You are using Snowpipe with an external function to transform data as it is loaded into Snowflake. The Snowpipe is configured to load data from AWS SQS and S3. You observe that some messages are not being processed by the external function, and the data is not appearing in the target table. You have verified that the Snowpipe is enabled and the SQS queue is receiving notifications. Analyze the following potential causes and select all that apply:

• A. The IAM role associated with the Snowflake stage does not have permission to invoke the external function. Verify that the role has the necessary permissions in AWS IAM.
• B. The Snowpipe configuration is missing a setting that allows the external function to access the data files in S3. Ensure that the storage integration is configured to allow access to the S3 location.
• C. The external function is experiencing timeouts or errors, causing it to reject some records. Review the external function logs and increase the timeout settings if necessary.
• D. The AWS Lambda function (or other external function) does not have sufficient memory or resources to process the incoming data volume, leading to function invocations being throttled and messages remaining unprocessed.
• E. The data being loaded into Snowflake does not conform to the expected format for the external function. Validate the structure and content of the data before loading it into Snowflake.

Your company has a Snowflake account in the AWS cloud (us-west-2). You are planning to implement a disaster recovery strategy by replicating data to a separate Snowflake account in the Azure cloud (eastus2). You need to replicate multiple databases and shared objects. Which of the following steps are REQUIRED to configure and manage the replication process successfully? (Choose all that apply)

• A. Grant the REPLICATE privilege on the source AWS account to the account locator of the target Azure account.
• B. Create a replication group in the source AWS account and add the databases and shared objects to it.
• C. Create a secondary database in the target Azure account using the 'CREATE DATABASE AS REPLICA OF command.
• D. Create a storage integration in the target Azure account and grant the 'USAGE privilege on it to the replication group.
• E. Configure network policies in both AWS and Azure accounts to allow communication between the Snowflake instances, particularly ingress and egress rules.

You are implementing a data pipeline in Snowpark that reads data from an external stage (e.g., AWS S3) and performs complex transformations, including joins with large Snowflake tables. You notice that the pipeline's performance is significantly slower than expected, despite having sufficient warehouse resources. Which of the following actions would MOST likely improve the performance of the Snowpark data pipeline?

• A. Increase the warehouse size to the largest available option (e.g., X-Large or larger).
• B. Reduce the number of partitions in the DataFrame representing the data from the external stage using 'df.repartition(l )'.
• C. Persist the DataFrame representing the data from the external stage using 'df.cache()' before performing the joins.
• D. Optimize the SQL joins within the Snowpark DataFrame operations by using broadcast joins when appropriate and ensuring correct join key data types.
• E. Ensure that the external stage is properly configured with appropriate data formats (e.g., Parquet) and partitioning schemes that align with the join keys.

A data engineer is working with a Snowpark DataFrame 'sales df containing sales data with columns 'product id', 'sale_date', and 'sale amount'. The engineer needs to calculate the cumulative sales amount for each product over time. Which of the following code snippets using window functions correctly calculates the cumulative sales amount, ordered by 'sale date'?

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

You're building a data pipeline that ingests JSON data from URLs representing real-time weather information. The data structure varies slightly between different weather providers, but all contain a 'location' object with 'city' and 'country' fields, and a 'temperature' field. You need to create a generic function that can handle these variations and extract the location and temperature, returning a flattened JSON object with keys 'city', 'country', and 'temperature'. You want to avoid explicit schema definition and take advantage of Snowflake's VARIANT data type flexibility Given the following sample JSON structures, which approach will best accomplish this?

• A. Define a Snowflake stored procedure that uses 'SYSTEM$URL_GET to fetch the JSON data, then uses conditional logic with 'TRY TO BOOLEANS and STRY TO DATE to handle different data types. The stored procedure constructs a new JSON object with 'city', 'country', and 'temperature' fields using 'OBJECT_CONSTRUCT.
• B. Create a pipe that uses 'COPY INTO to ingest JSON data directly from the URLs into a VARIANT column. The 'FILE FORMAT object is configured to use = TRUE to handle different data types. Post ingestion create a view to query data.
• C. Define a Snowflake view that selects from a table containing the URLs, using 'SYSTEM$URL GET to fetch the JSON data and to extract the 'city', 'country', and 'temperature' fields. Use 'TRY_CAST to convert the 'temperature' to a numeric type.
• D. Create a Snowflake external function written in Java that uses 'java.net.lJRL' to fetch the JSON data and 'com.fasterxml.jackson.databind' library to parse it. Use Jackson's 'JsonNode' to navigate the varying JSON structure and extract 'city', 'country', and 'temperature' fields. Return a JSON string of the result.
• E. Define a Snowflake external function (UDF) that fetches the JSON data using a Python library like 'requests' or The function then parses the JSON and extracts the required fields, handling potential missing fields using 'try...except' blocks. The function returns a JSON string representing the flattened object.