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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Performance and Best Practices	10%	- Optimization techniques 1. Caching and warehouse sizing 2. Query pushdown and execution plans 3. Minimizing data movement - Security and governance 1. Access control and permissions 2. Data protection and compliance
Topic 2: Snowpark API and Development	30%	- Multi-language support 1. Environment setup and dependencies 2. Java and Scala API basics - Python API fundamentals 1. Data persistence and writing results 2. Column operations and functions 3. DataFrame creation from tables, views, SQL
Topic 3: Snowpark Concepts and Architecture	25%	- Snowpark architecture and execution model 1. Lazy evaluation and DAG execution 2. Transformations vs actions 3. Client-side vs server-side processing - Session management and connection 1. Create and configure Snowpark sessions 2. Authentication and connection settings
Topic 4: Data Transformations and Operations	35%	- Advanced operations 1. Window functions and analytics 2. Pivot and unpivot transformations 3. Semi-structured data processing - DataFrame manipulation 1. Filtering, sorting, grouping, aggregation 2. Selection, projection, renaming, casting 3. Joins, unions, set operations - User-defined logic 1. UDFs, UDAFs, UDTFs 2. Stored procedures with Snowpark

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Snowflake SPS-C01 Prüfungsfragen - SPS-C01 Vorbereitungsfragen

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Snowflake Certified SnowPro Specialty - Snowpark SPS-C01 Prüfungsfragen mit Lösungen (Q262-Q267):

262. Frage
You are designing a Snowpark application to process streaming data ingested into Snowflake using Snowpipe. The application needs to apply a complex set of transformations and aggregations to the incoming data in real-time. Which of the following approaches would be MOST suitable for this scenario, leveraging the strengths of Snowpark architecture?

A. Utilize Snowflake's Streams and Tasks feature and define views with complex SQL transformations that leverages Snowpipe.
B. Continuously query the incoming data from Snowpipe using a Snowpark DataFrame and perform the transformations and aggregations on the client-side in a loop.
C. Create a Snowpark DataFrame that represents the incoming data and use the 'write_pandaS function to write the transformed data to a separate Snowflake table after each micro-batch.
D. Define a Snowpark Stored Procedure ('sproc') that is triggered automatically by a Snowflake Task whenever new data arrives via Snowpipe. The stored procedure performs the transformations and aggregations and stores the results in a new table.
E. Use Snowpark to define a series of chained UDFs that perform the transformations and aggregations directly within the Snowpipe pipeline.

Antwort: D

Begründung:
Using a Snowpark Stored Procedure triggered by a Snowflake Task provides the best solution for real-time processing of streaming data from Snowpipe. The Task automates the execution of the stored procedure whenever new data is available, and the stored procedure leverages Snowpark's server-side capabilities to perform the transformations and aggregations efficiently within the Snowflake environment. Option A involves client-side processing, Option B isn't compatible with Snowpipe directly and chained UDFs may not optimal for complex transformations and aggregations. Option C involves constant writes and may have performance issues with large datasets. While option E could work, utilizing Snowpark stored procedures provides better flexibility and Python code integration for more complex logic.

263. Frage
You have a Snowpark DataFrame named containing customer data. You want to calculate the average order value for each customer and rank them based on this average. The 'customer_df contains columns 'customer_id' and 'order_value'. Which of the following Snowpark code snippets correctly performs this task, returning a new DataFrame with 'customer_id', 'average_order_value', and 'rank' columns?

Antwort: A

Begründung:
Option E is the correct answer because it uses 'Window.orderBy' without partitioning (partitionBy) to create a ranking across all customers based on their average order value. The window specification is ordered by the average order value in descending order and the 'rank()' function is applied over this window. Option A & B incorrectly partitions by customer id. Option C uses the function 'mean' instead of 'avg', which are synonyms, but is using 'row_number' incorrectly when a standard ranking is required Option D does not apply 'rank()' over a window specification and would throw an error since rank function need to apply on window function.

