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| Section | Objectives |
|---|---|
| Topic 1: DataFrame Operations and Data Processing | - Data transformation workflows
|
| Topic 2: Snowpark Fundamentals | - Snowpark architecture and concepts
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| Topic 3: Data Engineering with Snowpark | - Pipeline development
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| Topic 4: Testing, Debugging, and Deployment | - Production readiness
|
| Topic 5: User Defined Functions and Stored Procedures | - Extending Snowpark with custom logic
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| Topic 6: Performance Optimization and Best Practices | - Efficient Snowpark execution
|
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NEW QUESTION # 337
You have a Python function named 'process data' that performs data cleaning and transformation on a Pandas DataFrame. You want to convert this function into a Snowpark Python stored procedure to leverage Snowflake's compute resources. However, the 'process_data' function relies on several external Python libraries (e.g., 'pandas', 'numpy', 'scikit-learn') that are not pre-installed in the Snowflake environment. Which of the following approaches would ensure that these dependencies are available within the Snowpark stored procedure? Choose all that apply
Answer: C,D
Explanation:
Options B and D are the correct ways to handle external dependencies for Snowpark Python stored procedures. Option B: The packages argument of the '@sproc' decorator or 'session.add_packages' method is the most straightforward way to specify dependencies. Snowflake will automatically download and install these packages from its Anaconda channel. Option D: Bundling libraries into a ZIP file and uploading it to a stage is a valid approach when you need to use specific versions of libraries or libraries that are not available in the Snowflake Anaconda channel. However, it requires careful management of library paths within the Python code. Option A is incorrect. Snowflake does not automatically resolve and install dependencies based solely on 'import' statements. Option C is a more complex approach and is generally not necessary unless you have very specific requirements for package versions or custom packages. It's often easier to use the 'packages' argument for standard libraries. Option E : there is no session.custom_package in snowpark python API.
NEW QUESTION # 338
You have a Snowpark DataFrame named with columns 'category', , and You want to perform the following transformations using Snowpark:
Answer: C
Explanation:
Option E is correct, because the 'pivot' operation needs to be inside 'groupBy' . It first groups the data by 'category', then pivots the data based on the 'date' column, aggregating the 'value' column using the sum function. Options A,B,C, and D, will cause a Snowflake error.
NEW QUESTION # 339
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)
Answer: C,D,E
Explanation:
Options A, B and D provide valid ways to fetch 'discount_rate' as a single numerical value. And fetch the data and gets the first value from the first row. Similarly, gets the data and return the first row. However, Option C does not have LIMIT 1 and will not work. Option E fetches one row as one array, thus requires rate[0] to compute discounted_price.
NEW QUESTION # 340
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,C,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 # 341
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?
Answer: B
Explanation:
Option D first flattens the array of order items using 'flatten' , creating a new row for each item. It then calculates the product of 'quantity' and 'price' for each item and sums these products to get the total price. Option A and C will not work, because they doesn't flatten the array first. Option E should use flatten instead of explode.
NEW QUESTION # 342
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