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

1. You're loading data into a Snowflake table using 'COPY INTO'. You notice that some rows are being rejected due to data validation errors (e.g., data type mismatch, uniqueness constraint violations). You want to implement a strategy to capture these rejected rows for further analysis and correction. Which of the following approaches offers the MOST efficient and reliable method for capturing and storing the rejected rows, minimizing performance impact during the data loading process? Assume no staging table exists before loading data to production table.

A) Option D
B) Option A
C) Option C
D) Option B
E) Option E

2. You are troubleshooting a slowly performing query in Snowflake that aggregates data from a large ORDERS table (10 billion rows) partitioned by ORDER DATE. The query execution plan shows significant 'Remote Spill to Disk'. Which of the following actions would be MOST effective in reducing the spill and improving query performance? Assume all statistics are up-to-date and the data is properly clustered by ORDER_DATE.

A) Increase the virtual warehouse size. This will provide more memory for the query to execute.
B) Rewrite the query to use window functions instead of aggregate functions.
C) Optimize the query to leverage data pruning based on ORDER DATE by ensuring the query filters on a specific or limited range of ORDER DATE values.
D) Reduce the number of columns selected in the query, only selecting those that are essential for the aggregation.
E) Increase the value of the parameter. This allows the warehouse to scale up further if needed.

3. You are ingesting data from an external stage (AWS S3) into a Snowflake table using Snowpipe. Data files are continuously being uploaded to the stage. After several hours, you notice that some data files are not being loaded. You check the Snowpipe error notifications and see 'net.snowflake.ingest.errors.FileSizeLimitExceededError'. You have already verified that the Snowpipe is correctly configured and the user has the necessary permissions. What are the MOST LIKELY reasons for this error and how can you resolve them?

A) The Snowpipe configuration is incorrect; specifically, the 'FILE FORMAT parameter is not correctly specified to handle the file type. Reconfigure the Snowpipe with the correct 'FILE FORMAT.
B) The Snowpipe is encountering a transient network error. Reset the pipe using ALTER PIPE REFRESH;
C) The size of the data files in the stage exceeds the maximum allowed size for Snowpipe. Split the large files into smaller files before uploading to the stage.
D) The data files are being uploaded to the stage faster than Snowpipe can process them. Increase the value of the 'MAX CONCURRENCY parameter in the Snowpipe definition.
E) Snowflake has reached its maximum allowable data storage capacity. Increase your Snowflake storage capacity to resolve this issue.

4. A data engineering team is responsible for an ELT pipeline that loads data into Snowflake. The pipeline has two distinct stages: a high- volume, low-complexity transformation stage using SQL on raw data, and a low-volume, high-complexity transformation stage using Python UDFs that leverages an external service for data enrichment. The team is experiencing significant queueing during peak hours, particularly impacting the high-volume stage. You need to optimize warehouse configuration to minimize queueing. Which combination of actions would be MOST effective?

A) Create two separate warehouses: a Medium warehouse for the high-volume, low-complexity transformations and an X-Small warehouse for the low-volume, high-complexity transformations.
B) Create two separate warehouses: a Large, multi-cluster warehouse configured for auto-scale for the high-volume, low-complexity transformations and a Small warehouse for the low-volume, high-complexity transformations.
C) Create a single, X-Small warehouse and rely on Snowflake's query acceleration service to handle the workload.
D) Create two separate warehouses: a Small warehouse configured for auto-suspend after 5 minutes for the high-volume, low-complexity transformations and a Large warehouse configured for auto-suspend after 60 minutes for the low-volume, high-complexity transformations.
E) Create a single, large (e.g., X-Large) warehouse and rely on Snowflake's automatic scaling to handle the workload.

5. You are designing a data product for the Snowflake Marketplace that provides daily weather forecasts. You need to ensure that consumers of your data receive the latest forecast data every morning automatically with minimal latency. Which of the following strategies offers the MOST efficient and cost-effective solution for updating the shared data?

A) Share the raw data files stored in an external stage with the consumers. Consumers will then need to create their own pipelines to process and load the data.
B) Create a scheduled task that executes a full refresh of the shared tables every morning at 6 AM. This task uses CREATE OR REPLACE TABLE AS SELECT to rebuild the tables with the latest forecast data.
C) Create a stored procedure that truncates and reloads the shared tables with the latest forecast data from a staging table. Schedule this stored procedure to run every morning at 6 AM using a Snowflake task.
D) Implement a continuous data pipeline using Snowflake Streams and Tasks to incrementally update the shared tables as new forecast data becomes available. The stream tracks changes and tasks apply those changes to the shared tables.
E) Manually upload a new CSV file containing the latest forecast data to a Snowflake stage and then load it into the shared tables every morning at 6 AM.

Solutions: