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

1. A data engineering team is tasked with optimizing a complex query that joins three tables: 'ORDERS' , 'CUSTOMERS' , and 'PRODUCTS. The 'ORDERS' table contains millions of records and is frequently joined with 'CUSTOMERS' (containing customer demographics) and 'PRODUCTS' (containing product details). The initial query uses standard JOIN syntax, but performance is slow. The query retrieves order details along with customer and product information, filtering by a specific date range in the 'ORDERS' table and a customer segment in the 'CUSTOMERS table. Which optimization strategy would be MOST effective for significantly improving query performance?

A) Convert the entire dataset into a single VARIANT column and query using JSON path expressions.
B) Replace the standard JOINs with LATERAL FLATTEN operations.
C) Create materialized views that pre-join the 'ORDERS', 'CUSTOMERS, and 'PRODUCTS tables and filter based on common criteria.
D) Increase the virtual warehouse size to X-LARGE without analyzing the query profile.
E) Apply clustering keys to the 'ORDERS table based on the date column used in the WHERE clause and clustering keys to the 'CUSTOMERS' table on the customer segment column. Also create appropriate indexes.

2. A critical database, 'PRODUCTION DB', in your Snowflake account was accidentally dropped. You need to restore it as quickly as possible, but you're unsure if Time Travel retention is sufficient. Which method guarantees restoration of the database even if it falls outside the Time Travel window?

A) Restore from a Snowflake-managed backup using the 'CREATE DATABASE ... FROM BACKUP' command. Specify the timestamp before the drop occurred.
B) Use the 'UNDROP DATABASE PRODUCTION command.
C) Contact Snowflake Support and request restoration from Fail-safe.
D) Fail-safe cannot be directly accessed by the user for restoration purposes; it is only used by Snowflake Support in extreme disaster recovery scenarios.
E) Utilize the data cloning feature: 'CREATE DATABASE CLONE PRODUCTION_DB BEFORE (STATEMENT 'DROP DATABASE PRODUCTION_DB');'

3. You are tasked with ingesting a large volume of CSV files from an external stage into a Snowflake table. Some of these CSV files contain corrupted records with inconsistent delimiters or missing values. You need to ensure that only valid records are loaded into the table, and the corrupted records are captured for further analysis. Which of the following COPY INTO options would BEST address this requirement?

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

4. You are building a data pipeline that extracts data from a REST API, transforms it using Pandas DataFrames, and loads it into Snowflake. You need to implement error handling to gracefully handle network issues and API rate limits. Which of the following code snippets demonstrates the most robust approach to handle potential errors during data loading into Snowflake using the Python connector?

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

5. A data engineering team is building a real-time data pipeline in Snowflake. Data arrives continuously and needs to be processed with minimal latency. The team is using Snowflake Streams and Tasks for incremental data processing. However, they are encountering issues where the tasks are sometimes skipped or delayed, leading to data inconsistencies. Which combination of actions would BEST address these issues and ensure reliable near real-time data processing?

A) Increase the warehouse size to ensure sufficient compute resources. This will prevent tasks from being skipped due to resource contention.
B) Adjust the ' ERROR_INTEGRATION' parameter on the task definition to send notifications when tasks fail. This allows for manual intervention but does not prevent skipping.
C) Monitor the 'TASK HISTORY view regularly to identify skipped or delayed tasks and manually re-run them as needed. This is a reactive approach and does not prevent future occurrences.
D) Configure the tasks to run using a serverless compute model (Snowflake-managed compute). Ensure the parameter is set to a higher value and implement error handling within the task using TRY/CATCH blocks.
E) Disable task scheduling and rely solely on Snowflake's Auto-Resume feature for warehouses. This simplifies the pipeline and reduces the chance of errors.

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