QUESTION 188
You are developing a Snowpark application using Visual Studio Code and the Snowflake VS Code extension. You want to configure the extension to automatically detect and use a specific Anaconda environment for your Snowpark development. Assuming you have already created an Anaconda environment named ‘snowpark_env’, which configuration setting in the VS Code settings.json file would correctly specify the Python path for the Snowflake extension?

QUESTION 189
You have a Snowpark Python application that performs several data transformations on a DataFrame representing customer transactions. The application is experiencing performance issues, and you suspect that some transformations are unnecessarily expensive. Which of the following techniques can MOST effectively optimize the performance of your Snowpark application, specifically focusing on minimizing data movement and leveraging Snowflake’s query optimization capabilities?

QUESTION 190
Consider the following Snowpark Python code snippet for creating a stored procedure:

What is the PRIMARY reason for explicitly defining ‘input_types’ and during the stored procedure registration?

QUESTION 191
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:

QUESTION 192
You have a Snowpark Python application that performs complex data transformations and machine learning model training. The data is stored in Snowflake tables. You notice that model training jobs, specifically those involving large feature sets and iterative algorithms, are consistently slow. The warehouse is already scaled to a LARGE size. Which of the following techniques, when applied individually or in combination, would MOST likely improve the performance of model training in Snowpark?

QUESTION 193
A Snowpark developer is using to create a Snowpark session. They want to ensure that the session uses a specific role and warehouse, but only if those parameters are not already defined in the Snowflake CLI configuration. Which of the following code snippets correctly implements this behavior?

QUESTION 194
You are tasked with building a Snowpark application that processes sensor data. The data arrives continuously and is ingested into a Snowflake table called ‘RAW SENSOR DATA’. You need to create a Snowpark DataFrame that applies a user-defined function (UDF) to each row to enrich the data. The UDF, named ‘ENRICH SENSOR DATA, is written in Python and resides in a stage called ‘UDF STAGE. The UDF takes three arguments: ‘timestamp’, and ‘raw_value’, all of which are STRING type in Snowflake. Which of the following code snippets correctly defines and calls the UDF using Snowpark?

QUESTION 195
Consider a DataFrame ‘products df loaded from a SnoMlake table. It contains a ‘features’ column of type VARIANT, where each row contains a JSON object representing product features. Your task is to create a new DataFrame where each feature becomes a separate column. You need to dynamically extract these features without knowing the specific feature names in advance. Which of the following approaches could achieve this using Snowpark, and what considerations are important? Choose all that apply:

QUESTION 196
You have a Python function, ‘calculate metrics(df: snowpark.DataFrame, metric name: str) -> snowpark.DataFrame’, that calculates various metrics on a Snowpark DataFrame. You want to deploy this function as a stored procedure in Snowflake. You need to ensure that the stored procedure has appropriate permissions to read data from a table named ‘customer data’ and write results to a table named ‘metrics_table’. Which of the following steps are necessary to achieve this, assuming you are using the ‘session.sproc.register’ method?

QUESTION 197
You are building a Snowpark application that requires you to connect to Snowflake from an environment where directly specifying credentials in the code is not permitted for security reasons. Which of the following are valid and recommended ways to securely pass authentication information to the Snowpark Session?

QUESTION 198
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.

QUESTION 199
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?

QUESTION 200
You have a Snowflake table ‘orders_json’ with a VARIANT column named “order details’. This column contains JSON objects, and one of the fields within these objects is an array called ‘items’. You need to use Snowpark to flatten the ‘items’ array into rows, extracting the ‘item_id’ , ‘item_name’ , and ‘quantity’ from each item in the array. Which of the following Snowpark code snippets will correctly achieve this, assuming ‘df is a DataFrame representing ‘orders_json’?

QUESTION 201
You are optimizing a Snowpark application that performs complex data transformations on a large dataset. The transformation involves multiple joins and aggregations. You notice that the query execution time is excessive. Which of the following techniques would be MOST effective in improving the performance of this application, assuming you have the appropriate Snowflake role and privileges?

QUESTION 202
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.

QUESTION 203
A data engineer has developed a Snowpark Python stored procedure, ‘calculate daily summary’, that processes sales data and generates a daily summary table. The procedure takes a date string as input and writes the summary to a table named ‘DAILY SALES SUMMARY’. The engineer needs to operationalize this stored procedure to run automatically every day at midnight. Which of the following approaches is the MOST efficient and reliable way to schedule and execute this Snowpark stored procedure in Snowflake?

QUESTION 204
A data engineering team is developing a Snowpark application that processes large volumes of JSON data’. They have created a UDF using Python that parses JSON strings and extracts specific fields. They need to deploy this UDF and ensure it can handle malformed JSON without causing the entire Snowpark job to fail. Which of the following strategies BEST addresses both the deployment and error handling requirements?

QUESTION 205
A data engineering team is using Snowpark Python to build a data pipeline. They need to create a User-Defined Function (UDF) that transforms a JSON string column representing customer information into a STRUCT type containing flattened fields for ‘name’, ‘age’, and ‘city’. The UDF should handle null values gracefully and return NULL if the input JSON is invalid or if the ‘name’ field is missing. Considering performance implications and error handling, which of the following approaches is MOST optimal for defining and registering this UDF?