SPS-C01試験無料問題集（374題）「Snowflake Certified SnowPro Specialty

出題：1

You have a Python function that calculates a complex statistical measure on a given row of a DataFrame. You want to apply this function to each row of a Snowpark DataFrame in a distributed manner. Which of the following is the MOST efficient way to achieve this?

A. Use the 'apply' method on the Snowpark DataFrame, passing the Python function as an argument.

B. Create a Pandas UDF (User-Defined Function) using decorator and apply it to the Snowpark DataFrame.

C. Iterate through the rows of the Snowpark DataFrame and call the Python function on each row individually.

D. Use the "map' method on the Snowpark DataFrame's underlying RDD (Resilient Distributed Dataset) and pass the Python function as an argument.

E. Use Snowpark's 'sprocs feature to create stored procedure and call the Python function.

正解：B 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：2

You have a Snowpark DataFrame named 'products_df' with columns 'product_id' (INT), 'product_name' (VARCHAR), and 'price' (FLOAT). You want to create a new DataFrame called 'discounted_products df that includes all columns from 'products_df' plus a new column named 'discounted_price', which is calculated as the original price minus a discount percentage specified by the variable 'discount_rate' (e.g., 0.1 for 10%). The 'discount_rate' is stored in the database table named 'discount_table'. You want to load the rate to variable. Choose the correct ways to achieve this. (Select all that apply)

正解：B,C,E 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：3

You have a Snowflake view named 'SALES SUMMARY VW' that joins several large tables and performs complex aggregations. You need to create a Snowpark DataFrame from this view Which of the following considerations are MOST important to ensure optimal performance and resource utilization when working with this DataFrame?

A. Materialize the view into a temporary table using 'CREATE TEMPORARY TABLE AS SELECT FROM SALES SUMMARY VW before creating the Snowpark DataFrame.

B. Use 'session.sql()' with the view's definition to create the DataFrame, as this is generally faster than 'session.table()' for views.

C. Avoid calling 'collect()' or 'toPandas()' on the entire DataFrame unless absolutely necessary, as these operations bring all data into the client's memory.

D. Always specify the schema explicitly when creating the DataFrame from the view to prevent Snowpark from inferring it.

E. Leverage Snowpark's lazy evaluation and pushdown optimization capabilities by performing transformations and filtering on the DataFrame before executing actions.

正解：C,E 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：4

You are tasked with building a data pipeline that uses Snowpark to process customer data in a table called 'CUSTOMERS'. The table contains sensitive information, and you need to ensure that any Personally Identifiable Information (PII) is removed from the table after 30 days. You decide to implement a mechanism to automatically delete records older than 30 days. Which of the following approaches would you consider when designing the deletion process with consideration of cost, performance and security?

A. Create a Snowpark Stored Procedure that executes the 'DataFrame.delete()' operation and schedule this stored procedure to run daily using a Snowflake Task.

B. Implement a Snowflake Task that executes a SQL 'DELETE statement directly against the 'CUSTOMERS' table, filtering records based on the 'created_at' column.

C. Schedule a daily Snowpark script to read the entire 'CUSTOMERS' table, filter out records older than 30 days based on the 'created_at' column, and then use DataFrame.delete()' to remove those records from the table.

D. Implement a dynamic data masking policy on the PII columns instead of deleting records. Set the policy to mask data older than 30 days.

E. Implement a Snowflake Stream on the 'CUSTOMERS' table. Create another table 'CUSTOMERS ARCHIVE' with the same schema and stream data older than 30 days using 'DataFrame.copy_into_table', then truncate data from original CUSTOMERS table

正解：A,B,D 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：5

You have two Snowpark DataFrames: containing customer information and 'orders_df containing order details. You need to merge these DataFrames based on the column to create a unified view. The 'customers_df may contain duplicate values. The contains recent orders. You want to use 'merge' with an 'UPDATE action based on 'customer id' and a 'WHEN NOT MATCHED BY TARGET action to insert new customer records from 'customers df into the 'orders df table that do not exist.

