SPS-C01無料問題集「Snowflake Certified SnowPro Specialty

質問 1

You are working with semi-structured data in Snowflake stored in a VARIANT column named 'payload'. You want to extract specific fields from this VARIANT column within a SQL query used to create a Snowpark DataFrame. Which of the following approaches allows you to access nested fields within the 'payload' column directly in the SQL query and create a corresponding column in your Snowpark DataFrame? Select all that apply.

（A）First create a temporary table containing only the extracted fields using a separate SQL query, then create a Snowpark DataFrame from that table.

（B）Extract the VARIANT data into a Pandas DataFrame and then use Pandas to access the nested fields before creating the Snowpark DataFrame.

（C）Use the "LATERAL FLATTEN(input payload)' function within the SQL query to unnest the VARIANT and then access the fields.

（D）Use the 'payload:fieldl :field2 syntax directly within the SELECT statement in the SQL query.

（E）Use the 'fieldl .field2')' function within the SELECT statement in the SQL query.

正解：C、D、E 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 2

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 "map' method on the Snowpark DataFrame's underlying RDD (Resilient Distributed Dataset) and pass 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 Snowpark's 'sprocs feature to create stored procedure and call the Python function.

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

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 3

A Snowpark application needs to manage multiple sessions concurrently, each connected to a different Snowflake warehouse for resource isolation. The application receives warehouse names dynamically at runtime. How can the application efficiently create and manage these sessions while ensuring proper resource cleanup?

（A）Create a single session object with connection pooling enabled and let Snowpark handle warehouse switching automatically based on the queries executed.

（B）Create a global session object and use 'session.use_warehouse(warehouse_name)' before each operation. This implicitly switches the warehouse for all operations on that session.

（C）Create a separate session object for each warehouse using a loop and store them in a list. Ensure each session is closed using 'session.close()' in a 'finally' block after use or using a context manager.

（D）Create a single session and create multiple Snowpark DataFrames, specifying the warehouse in each DataFrame's 'createOrReplaceTempView' method.

（E）Create separate sessions for each warehouse, but only close the last session created to avoid disconnecting all connections.

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 4

You are developing a Snowpark stored procedure to process PDF files stored in a Snowflake stage. You need to extract text from these PDF files and store the extracted text in a Snowflake table. Due to security requirements, you cannot use any external packages that require internet access. Which of the following approaches can you use to accomplish this task securely and efficiently? (Select all that apply)

（A）Use the function to read the PDF files as binary data. Implement a pure-Python PDF parsing library directly within the stored procedure to extract the text. Ensure the library code is included directly in the stored procedure code.

（B）Use Snowpark's built-in PDF parsing functions to extract the text. Snowflake provides native support for PDF parsing, eliminating the need for external libraries.

（C）Convert the PDF files to a text-based format (e.g., TXT) using an external tool before loading them into Snowflake. Then, use Snowpark to process the text files.

（D）Implement an external function using AWS Lambda or Azure Functions to parse the PDF files and extract the text. Configure the external function to have no internet access.

（E）Develop a custom Java UDF (User-Defined Function) that uses a secure, open-source PDF parsing library (e.g., PDFBox) and register it with Snowflake. Call this UDF from the Snowpark stored procedure to extract the text.

正解：A、E 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 5

You are tasked with optimizing a Snowpark Python application that performs complex geospatial calculations on a large dataset. The application experiences significant performance bottlenecks due to the computational intensity of the geospatial functions. Which of the following strategies would be MOST effective in improving performance?

（A）Disable automatic query optimization features in Snowflake to gain more control over query execution.

（B）Increase the size of the virtual warehouse to a larger instance (e.g., from X-SMALL to LARGE).

（C）Rewrite the geospatial functions using native Python libraries within the Snowpark environment.

（D）Utilize user-defined functions (UDFs) written in Java or Scala and leverage vectorized UDFs where possible.

（E）Distribute the dataset into smaller chunks using partitioning strategies within the Snowpark DataFrame API and process them independently.

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 6

You are developing a Snowpark application to process large datasets. You want to leverage asynchronous jobs to improve performance and prevent blocking the main thread. You have the following code snippet:

（A）Implement a callback function using 'job.on_success(callback_function)' and 'job.on_error(error_function)' to handle the result or any errors asynchronously.

（B）Use 'job.getQueryld(Y to fetch the query ID and query the execution status directly from Snowflake using SQL. This is inefficient and bypasses the Snowpark API.

（C）Call 'job.result(Y without any error handling. Snowflake will automatically handle any errors and return a null result.

（D）Continuously check 'job.status' in a loop until it returns 'SUCCESS', then retrieve the result using 'job.result()'. This is less efficient due to polling.

（E）Use to retrieve the result with a timeout of 30 seconds, catching 'TimeoutError' if the job takes too long. This is the only way to get the result safely.

正解：A 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 7

You have a Snowpark DataFrame containing customer order data with columns , and 'order_amount' . You need to identify customers who placed orders exceeding $1000 on more than 3 separate days. Which Snowpark code snippet correctly achieves this? Assume SparkSession 'spark' and DataFrame are already defined.

