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| Section | Weight | Objectives |
|---|---|---|
| Snowpark API for Python | 30% | - DataFrame creation and manipulation - Reading and writing data - User-Defined Functions (UDFs) and Stored Procedures - Establishing connections and session management - Working with Semi-structured data |
| Snowpark Concepts | 15% | - Transformations vs. Actions - Snowpark architecture and core concepts - Client-side vs. Server-side execution - Snowpark Sessions and connection management - Stored procedures and conditional logic - Snowpark DataFrames and query plans |
| Data Transformations and DataFrame Operations | 35% | - Complex data pipelines - Using built-in functions - Window functions - Persisting transformed data - Filtering, Aggregating, and Joining DataFrames |
| Performance Optimization and Best Practices | 20% | - Vectorized UDFs - Query pushdown and optimization - Minimizing data transfer - Warehouse sizing for Snowpark - Caching strategies - Debugging and explain plans |
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問題 #144
You are tasked with optimizing the performance of a Snowpark application that uses a UDF to perform complex image processing. The UDF is currently registered using 'session.udf.registeff. You observe that the UDF execution is slow, particularly when processing large batches of images. What steps could you take to potentially improve the performance of this UDF execution? Select all that apply.
答案：B,D,E
解題說明：
The correct answers are B, C, and E. Locating the stage in the same region minimizes latency (B). Smaller dependency files deploy faster (C). Larger warehouses provide more resources (E). Converting to a UDTF might not always improve performance, it depends on the nature of the image processing (A)' is not directly related to UDF performance optimization, it is used for session management and cleanup (D).
問題 #145
You are developing a Snowpark stored procedure to perform sentiment analysis on customer reviews. You need to use the 'nltk' Python package, which is not a built-in package in Snowflake. You have already created a stage named 'my_stage' in Snowflake and uploaded the necessary nltk data files (e.g., 'vader_lexicon.zip') to the stage. Which of the following code snippets correctly configures the session and imports the required nltk components within the stored procedure?
答案：A
解題說明：
Option E correctly adds the zipped nltk data file from the stage as an import, and then updates the NLTK_DATA environment variable to point to the /tmp directory where Snowflake unpacks the zip file. This ensures that nltk can find its data files. Option A and D attempt to use './nltk_data' which is incorrect as the file system is read-only, and Option B incorrectly uses sys.path.append and session.add_packages in wrong way as well as hard coded path. Option C fails as well with session.add_packages('snowflake-snowpark-python','nltk') incorrect syntax .
問題 #146
You are working with a Snowpark DataFrame named containing information about products, including 'CATEGORY , 'SUBCATEGORY , and 'PRICE'. You want to determine the maximum price for each subcategory within each category. Furthermore, you need to filter the results to only include categories that have more than 5 subcategories. Which of the following Snowpark Python code snippets accomplishes this task? (Select all that apply)
答案：A,E
解題說明：
The correct options are D and E. They both address the requirement and count distinct subcategories within each category. Option D: Correctly calculates the maximum price for each subcategory within each category. Then separately calculates the count of DISTINCT subcategories for each category. Filters the categories to include only those with more than 5 subcategories. Joins the two resulting DataFrames to provide the final output. This is a standard and explicit way to accomplish the task. Using countDistinct to ensure each subcategory is only counted once. Option E: Aggregates Max Price and CountDistinct of subcategory into same dataframe, this addresses all requirements in a more concise manner. Option A: Does not give the right result since 'count' will not give the countDistinct. Also using 'COUNT(SUBCATEGORYV in category_countS dataframe results in count of all rows in the group instead of the number of groups (number of Subcategories). Option B: Window functions is irrelevant here, and requires more coding while can be handled efficiently by aggregating and countDistinct combination Option C: Option C doesn't aggregate maximum price for each subcategory within each category as the question mentions.
問題 #147
You're designing a Snowpark application to process large CSV files containing sensor data'. Each CSV file has millions of rows, and you need to calculate aggregate statistics (e.g., average, min, max) for specific sensor readings. The processing needs to be highly parallelized for performance. You have the following code snippet (incomplete):
Which of the following code snippets, when inserted at the ' ??? Add code here to calculate aggregate statistics ??? ' marker, would correctly calculate the average, minimum, and maximum readings for a column named 'sensor value' and return the result in a new Snowpark DataFrame?
答案：E
解題說明：
Option C is correct. When calculating aggregate statistics across the entire DataFrame (i.e., without any grouping), you need to use groupBy(Y with no arguments. This effectively creates a single group for the entire dataset, allowing the aggregation functions to operate correctly. Option A is syntactically valid but misses the crucial 'groupBy()' step, resulting in an error. Option B is incorrect because the 'agg function expects a dictionary where keys are column names and values are aggregation function strings , not Snowpark functions themselves. It also does not return alias values. Options D and E are syntactically valid because you can use and "sensor_value" instead of , but they fail to include so give incorrect aggregations.
問題 #148
You are developing a Snowpark application that processes large datasets stored in Snowflake. You need to implement custom User-Defined Functions (UDFs) written in Java. The UDF requires specific third-party libraries that are not available in the default Snowflake Java environment. What steps are necessary to package and deploy these UDFs correctly?
答案：A
解題說明：
The correct approach involves packaging the Java code and its dependencies into a JAR file, uploading it to a Snowflake stage, and using the 'CREATE FUNCTION' command with the 'IMPORTS clause. This informs Snowflake to include the specified JAR file in the classpath when executing the UDF. Other options are not valid ways to deploy Java UDFs with dependencies in Snowflake.
問題 #149
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SPS-C01考古題: https://www.vcesoft.com/SPS-C01-pdf.html
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