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| Section | Weight | Objectives |
|---|---|---|
| Data Transformations and Operations | 35% | - DataFrame manipulation
|
| Snowpark Concepts and Architecture | 25% | - Session management and connection
|
| Performance and Best Practices | 10% | - Optimization techniques
|
| Snowpark API and Development | 30% | - Multi-language support
|
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NEW QUESTION # 271
You are using Snowpark Python to create a DataFrame from an existing Snowflake table "SALES DATA'. You want to apply a user- defined function (UDF) to each row of the DataFrame to calculate a custom sales metric. The UDF requires access to the 'session' object. Which of the following approaches is correct for defining and applying the UDF in Snowpark?
Answer: E
Explanation:
Option E is the correct approach. To access the session object from within the UDF, you can import it when registering the UDF with the session. It should be imported inside the function and use the decorator. The input_types parameter should be set to 0, since this allows for session access.
NEW QUESTION # 272
You are tasked with creating a Snowpark stored procedure to perform complex data transformations using a Pandas DataFrame. You want to optimize the performance of the stored procedure by leveraging Snowpark's distributed execution capabilities. Consider the following code snippet:
Which of the following changes to the above code will significantly improve the performance by utilizing Snowpark's distributed execution?
Answer: D
Explanation:
Option B provides the most significant performance improvement. Converting the Snowpark DataFrame to a Pandas DataFrame brings all the data to the client-side (where the stored procedure is running), negating the benefits of Snowpark's distributed processing. By performing the transformations directly on the Snowpark DataFrame using Snowpark's built-in functions, the transformations are pushed down to Snowflake's compute engine, allowing for distributed execution. Option A is incorrect as Pandas DataFrames do not leverage Snowpark's distributed processing. Option C 'session.write_pandas' is deprecated, and while it could write to the table, the computations will still happen client-side and not be distributed. Option D introduces complexity and might not be as efficient as Snowpark's native distributed execution, and it does not leverage Snowpark's optimized distributed processing within the Snowflake environment. Limiting the amount of data pulled into pandas is a helpful best practice to minimize data transfer (Option E), however, its not the most efficient. Therefore, the option that would make the largest improvement would be Option B.
NEW QUESTION # 273
You are working with a Snowpark DataFrame containing product review data'. The DataFrame has a 'review_text' column containing unstructured text reviews. Your task is to perform sentiment analysis on these reviews using Snowpark for Python. However, you are restricted to using only Snowpark built-in functions and UDFs; you cannot use external libraries like NLTK or TextBlob directly within your Snowpark code. Given this contraint, what is a valid approach to enrich your dataframe?
Answer: A,B,D,E
Explanation:
Options A, B, D and E are valid approaches given the constraints, since the task says enrich your dataframe, so we need some final column containing a score. Option A: The microservice approach, using 'call_udf and external function is a robust and scalable solution. It enables to leverage complex sentiment analysis models without directly integrating them into Snowflake. Option B: The Java UDF allows leveraging existing Java-based NLP libraries, extending the capabilities of Snowpark for sentiment analysis while adhering to the constraint of not using external Python libraries directly. Option D: Snowflake's External Functions are designed for calling external services. Passing 'review_text as an argument to pre-trained sentiment analysis model, and receive a result. Option E: This solution utilizes regular expression and UDFs. To search for sentiment indicative keywords and phrases and assigning the score using 'when' statements. Option C: This approach is the least efficient because it requires maintaining an internal vocabulary, which can be less accurate and harder to update compared to using pre-trained models or external services and it also does not generate a final new column to enrich the dataframe.
NEW QUESTION # 274
You have a Snowpark DataFrame containing sales data with columns 'sale_date', and 'sale_amount'. You need to calculate the cumulative sales amount for each product over time, ordered by 'sale_date'. Which of the following Snowpark code snippets correctly implements this using window functions?
Answer: D
Explanation:
Option A is correct. It correctly uses to group by 'product_id' and 'order_by' to sort by 'sale_date' within each product group. It then calculates the cumulative sum using Options B, C, D and E contain typos or incorrect function usage or order of arguments. 'cumulative_surn' is not a standard function provided.
NEW QUESTION # 275
You are developing a Snowpark application to process large datasets stored in Snowflake. You need to create a session using the 'snowflake.connector.connect' method. Which of the following code snippets correctly establishes a session with Snowflake, leveraging an external browser authentication mechanism, ensuring secure and reliable access to your data while minimizing exposed credentials in the code?
Answer: B
Explanation:
The correct answer is B. When using 'externalbrowser' authentication, providing the username and password in the connection string is not required, as the authentication is handled through the browser. Options A and C are incorrect because including a password with 'externalbrowser' is unnecessary and 'snowflake' is for Snowflake username/password authentication, respectively. Option D is incomplete because it doesn't specify an authenticator. Option E is missing the warehouse, database and schema, required for a functioning Snowpark session in most scenarios.
NEW QUESTION # 276
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