（A）

（B）

（C）

（D）

（E）

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 8

You are using Snowpark Python to process a DataFrame containing customer data,. One of the columns, 'phone_number' , contains phone numbers in various formats (e.g., '123-456-7890', '(123) 456-7890', '1234567890'). You need to standardize these phone numbers to the format 'XXX-XXX-XXXX' using a User-Defined Function (UDF). You want to create a UDF called 'standardize_phone_number' that takes a string as input and returns the standardized phone number. Which of the following code snippets correctly defines and registers this UDF in Snowpark, and applies it to the 'phone_number' column of the 'customer df DataFrame? Assume a Snowflake session object called 'session' is already available.

（A）

（B）

（C）

（D）

（E）

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 9

You have two Snowpark DataFrames: 'employees_df with columns 'employee_id' (INTEGER), 'employee_name' (STRING), 'department_id' (INTEGER), and 'salaries_df' with columns 'employee_id' (INTEGER), "salary' (FLOAT), 'effective_date' (DATE). You need to create a new DataFrame that contains the employee's name, department, and the highest salary they have ever received. Assuming there can be multiple salary entries for the same employee with different 'effective date' values, which of the following Snowpark code snippets would correctly and efficiently solve this problem?

（A）

（B）

（C）

（D）

（E）

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 10

You have developed a Snowpark Python stored procedure that calculates the average sales per region from a large sales data table. The procedure is currently defined inline within your Snowflake notebook. You want to operationalize this by creating the stored procedure from a local Python file named The file contains the following code: "'python from snowflake.snowpark.session import Session def calculate_avg_sales(session: Session, sales_table_name: str, region_column: str, sales_column: str) -> float: sales df = session.table(sales table name) avg_sales df = sales_df.group_by(region_column).agg({sales_column: 'avg'}) avg_sales = avg_sales_df.collect() return avg_sales[0][1] Which of the following code snippets correctly creates the stored procedure 'AVG SALES PROC in Snowflake, referencing the Python file, and handles potential dependency issues? Assume you have already established a Snowpark session named 'session' and that the stage 'my_stage' already exists.

（A）

（B）

（C）

（D）

（E）

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 11

You have a Snowpark DataFrame named containing daily sales transactions. The DataFrame includes columns like 'transaction_id' , 'product id', 'sale_date', and 'sale_amount'. You need to perform the following transformations and persist the results: (1) Calculate the total sales amount for each product on a daily basis. (2) Store the aggregated results into a new table named , partitioned by 'sale_date'. (3) Ensure that if the table already exists, the new data is appended to the existing table. Which of the following code blocks achieve these requirements in the most efficient and correct manner?

（A）

（B）

（C）

（D）

（E）

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 12

Consider the following Snowpark code snippet designed to process data from an event table and calculate a rolling average:

After deploying this code, you observe that the rolling average calculation is significantly slower than expected, even though the virtual warehouse is adequately sized. What is the MOST effective way to optimize the performance of this rolling average calculation in Snowpark, considering the size of window_size ?

（A）Reduce the 'window_size' parameter. Smaller windows inherently require less computation.

（B）Use the 'rangeBetween' method instead of 'rowsBetween' in the 'Windows specification, as it is generally more efficient for numerical data.

（C）Utilize Snowflake's APPROX AVG aggregate function in conjunction with a custom UDF to estimate the rolling average, trading off accuracy for performance.

（D）Use the SNOWFLAKML.FORECAST package in the Snowpark DataFrame to forecast the rolling average of events in each of the user session.

（E）Materialize the intermediate 'event_df DataFrame into a temporary table using before applying the window function.

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 13

You have two Snowpark DataFrames, 'dfl' and 'df2', representing customer data'. 'dfl' contains customer IDs and names, while 'df2' contains customer IDs and email addresses. You need to create a new DataFrame that contains all customer IDs, names, and email addresses, including customers present in only one of the DataFrames. Which Snowpark set operation and join type would be most appropriate for achieving this?

（A）UNION and FULL OUTER JOIN

（B）DISTINCT and CROSS JOIN

（C）UNION ALL and INNER JOIN

（D）INTERSECT and LEFT JOIN

（E）EXCEPT and RIGHT JOIN

正解：A 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 14

You have a Snowpark DataFrame named 'orders df with columns 'order id', 'customer id', 'order date', and 'total amount'. You need to create a new DataFrame that contains only the 'customer_id" and the total number of orders placed by each customer. However, you want to perform this aggregation in parallel using a user-defined function (UDF) to improve performance. Which approach is MOST efficient and CORRECT?

（A）Create a UDF that takes a customer ID as input and returns the count of orders for that customer, then apply this UDF to each distinct customer ID using 'map'.

（B）Create a UDF that performs the entire aggregation and call it with 'orders_df. The UDF uses a Pandas DataFrame internally to perform the count.

（C）Create a UDF that takes an iterator of order IDs and returns the count. Apply this UDF to each partition of the DataFrame using 'maplnPartitions' .

（D）Create a UDF that calculates the mode of total_amount, then apply the UDF to the DataFrame using 'select'.

（E）Use 'groupBy' to group by 'customer_id' and then use the 'count' aggregate function. Do not use a UDF.

正解：E 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 15

You have a Snowpark DataFrame 'customer df with a 'customer name' column. You need to create a new column 'initials' that contains the initials of each customer's name. For example, if 'customer name' is 'John Doe', 'initials' should be 'JD'. You must handle names with multiple words correctly. Which Snowpark SQL expression using the "col()' function is the most efficient and correct way to define the 'initials' column?

（A）

（B）

（C）

（D）

（E）

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

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

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