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| Section | Weight | Objectives |
|---|---|---|
| Snowpark API and Development | 30% | - Python API fundamentals
|
| Data Transformations and Operations | 35% | - User-defined logic
|
| Performance and Best Practices | 10% | - Optimization techniques
|
| Snowpark Concepts and Architecture | 25% | - Session management and connection
|
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NEW QUESTION # 322
A data science team wants to operationalize a Snowpark Python UDF that performs sentiment analysis on customer reviews. The UDF, 'analyze sentiment(review_text)', is currently defined within a Snowpark session. Which of the following approaches is the MOST efficient and scalable way to deploy this UDF for real-time scoring of incoming review data in a Snowflake table named 'CUSTOMER REVIEWS'?
Answer: E
Explanation:
Registering the UDF as a persistent UDF in Snowflake allows it to be called directly from SQL, leveraging Snowflake's query engine for optimal performance and scalability. Option A keeps the transformation entirely within Snowpark, which is a valid approach, but less scalable. Option C involves converting to a Pandas DataFrame, which moves data outside of Snowflake and negates its benefits. Option D creates a stored procedure that is correct but the question ask to perform in real time so it's not ideal. Option E is not the way to persist Snowpark with UDF.
NEW QUESTION # 323
Consider a Snowflake table named 'raw events' containing web event data in a VARIANT column called 'event data'. Each 'event_data' JSON object may contain an array of product IDs under the key 'product_ids', and another array of user IDs who interacted with those products under the key 'user_ids'. These arrays are not guaranteed to be the same length, and a missing array should be handled gracefully (return null/empty result for that row rather than throwing error). Your objective is to create a Snowpark DataFrame that flattens both arrays into rows, pairing each product ID with each user ID for the specific event, generating as many rows as possible, and including event timestamp. You can use a zero-based index for the array elements using 'ARRAY CONSTRUCT()' and then using that array for using the EXPLODE function. Which of the following are necessary steps to fulfill this requirement and which are not?
Answer: C,D,E
Explanation:
Options A, C, and E are correct. A: Using and along with ensures dynamic index array generation and graceful handling of missing or non-numeric dat a). C: Using 'LEFT OUTER JOIN LATERAL FLATTEN' allows both 'product_ids' and 'user_ids' arrays to be flattened, even if one or both are missing. The LEFT JOIN ensures all rows from the original data are kept. The use of handles cases where either array is null or empty, preventing errors and providing default values. E:Using or 'case when' handles exception and different length of arrays.
NEW QUESTION # 324
A data engineer is tasked with transforming a large dataset of customer transactions using Snowpark Python. The dataset contains personally identifiable information (PII) that needs to be masked before further analysis. They decide to use a UDF to perform the masking. Consider the following Python UDF:
The engineer registers this UDF and attempts to apply it to a column named 'customer email' in a Snowpark DataFrame named 'customer data'. Which of the following code snippets is the MOST efficient and secure way to apply this UDF and replace the 'customer email' column with the masked values?
Answer: B
Explanation:
The most efficient and secure way is to directly call the UDF on the DataFrame column using 'with_column'. This leverages Snowpark's lazy evaluation and avoids unnecessary data movement. It also modifies the original 'customer_email' column as requested. Option A requires the string name of the UDF instead of the function and option D adds a new column named 'masked_email' instead of replacing customer_email. Option B and E do not replace the current 'customer_email' column.
NEW QUESTION # 325
When using key pair authentication with Snowpark, what security best practices should you implement to protect your private key?
(Select all that apply)
Answer: B,C,D
Explanation:
Storing the private key directly in the code repository (A) is a major security risk. Encrypting the private key at rest (B) provides an additional layer of security. Storing the private key in an environment variable or secure secret management system (C) is the recommended approach. Granting broad access (D) increases the risk of compromise. Regularly rotating the key pair (E) limits the impact if a key is compromised. Options B,C and E are the most secure ones.
NEW QUESTION # 326
Given a Snowpark DataFrame 'df with a column named 'data' of VARIANT type, where the VARIANT contains JSON objects with nested fields. You need to extract the value of the nested field 'address.city' as a STRING and the value of as a DOUBLE, handling cases where either 'address' or 'items' might be missing. Which combination of Snowpark functions is best suited to achieve this robustly and efficiently?
Answer: C
Explanation:
Option C is the most robust because it uses 'get_patW combined with and 'get_patm is specifically designed for extracting nested fields from VARIANT data, and handle cases where the path doesn't exist (returning NULL instead of an error).option A will fail if 'address' or 'items' is missing. Option B doesn't handle the NULL cases gracefully and 'to_double' expects a string not a variant. Option D doesn't handle the NULL cases gracefully and used 'to_varchaff which is deprecated. Option E will fail if 'address' or 'items' is missing and doesn't use get_path
NEW QUESTION # 327
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