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NEW QUESTION # 205
You are designing a Snowpark Python application to process streaming data from a Kafka topic and land it into a Snowflake table 'STREAMED DATA. Due to the nature of streaming data, you want to achieve the following: 1. Minimize latency between data arrival and data availability in Snowflake. 2. Ensure exactly-once processing semantics to prevent data duplication. 3. Handle potential schema evolution in the Kafka topic without breaking the pipeline. Which combination of Snowpark and Snowflake features, applied with the correct configuration, would BEST satisfy these requirements? Select all that apply.
Answer: B,C
Explanation:
Options D and E represent the most reliable solutions to this problem statement. Option D: The combination of the Snowflake Connector for Kafka and Snowpark offers a balanced approach. The connector efficiently loads the raw data, and Snowpark Python provides the flexibility to transform the data within a transaction and implement schema evolution logic. Option E: Snowflake's Kafka connector, combined with tasks, streams, and a Snowpark IJDF, provides a pipeline that continuously transforms data and is only triggered by new events in the staging table created by the Kafka connector. Implementing schema evolution in the IJDF itself handles small changes effectively. Option A does not provide exactly-once semantics. While VARIANT columns handle schema evolution, Snowpipe itself might deliver messages more than once. Option B is less scalable and harder to manage compared to using the Snowflake Connector for Kafka or Streams/Tasks. Option C, using Streams on 'STREAMED_DATA' , can lead to data duplication if not managed correctly and updating an external table negates a central table stream for change control.
NEW QUESTION # 206
You are working with a Snowflake table 'customer_data' which contains customer information stored in a VARIANT column named raw_info'. The 'raw_info' JSON structure includes nested addresses, and preferences. Your task is to extract the city from the first address in the 'addresses' array, and the customer's preferred communication method from the 'preferences' object. Some customers might not have addresses or preferences defined. Select the two SQL snippets that correctly and efficiently extract this data, handling missing fields gracefully and providing appropriate type casting. Address array is in the format 'addresses: [ { 'city': '...', 'state': ' '},
Answer: D,E
Explanation:
Options D and E correctly extract the required data while handling potential null values and type conversions. Option D uses TRY_TO_VARCHAR which returns NULL if the cast fails or the value is missing. Option E uses ' IFF in conjunction with ' IS_ARRAY , 'ARRAY SIZE', and 'IS OBJECT to check for the existence and validity of the 'addresses' array and 'preferences' object, and then uses TRY_TO_VARCHAR for safe type conversion, making it very robust. Option A will throw an error if the 'addresses' array or 'preferences' object is missing. B is wrong as its where condition will filter the results. C does not handle null and type conversion.
NEW QUESTION # 207
You are designing a data protection strategy for a Snowflake environment that processes sensitive payment card industry (PCI) data'. You decide to use a combination of column-level security and external tokenization. Which of the following statements are TRUE regarding the advantages of using both techniques together? (Select TWO)
Answer: C,D
Explanation:
Combining masking policies and external tokenization provides defense in depth. Masking policies control who can see sensitive data (or a masked version), while tokenization replaces the actual data with a non-sensitive representation. This means that even if a user bypasses the masking policy (e.g., through a vulnerability), they still won't see the actual PCl data. Also, you can use column-level security to control access to the tokenization and detokenization functions. Option A is incorrect, sensitive data exists until tokenized. Option C is incorrect as using both techniques strengthens security. Option E is Incorrect, as both the techniques impact query performance
NEW QUESTION # 208
Consider the following Snowflake UDTF definition written in Python:
Which of the following statements are TRUE regarding the deployment and usage of this UDTF?
Answer: B,D
Explanation:
UDTFs in Snowflake require explicit registration using 'session.udtf.register' or 'create or replace function' command, defining the location of the source code (Python file) and the function to be executed. Also, data types of values which is 'yield'ed in the body of UDTF must strictly adhere with Output schema. Libraries from 'snowflake.snowpark' are usually available and does not needs explicit configuration. UDTFs are schema-bound, not automatically available everywhere. Direct call to UDTF without creation isn't possible.
NEW QUESTION # 209
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 # 210
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