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

1. You are building a data pipeline that involves ingesting data from AWS S3 into Snowflake using Snowpipe. The data arrives in small files frequently, and you are experiencing performance issues with delayed data availability. You need to optimize the pipeline for near real-time ingestion. Which combination of strategies will MOST effectively address this scenario?

A) Enable auto-ingest on the Snowpipe and increase the frequency of S3 event notifications to Snowflake. Combine this with clustering the target table on a relevant column to optimize query performance after loading.
B) Manually refresh the Snowpipe using the 'ALTER PIPE ... REFRESH' command every few minutes to force ingestion of new data.
C) Increase the warehouse size used for the Snowpipe load process and adjust the 'MAX FILE SIZE' parameter on the pipe definition to match the size of the incoming files.
D) Configure S3 event notifications to trigger Snowpipe only when a sufficient number of files have arrived in the S3 bucket, using a serverless function (like AWS Lambda) to manage the file count threshold.
E) Implement a micro-batching process using a third-party tool (like Apache Spark) to aggregate the small files into larger batches before loading them into S3, then configure Snowpipe to ingest the larger files.

2. You have a table named 'EMPLOYEES with a retention period of 1 day. You accidentally deleted several important rows from this table, but you need to recover the data'. You know the deletion occurred 25 hours ago. What actions should be taken to attempt to recover the deleted data, and what outcome can you expect? Assume you are working in an Enterprise edition of Snowflake account.

A) Attempt to clone the table using Time Travel to a point in time before the deletion, then extract the deleted rows. Expect the recovery to be successful as long as the deletion occurred within the data retention period.
B) Since its Enterprise edition of Snowflake account, the Time travel and cloning will work with 7 days retention period, hence attemtp clone table using Time Travel and recover data successfully
C) Attempt to use UNDROP TABLE command if the table was dropped. Expect the recovery to be successful as long as the deletion occurred within the data retention period.
D) Attempt to use Time Travel or cloning to recover the data. Expect the recovery to fail because the deletion occurred outside the I-day data retention period.
E) Attempt to use Time Travel to query the table before the deletion and re-insert the deleted rows. Expect the recovery to be successful as long as the deletion occurred within the data retention period.

3. You are developing a JavaScript stored procedure in Snowflake using Snowpark to perform a complex data transformation. This transformation involves multiple steps: filtering, joining with another table, and aggregating data'. You need to ensure that the stored procedure is resilient to failures and can be easily debugged. Which of the following practices would contribute to the robustness and debuggability of your stored procedure? (Select all that apply)

A) Directly manipulating the Snowflake metadata (e.g., table schemas) within the stored procedure for dynamic schema evolution.
B) Relying solely on try-catch blocks within the stored procedure to handle all potential exceptions.
C) Using Snowpark's logging capabilities to record intermediate results and error messages at various stages of the transformation.
D) Breaking down the complex transformation into smaller, modular functions within the stored procedure and testing each function independently.
E) Passing the 'snowflake' binding as an argument to each modular function to facilitate logging and SQL execution within those functions.

4. You have created an external table in Snowflake that points to a large dataset stored in Azure Blob Storage. The data consists of JSON files, and you've noticed that query performance is slow. Analyzing the query profile, you see that Snowflake is scanning a large number of unnecessary files. Which of the following strategies could you implement to significantly improve query performance against this external table?

A) Create an internal stage, copy all JSON Files, create and load the target table, and drop external table
B) Increase the size of the Snowflake virtual warehouse to provide more processing power.
C) Partition the data in Azure Blob Storage based on a relevant column (e.g., date) and define partitioning metadata in the external table definition using PARTITION BY.
D) Convert the JSON files to Parquet format and recreate the external table to point to the Parquet files.
E) Create a materialized view on top of the external table to pre-aggregate the data.

5. A Snowflake table 'ORDERS' is clustered on the 'ORDER DATE column. After several months, you notice that many micro-partitions contain data from a wide range of 'ORDER DATE values, and query performance on date range filters is degrading. Which of the following actions could improve performance and reduce the overlap in micro-partitions?

A) Run 'OPTIMIZE TABLE ORDERS' to recluster the table.
B) Change the ORDER DATE column to VARCHAR and then cluster.
C) Increase the virtual warehouse size used for loading data into the "ORDERS' table.
D) Change the clustering key to a composite key including ORDER DATE and another relevant column.
E) Drop and recreate the table with a different clustering key.

Solutions: