You are developing a data pipeline to ingest customer feedback data from a third-party service using the Snowflake REST API. This service imposes rate limits, and exceeding them results in temporary blocking. To handle this, you implement exponential backoff with jitter. Which of the following code snippets BEST demonstrates how to correctly implement exponential backoff with jitter when calling the Snowflake REST API in Python, assuming data)' is a function that makes the API call and raises an exception on rate limiting?

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You have a Snowpark Python application that performs complex calculations on a large dataset stored in Snowflake. The application is currently running slowly. After profiling, you've identified that the UDFs you're using are the bottleneck. These UDFs perform custom data transformations using a third-party Python library which has a significant initialization overhead. Which of the following strategies would be MOST effective to optimize performance, minimizing both runtime and resource consumption?

• A. Increase the size of the Snowflake warehouse being used for the Snowpark workload. This will provide more CPU and memory resources.
• B. Implement UDF caching at the Snowflake level by setting the 'VOLATILE property to 'IMMUTABLE or 'STABLE' (if appropriate), and leverage the Snowflake query result cache.
• C. Use Snowpark's 'pandas_udf with 'vectorized=True' and pre-initialize the third-party library within the UDF's execution context using a closure or similar technique for reuse across batches.
• D. Rewrite the UDFs in SQL using Snowflake's built-in functions to avoid the overhead of Python execution. If the library's functions aren't available, consider creating external functions using a cloud provider's serverless compute service.
• E. Convert the Snowpark Python application to a Snowpark Java application as Java generally offers better performance than Python.

You are tasked with creating a development environment from a production database in Snowflake. The production database is named 'PROD DB' and contains several schemas, including 'CUSTOMER DATA' and 'PRODUCT DATA'. You want to create a clone of the 'PROD DB' database named 'DEV DB', but you only need the 'CUSTOMER DATA' schema for development purposes and all the data should be masked with a custom UDF 'MASK EMAIL' for 'email' column in 'CUSTOMER' table. The 'email' column is VARCHAR. Which of the following sequences of SOL statements would achieve this in Snowflake? Note: UDF MASK EMAIL already exists in the account.

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You are tasked with building a data pipeline that ingests customer interaction data from multiple microservices using Snowpipe Streaming. Each microservice writes data in JSON format to its own Kafka topic. You need to design an efficient and scalable solution to ingest this data into a single Snowflake table, while ensuring data integrity and minimizing latency. Consider these constraints: 1. High data volume with variable ingestion rates. 2. The need to correlate data from different microservices based on a common 'customer id'. 3. Potential for schema evolution in the microservices. Given these requirements and constraints, which of the following architectural approaches, leveraging Snowpipe Streaming features and Snowflake capabilities, would be the MOST appropriate and robust?

• A. Develop a Spark Streaming application that reads data from Kafka, transforms it, and then uses the Snowflake Connector for Spark to write the data to Snowflake in micro-batches.
• B. Implement a custom Kafka Connect connector that directly writes data to Snowflake using Snowpipe Streaming. The connector should handle schema evolution and routing based on topic name. Define a clustering key on the Snowflake table on the 'customer id'
• C. Create a separate Snowpipe Streaming client for each Kafka topic, ingesting data into separate staging tables. Then, use a scheduled task to merge the data into the final target table based on 'customer id'.
• D. Use a single Snowpipe Streaming client to ingest data from all Kafka topics into a single VARIANT column in the Snowflake table. Then, use Snowflake's external functions to transform and load the data into the final target table based on the 'customer_id'
• E. Develop a single Snowpipe Streaming client that consumes data from all Kafka topics, using a transformation function to route the data to the correct table based on the topic name. Use Snowflake's clustering key on 'customer _ id' for efficient querying.

You have an external table in Snowflake pointing to data in Azure Blob Storage. The data consists of customer transactions, and new files are added to the Blob Storage daily You want to ensure that Snowflake automatically picks up these new files and reflects them in the external table without manual intervention. However, you are observing delays in Snowflake detecting the new files. What are the potential reasons for this delay and how can you troubleshoot them? (Choose two)

• A. The file format used for the external table is incompatible with the data files in Blob Storage.
• B. Snowflake's internal cache is not properly configured; increasing the cache size will solve the problem.
• C. The external table's 'AUTO_REFRESH' parameter is set to 'FALSE', which disables automatic metadata refresh.
• D. The Azure Event Grid notification integration is not properly configured to notify Snowflake about new file arrivals in the Blob Storage.
• E. The storage integration associated with the external table does not have sufficient permissions to access the Blob Storage.