You are using Snowpark Python to perform data transformation on a large dataset stored in a Snowflake table named customer transactions'. This table contains columns such as 'customer id', 'transaction date', 'transaction amount', and product_category'. Your task is to identify customers who have made transactions in more than one product category within the last 30 days. Which of the following Snowpark Python snippets is the most efficient way to achieve this, minimizing data shuffling and maximizing query performance?

• A. Option A
• B. Option E
• C. Option B
• D. Option C
• E. Option D

You need to define a UDF in Snowflake that takes a date as input and returns the next business day (Monday-Friday). If the input date is a Friday, the UDF should return the following Monday. If the input date is a Saturday or Sunday, the function should return the following Monday as well. Which of the following UDF definitions correctly implements this logic?

• A. Option A
• B. Option E
• C. Option B
• D. Option C
• E. Option D

You are designing a data loading process for a high-volume streaming data source. The data arrives as Avro files in an AWS S3 bucket. You need to load this data into a Snowflake table with minimal latency and operational overhead. Which of the following combinations of Snowflake features and configurations would be MOST suitable for this scenario? (Select TWO)

• A. Use a Kafka connector to stream data directly from the Kafka topic to Snowflake.
• B. Use the 'COPY INTO' command with a scheduled task that runs every 5 minutes to load new files from the S3 bucket.
• C. Create a custom Spark application that reads Avro files from S3, transforms the data, and then writes it to Snowflake using the Snowflake Spark connector.
• D. Implement Snowpipe with auto-ingest configured to listen for S3 event notifications whenever a new Avro file is added to the bucket.
• E. Configure an external table pointing to the S3 bucket and query the Avro files directly from Snowflake.

A data engineering team is implementing a change data capture (CDC) process using Snowflake Streams on a table 'CUSTOMER DATA'. After several days, they observe that some records are missing from the target table after the stream is consumed. The stream 'CUSTOMER DATA STREAM' is defined as follows: 'CREATE STREAM CUSTOMER DATA STREAM ON TABLE CUSTOMER DATA;' and the transformation code to process the data is shown below. What could be the possible reasons for the missing records, considering the interaction between Time Travel and Streams? Assume all table sizes are significantly larger than micro-partitions, making full table scans inefficient.

• A. The underlying table 'CUSTOMER DATA' was dropped and recreated with the same name, invalidating the stream's tracking capabilities.
• B. DML operations (e.g., DELETE, UPDATE) performed directly against the target table are interfering with the stream's ability to track changes consistently.
• C. The stream's 'AT' or 'BEFORE clause in the consumer query is incorrectly configured, causing it to skip some historical changes.
• D. The stream's offset persistence is reliant on Time Travel, If the data being ingested is older than the set Time Travel duration, the change may not be seen by the stream.
• E. The parameter for the database containing 'CUSTOMER_DATR is set to a value lower than the stream's offset persistence, causing some changes to be purged before the stream could consume them.

You have implemented a masking policy on the 'SSN' column of the 'EMPLOYEES' table. You now need to suspend the masking policy temporarily for a specific batch job that requires access to the unmasked data'. What is the recommended way to achieve this without dropping the masking policy or altering the user's role?

• A. Grant the 'APPLY MASKING POLICY privilege to the user running the batch job, allowing them to bypass the masking policy.
• B. Create a temporary view on the 'EMPLOYEES' table without the 'SSN' column, grant access to the view for the batch job, and drop the view after the job is complete.
• C. Create a new role with 'ACCOUNTADMIN' privileges, assign this role to the batch job process during the execution, and then revert back to the original role after the job is done.
• D.
  
• E. Set the 'DISABLE MASKING' session parameter to 'TRUE' for the batch job session. This will temporarily disable all masking policies.