A. The 'merge' operation will succeed, processing each matching record sequentially. If more than one row matches it will process based on order in the dataframe

B. You must use on the source DataFrame ('customers_df) before using 'merge' to ensure only one matching row exists per customer.

C. The 'UPDATE action in 'merge' will update all matching rows in the target table based on the join condition, regardless of duplicates in the source.

D. The merge operation cannot handle duplicate keys in the source DataFrame without pre-aggregation.

E. The 'merge' operation will fail immediately upon encountering the first duplicate key during the update operation. Consider stage the source dataframe and use an appropriate dedup option with merge.

正解：D 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：6

You have a CSV file stored in a Snowflake stage named 'my_stage/data.csv'. The file contains customer data, including 'customer id' (INT), 'first_name' (VARCHAR), 'last_name' (VARCHAR), and 'email' (VARCHAR). You want to create a Snowpark DataFrame representing this data, explicitly defining the schema for improved type safety and performance. Which of the following code snippets is the MOST efficient and correct way to create the DataFrame with the specified schema, assuming you have a valid Snowpark session object named 'session'?

正解：C 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：7

You are working with a Snowpark application designed to process data from an event table. While testing a complex transformation involving several joins and window functions, you encounter the following error: 'java.lang.OutOfMemoryError: Java heap space'. The application uses Snowpark DataFrames and is running on a reasonably sized virtual warehouse. What is the MOST likely cause of this error in the context of Snowpark and Snowflake?

A. Snowflake's internal query optimizer has generated a suboptimal execution plan, leading to excessive intermediate data materialization.

B. An inefficient UDF (User-Defined Function) is consuming excessive memory within the Java runtime.

C. There's a circular dependency in the DataFrame transformations, causing an infinite loop and memory leak.

D. The virtual warehouse is undersized for the volume of data being processed, leading to excessive spilling to disk and eventual memory exhaustion on the driver node.

E. The Snowpark driver process is attempting to load the entire result set into memory, exceeding the available heap space.

正解：E 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：8

You are tasked with optimizing a Snowpark application that performs complex geospatial calculations on a large dataset of location coordinates. The application is currently running on a standard Snowflake warehouse. Initial tests indicate that the application is CPU- bound. Which of the following actions would be MOST effective in improving the performance of this Snowpark application?

A. Switching to a Snowpark-optimized warehouse and increasing the warehouse size.

B. Enable result caching and reduce the amount of data being processed

C. Partitioning the location data based on latitude and longitude and leveraging Snowpark's DataFrame API for filtering data before geospatial calculations.

D. Switching to a larger Snowflake warehouse size (e.g., from X-Small to Small) with the same warehouse type.

E. Increasing the value of parameter at the account level.

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：9

You have created a Python UDTF in Snowpark to process large volumes of image data'. This UDTF resizes each image and extracts certain features from it. The process is memory-intensive and sometimes fails due to Python process exceeding memory limits. You need to optimize this UDTF for memory usage. Which of the following strategies would be MOST effective? (Select TWO)

A. Implement lazy evaluation and iterators within the UDTF to process images one at a time instead of loading all images into memory at once.

B. Increase the value of 'MAX BATCH SIZE in the Snowpark session configuration to allow the UDTF to process larger batches of images at once.

C. Use a scalar UDF instead of a UDTF. Scalar UDFs are generally more memory efficient.

D. Leverage external packages such as PILIPillow with appropriate image compression techniques to reduce the memory footprint of each image before processing.

E. Utilize a smaller Snowflake warehouse size. Smaller warehouses have less memory per node, which will force the UDTF to process data in smaller chunks.

正解：A,D 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：10

A Snowpark application is configured to connect to Snowflake using environment variables for authentication. However, the application frequently encounters intermittent connection errors. You suspect that the environment variables are not being correctly accessed by the Snowpark session builder. Which of the following actions could help diagnose and resolve the issue? (Select TWO)

A. Explicitly print the values of the relevant environment variables (e.g., 'SNOWFLAKE_USER , 'SNOWFLAKE_PASSWORD, 'SNOWFLAKE_ACCOUNT) within the Snowpark application before attempting to create the session.

