing, but is incomplete. Option E offers the best balance of performance, scalability, and maintainability.

252. Frage
You have a Snowflake table 'orders_json' with a VARIANT column named "order details'. This column contains JSON objects, and one of the fields within these objects is an array called 'items'. You need to use Snowpark to flatten the 'items' array into rows, extracting the 'item_id' , 'item_name' , and 'quantity' from each item in the array. Which of the following Snowpark code snippets will correctly achieve this, assuming 'df is a DataFrame representing 'orders_json'?

Antwort: A

Begründung:
Option E is correct because it first extracts the 'items' array into a new column 'items_array' using 'withColumn' . Then, it uses the explode' function to flatten the array into rows, aliasing the exploded column as 'item'. Finally, it selects the desired fields 'item_name' , quantity') from the 'item' column using 'getltem'.

253. Frage
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