264. Frage
Consider a Snowpark DataFrame with a containing date values, some of which are corrupted (e.g., invalid date formats or out-of-range values). You need to identify and either remove or correct these corrupted date values. Which of the following approaches can be effectively used in Snowpark Python to handle such scenarios? (Select all that apply)

A. Use the 'to_date' function with the 'IGNORE' keyword to automatically replace invalid dates with NULL.
B. Define a UDF that attempts to parse the date string and returns a valid date or None if parsing fails; then, use this UDF to create a new column and filter rows with None values.
C. Use function to convert the column and filter out rows where the conversion results in NULL.
D. Implement a regular expression-based filter to remove rows where the 'date_column' does not match a valid date format.
E. Directly replace corrupt dates using df.na.fill() after identifying the corrupt dates.

Antwort: B,C,D

Begründung:
Options A, B, and D are valid approaches. Option A allows converting the column to date, setting incorrect values as NULL, which can then be filtered. Option B (UDF) provides flexibility for custom date parsing and error handling. Option D is valid as well, as it filters out data which doesn't match the expression. Option C is incorrect. The 'to_date' function in Snowpark does not have an 'IGNORE' keyword to replace invalid dates with NULL automatically. If it fails to convert to date, it will throw an error. Option E assumes that the corrupted dates can be pre-identified, which is generally not the case as the process aims to identify the dates that are corrupt, thus making it an incorrect answer.

265. Frage
You are working with a Snowpark DataFrame 'products_df' that contains product information, including 'product_name', 'category', and 'price'. You need to perform several transformations: 1. Rename the 'product_name' column to 'item_name'. 2. Create a new column 'discounted_price' by applying a 10% discount to the 'price' column. 3. Filter the DataFrame to only include products in the 'Electronics' category where the 'discounted_price' is less than 100. Which of the following code sequences correctly and efficiently performs these transformations in Snowpark?

Antwort: B

Begründung:
Option D is the most efficient. Option D is correct because it renames the column first, then filters for 'Electronics', creates the 'discounted_price' column, and finally filters based on the discounted price being less than 100, all correctly chained. Other options will not perform transformation correctly or not follow the correct sequence.

266. Frage
You have a Snowpark Python application that uses a UDF to perform custom data transformations. The UDF relies on a large, read-only lookup table that is stored as a CSV file on a Snowflake stage. Which of the following strategies would be the MOST efficient way to access the lookup table within the UDF?

A. Read the CSV file from the stage every time the UDF is called using 'snowflake.connector.connect()' and then load the data into a Pandas DataFrame within the UDF function.
B. Load the CSV file into a Snowflake table and then query the table from within the UDF using 'session.sql(V.
C. Read the CSV file from the stage once when the UDF is first called, cache the data in a global variable within the UDF module, and then reuse the cached data for subsequent calls.
D. Use the 'cachetoolS library with a Least Recently Used (LRU) cache to store the lookup table in memory. The UDF will check the cache before reading the CSV file, and update the cache if necessary. The CSV file is read with get_stage_file API from session.
E. Load the CSV file into a Snowflake stage, and in the python UDF code, use the get_stage_file API from session object to read the file once. Then the data cached in-memory within the UDF module, and reuse the cached data for subsequent calls.

Antwort: B,D

Begründung:
Both options C and E are efficient. Option C leverages Snowflake's internal storage and query capabilities, avoiding repeated file reads. Although reading a file from stage only at once is good, it would impact the first call. Option E avoids hitting the stage every time, and only when the key is not present in cache. Options A and B suffer from performance bottlenecks due to repeated file access. In general, reading data within the Snowflake environment (C) is more performant than reading data from external sources within the UDF, especially when Snowflake's query optimizer can be used. Caching with LRU using the 'cachetoolS library is effective, as cache would contain some values from large data. get_stage_file API from session object is efficient way.

267. Frage
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