B. Check that the environment variables are set at the correct scope (e.g., system-level, user-level, or within the application's execution environment) and are accessible to the Python process running the Snowpark application.

C. Replace 'session.builder.getorcreate()' with 'Session.builder.appName('MySnowparkApp').create()'

D. Verify that the user account specified in the environment variables has the necessary permissions (e.g., USAGE on the warehouse, database, and schema) in Snowflake.

E. Use to explicitly set connection parameters instead of relying on environment variables for debugging.

正解：A,B 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：11

You are developing a Snowpark application that needs to connect to Snowflake using programmatic access. You want to use a secure method of authentication. Which of the following methods, when passed as parameters to the 'snowpark.Session.builder.configS method, would be MOST secure and appropriate for production environments?

A. Passing the 'user' and 'password' directly, but retrieving the 'account' from an environment variable.

B. Using 'private_key' stored securely and referencing it using 'private_key_file'.

C. Setting the 'authenticator' parameter to 'snowflake' and rely on default Snowflake authentication mechanism assuming it setup correctly

D. Using 'oauth_access_token' obtained from an external OAuth server.

E. Passing the 'user', 'password', and 'account' parameters directly as strings.

正解：B,D 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：12

You are developing a Snowpark application that utilizes a DataFrame named 'transactions df containing transactional data. You need to apply a series of complex transformations, including window functions and joins with other DataFrames. To optimize performance and manage resources effectively, you want to control how Snowpark executes these operations within Snowflake. Which of the following actions or configurations would have the MOST significant impact on controlling the execution plan and resource utilization of your Snowpark application?

A. Explicitly cache the 'transactions_df DataFrame using before applying any transformations. This forces Snowpark to materialize the DataFrame in memory.

B. Implement iterative algorithms within your Snowpark application using imperative Python loops instead of declarative DataFrame operations. This provides finer-grained control over the execution flow.

C. Configure the 'net.snowflake.snowpark.use_native_execution' parameter to 'true' at the session level. This forces Snowpark to translate DataFrame operations into native Snowflake SQL queries.

D. Use the 'DataFrame.explain()' method to analyze the generated SQL query plan before executing the transformations. Then, manually optimize the code based on the query plan output.

E. Specify the 'num_partitionS parameter when creating or transforming the 'transactions_df DataFrame. This controls the number of partitions used for parallel processing.

正解：D 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：13

You are developing a Snowpark application to process customer reviews. You need to use a third-party sentiment analysis library, 'SentimentAnalyzer', which is NOT available in the Anaconda repository. You have the library JAR file stored in an internal artifact repository accessible via HTTP. Which of the following steps are necessary to make this library available to your Snowpark session?

A. Upload the JAR file toa Snowflake stage and register it as a Java UDF using CREATE FUNCTION.

B. Use 'session.add_dependency('/path/to/SentimentAnalyzer.jar')' in your Snowpark Python code after uploading the JAR to an internal stage.

C. Create a conda environment that includes the JAR, upload it to a stage, and use the environment in Snowpark.

D. Configure the Snowflake account-level parameter to point to the HTTP location of the JAR file. Then use session.add_import' to use it.

E. Upload the JAR file to a Snowflake stage. Then use 'session.add_import' to make the file available in your Snowpark session.

正解：E 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：14

A data engineering team is developing a Snowpark stored procedure to perform complex data transformations and load the results into a target table. They want to operationalize this procedure by scheduling it to run daily. Which of the following is the MOST reliable and scalable way to schedule the execution of this Snowpark stored procedure within Snowflake?

A. Use Snowflake Tasks to schedule a SQL statement that calls the stored procedure.

B. Utilize a third-party orchestration tool, such as Airflow, to schedule and monitor the execution of the stored procedure through the Snowflake connector.

C. Implement a Streamlit application that calls the stored procedure when a button is pressed.

D. Use Snowflake Pipes to ingest data and trigger the stored procedure based on new data arrival.

E. Create a Python script that uses the Snowpark API to connect to Snowflake and execute the stored procedure, then schedule the script using a Linux cron job.

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

SPS-C01試験無料問題集「Snowflake Certified SnowPro Specialty - Snowpark 認定」