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NEW QUESTION # 328
You are using the Snowflake Spark connector to update records in a Snowflake table based on data from a Spark DataFrame. The Snowflake table 'CUSTOMER' has columns 'CUSTOMER ID' (primary key), 'NAME, and 'ADDRESS'. You have a Spark DataFrame with updated 'NAME and 'ADDRESS' values for some customers. To optimize performance and minimize data transfer, which of the following strategies can you combine with a temporary staging table to perform an efficient update?
Answer: A,C
Explanation:
Options A and D are the most efficient. Writing to a temporary table allows Snowflake to perform the update operation using its optimized internal processes. The MERGE command is designed for efficient upserts and is preferred over individual UPDATE statements. Option B is highly inefficient due to the overhead of multiple individual queries. Option C is also not optimal, as UDFs don't necessarily improve performance for simple UPDATE operations and broadcasting data is not needed. Option E can also be an approach for batch update, its effective way for the performance but its less compared with option A and D.
NEW QUESTION # 329
You are designing a data governance strategy for a Snowflake data warehouse. You need to track data lineage for compliance purposes. Specifically, you need to identify all downstream tables that depend on a specific column in a source table. Which combination of Snowflake features and techniques would you use to achieve this goal effectively?
Answer: E
Explanation:
Snowflake's data lineage features provide a direct and integrated solution for tracking data dependencies. Object tagging enhances this by allowing you to identify and categorize sensitive data, making it easier to trace lineage for specific data elements. The other options are either manual (A, D), require significant custom development (C, E), or don't leverage Snowflake's built-in capabilities.
NEW QUESTION # 330
You are designing a data recovery strategy for a critical table 'CUSTOMER DATA' in your Snowflake environment. The data in this table is highly sensitive, and regulatory requirements mandate a retention period of at least 90 days for potential audits. You need to configure the Time Travel retention period to meet these requirements. What is the maximum supported Time Travel retention period, and how would you set it at the table level?
Answer: C
Explanation:
For Snowflake Enterprise Edition (or higher), the maximum Time Travel retention period is 90 days. The 'ALTER TABLE ... SET DATA RETENTION_TIME IN DAYS' command allows setting the retention period at the table level. Option D is partially correct about editions impacting limits, but incorrect about account-level settings only.
NEW QUESTION # 331
A data engineer is tasked with implementing a data governance strategy in Snowflake. They need to automatically apply a tag 'PII CLASSIFICATION' to all columns containing Personally Identifiable Information (PII). Given the following requirements: 1. The tag must be applied as close to data ingestion as possible. 2. The tagging process should be automated and scalable. 3. The tag value should be dynamically set based on a regular expression match against column names and data types. Which of the following approaches would be MOST effective and efficient in achieving these goals?
Answer: C
Explanation:
Option C is the most effective because it leverages Snowflake's native event capture mechanisms (Event Tables, Streams and Tasks) to react to DDL changes in near real-time. This approach is automated, scalable, and avoids the overhead of periodic polling. Options A and B involve periodic scanning which is less efficient. Option D is manual and doesn't scale. Option E introduces unnecessary complexity with external functions and ML models for a relatively simple task, increasing operational overhead.
NEW QUESTION # 332
You are developing a Snowpark Python application that transforms data from a source table "ORDERS RAW' into a target table 'ORDERS CLEANED'. The transformation involves multiple steps, including data validation, cleansing, and aggregation. You need to ensure that either all steps succeed, or none of them do, to maintain data integrity. You are considering different approaches to transaction management. Which of the following strategies offer the MOST comprehensive and reliable approach to manage transactions in this scenario, especially considering potential network interruptions and session timeouts?
Answer: C
Explanation:
The 'with session.transaction_context() as transaction:' statement provides the most reliable and concise way to manage transactions in Snowpark Python. This automatically handles transaction lifecycle, including committing if successful and rolling back if exceptions arise. Options A and C do not provide sufficient control over transaction boundaries. Option B requires explicit handling of commit, rollback, and retry logic, increasing code complexity. Option D introduces complexity with temporary tables. Using transaction_context automatically manages the session and provides the most robust solution to a potentially unstable network or long-running operation.
NEW QUESTION # 333
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