Snowflake DEA-C02 : SnowPro Advanced: Data Engineer (DEA-C02)

You are tasked with optimizing a Snowpipe Streaming pipeline that ingests data from Kafka into a Snowflake table named 'ORDERS' You notice that while the Kafka topic has high throughput, the data ingestion into Snowflake is lagging. The pipe definition is as follows: "sql CREATE OR REPLACE PIPE ORDERS_PIPEAS COPY INTO ORDERS FROM @KAFKA STAGE FILE_FORMAT = (TYPE = JSON); Which of the following actions, taken individually, would be MOST effective in improving the ingestion rate, assuming sufficient compute resources are available in your Snowflake virtual warehouse?

• A. Increase the number of Kafka partitions and ensure Snowflake has enough compute to consume them in parallel using Snowpipe Streaming.
• B. Enable auto-ingest for the Snowpipe.
• C. Increase the size of the virtual warehouse associated with the Snowflake account.
• D. Tune the parameter in the file format definition to a smaller value.
• E. Implement batching within the Kafka producer to send larger messages.

You are developing a Snowpark Python stored procedure that performs complex data transformations on a large dataset stored in a Snowflake table named 'RAW SALES'. The procedure needs to efficiently handle data skew and leverage Snowflake's distributed processing capabilities. You have the following code snippet:

Which of the following strategies would be MOST effective to optimize the performance of this Snowpark stored procedure, specifically addressing potential data skew in the 'product id' column, assuming 'product_id' is known to cause uneven data distribution across Snowflake's micro-partitions?

• A. Increase the warehouse size significantly to compensate for the data skew and improve overall processing speed without modifying the partitioning strategy.
• B. Combine salting with repartitioning by adding a random number to the 'product_id' before repartitioning, then removing the salt after the transformation to break up the skew. Then, enable automatic clustering on the 'TRANSFORMED SALES' table.
• C. Use the 'pandas' API within the Snowpark stored procedure to perform the transformation, as 'pandas' automatically optimizes for data skew.
• D. Implement a custom partitioning strategy using before the transformation logic to redistribute data evenly across the cluster.
• E. Utilize Snowflake's automatic clustering on the 'TRANSFORMED_SALES table by specifying 'CLUSTER BY when creating or altering the table to ensure future data is efficiently accessed.

A data engineer observes that a daily data transformation pipeline in Snowflake, which processes data from external stage 's3://my- bucket/raw_dataP , is consistently taking longer to complete. Upon investigation, the engineer finds that the COPY INTO statement is the bottleneck. The COPY INTO statement is as follows:

Which of the following could be the root cause of the performance degradation and how would you address them? Select two options.

• A. The virtual warehouse used for the COPY INTO operation is undersized. Increase the virtual warehouse size to improve performance.
• B. The 'ON_ERROR = 'CONTINUE" option is causing the COPY INTO statement to perform additional error handling, slowing down the process. Remove the 'ON ERROR clause to improve performance.
• C. The external stage contains a large number of small files. Snowflake's COPY INTO statement performs best with fewer, larger files. Consolidate the small files into larger files before loading.
• D. The PATTERN '. .csv' is inefficient. Refine the PATTERN to be more specific, targeting only the necessary files, potentially using date-based partitioning. If date partitioning is used, leverage partition pruning by including the appropriate date criteria in the COPY INTO statement.
• E. Snowflake automatically optimizes COPY INTO operations. No specific action is needed.

You are designing a data pipeline using Snowpipe to ingest data from multiple S3 buckets into a single Snowflake table. Each S3 bucket represents a different data source and contains files in JSON format. You want to use Snowpipe's auto-ingest feature and a single Snowpipe object for all buckets to simplify management and reduce overhead. However, each data source has a different JSON schem a. How can you best achieve this goal while ensuring data is loaded correctly and efficiently into the target table?

• A. Use a single Snowpipe with a generic FILE FORMAT that can handle all possible JSON schemas. Implement a VIEW on top of the target table to transform and restructure the data based on the source bucket.
• B. Create a separate Snowpipe for each S3 bucket. Although this creates more Snowpipe objects, it allows you to specify a different FILE FORMAT and transformation logic for each data source.
• C. Use a single Snowpipe and leverage Snowflake's ability to call a user-defined function (UDF) within the 'COPY INTO' statement to transform the data based on the S3 bucket path. The UDF can parse the bucket path and apply the appropriate JSON schema transformation.
• D. Since Snowpipe cannot handle multiple schemas with a single pipe, pre-process the data in S3 using an AWS Lambda function to transform all files into a common schema before they are ingested by the Snowpipe.
• E. Use a single Snowpipe and leverage Snowflake's VARIANT data type to store the raw JSON data. Create separate external tables, each pointing to a specific S3 bucket, and use SQL queries to transform and load the data into the target table.

A Snowflake data engineer is troubleshooting a slow-running query that joins two large tables, 'ORDERS' (1 billion rows) and 'CUSTOMER' (10 million rows), using the 'CUSTOMER ID' column. The query execution plan shows a significant amount of data spilling to local disk. The query is as follows:

Which of the following are the MOST likely root causes of the disk spilling and the best corresponding solutions? Select two options that directly address the disk spilling issue.

• A. The statistics on the tables are outdated. Run 'ANALYZE TABLE ORDERS' and 'ANALYZE TABLE CUSTOMER to update the statistics.
• B. The join operation is resulting in a large intermediate result set that exceeds the available memory. Apply a filter on the 'ORDERS' table to reduce the data volume before the join.
• C. The 'CUSTOMER_ID column is not properly clustered in either the 'ORDERS' or 'CUSTOMER table. Define a clustering key on 'CUSTOMER_ID for both tables.
• D. The virtual warehouse is undersized for the amount of data being processed. Increase the virtual warehouse size to provide more memory.
• E. The query is performing a full table scan on the 'ORDERS' table. Add an index on the 'CUSTOMER ID column in the 'ORDERS table.