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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider a Snowflake table 'sales_data' with a VARIANT column 'order_details' containing an array of JSON objects, where each object represents an item in an order. Each item object has fields like 'quantity', and 'price'. You need to calculate the total price for each order by summing the product of 'quantity' and 'price' for all items in the 'order_details' array. Which of the following Snowpark Python snippets correctly accomplishes this?

A)

B)

C)

D)

E)

2. Consider the following Snowpark Python code snippet that defines and registers a User-Defined Table Function (UDTF):

Which of the following statements is MOST accurate regarding the behavior and limitations of this UDTF when used in a Snowpark DataFrame transformation?

A) The UDTF will process each input string in parallel, with Snowflake automatically distributing the processing across multiple worker nodes.
B) The 'input_string' argument passed to the 'process' method will always be a single string value, even if the input DataFrame column contains NULL values.
C) The UDTF can only be used with DataFrames that have been explicitly persisted as Snowflake tables.
D) If the input DataFrame column contains NULL values, the 'process' method will receive 'None' as the value for 'input_string'. The 'output_schema' correctly defines the structure of the output rows.
E) The UDTF will be executed within the same Python process as the Snowpark driver program, limiting its scalability for large datasets.

3. You're developing a Snowpark Python application to process log files stored in an external stage 's3_logs'. These logs are in plain text, with each line representing a log entry. You need to filter log entries based on a specific keyword and extract timestamps from the matching lines. Which of the following approaches, using and Snowpark DataFrames, will efficiently accomplish this, avoiding unnecessary data transfer to the client?

A) Download each log file using 'SnowflakeFile.get' , read the file line by line in the client application, filter lines based on the keyword, extract timestamps, and then create a Snowpark DataFrame with the extracted timestamps.
B) Create an external function that accesses the files directly from S3, performs the filtering and timestamp extraction, and returns the results. Then create a Snowpark DataFrame to invoke the external function.
C) Create a UDF that accepts a 'SnowflakeFile' object as input, reads the file line by line inside the UDF, filters lines based on the keyword, extracts timestamps, and returns a list of timestamps. Call this UDF on a Snowpark DataFrame created using 'session.read.option('PATTERN', ' .10g').format('CSV').load('@s3_logs'V.
D) Use 'session.read.option('PATTERN', ' to load all log files into a Snowpark DataFrame with a single column containing the log lines. Create a UDTF that reads a partition of the DataFrame, filters lines based on the keyword, extracts timestamps, and returns a table of timestamps.
E) Use pattern=' .log')' to load all log files into a Snowpark DataFrame with a single column containing the log lines. Then, filter the DataFrame using like' to find lines containing the keyword and extract timestamps using regular expressions in a subsequent select statement.

4. Consider the following Snowpark Python code snippet designed to aggregate sales data by region:

During testing, you observe that the performance of this code is suboptimal, especially when dealing with very large 'SALES DATA tables. Using Snowflake's query history, you notice that a significant amount of time is spent on data shuffling during the operation. What optimization strategies could you employ within this Snowpark code to minimize data shuffling and improve the overall performance?

A) Increase the warehouse size to provide more resources for data processing, which will inherently reduce data shuffling.
B) Utilize the 'hint' function in Snowpark to provide a join hint that suggests a specific join strategy to the Snowflake query optimizer.
C) Apply a more selective 'filter operation to the 'sales_df' DataFrame before the 'groupBy' operation, reducing the amount of data that needs to be shuffled.
D) Repartition the 'sales_df DataFrame using 'repartitionBy' before the 'groupBy' operation, specifying the 'REGION' column to ensure that data for each region is co-located on the same node.
E) store 'SALES_DATX as a clustered table using the 'REGION' column.

5. 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)

Solutions: