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

1. You are using Snowpipe to ingest data from Azure Blob Storage into a Snowflake table. You have successfully set up the pipe and configured the event notifications. However, you notice that duplicate records are appearing in your target table. After reviewing the logs, you determine that the same file is being processed multiple times by Snowpipe. Which of the following strategies can you implement to prevent duplicate data ingestion, assuming you cannot modify the source data in Azure Blob Storage to include a unique ID or timestamp?

A) Use a data masking policy with the 'MASK' function to obfuscate duplicate records based on their similarity, making them effectively invisible to downstream queries.
B) Create a Snowflake stream on the target table and use it to incrementally load data into a separate, deduplicated table using a merge statement with conditional logic to insert or update records based on a combination of columns.
C) Modify the Azure Event Grid subscription configuration to filter events based on file size or creation time to avoid resending events for already processed files.
D) Configure the Snowpipe definition with the 'PURGE = TRUE parameter. This will ensure that each file is only processed once.
E) Implement idempotent logic within a Snowflake stored procedure that is triggered by a task after the data is loaded by Snowpipe. The stored procedure should identify and remove duplicate rows based on all other columns in the table.

2. A data engineering team is building a real-time fraud detection system. They have a large 'TRANSACTIONS table that grows rapidly. They need to calculate the average transaction amount per merchant daily. The following query is used:

This query is run every hour and is performance-critical. Which of the following materialized view definitions would provide the BEST performance improvement, considering the need for near real-time data and minimal latency?

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

3. A Snowflake table 'CUSTOMER ORDERS is clustered by 'ORDER DATE. You have observed the clustering depth increasing over time, impacting query performance. To improve performance, you decide to recluster the table. However, you need to minimize the impact on concurrent DML operations and cost. Which of the following strategies would be MOST effective in managing this reclustering process?

A) Create a new table clustered by 'ORDER_DATE, copy data in parallel, and then swap tables.
B) Recluster the entire table in a single transaction during off-peak hours.
C) Implement a continuous reclustering process using Snowpipe to automatically recluster new data as it arrives.
D) Leverage Snowflake's automatic reclustering feature, monitor its performance, and adjust warehouse size as needed.
E) Use 'CREATE OR REPLACE TABLE with SELECT FROM CUSTOMER ORDERS to rebuild the table with optimized clustering.

4. You need to create a UDF in Snowflake to perform complex data validation. This UDF must access an external API to retrieve validation rules based on the input data'. You want to ensure that sensitive API keys are not exposed within the UDF's code and that the external API call is made securely. Which of the following approaches is the MOST secure and appropriate for this scenario?

A) Use a Snowflake Secret to securely store the API key. Retrieve the secret within the UDF using the 'SYSTEM$GET_SECRET function, and use 'SECURITY INVOKER with caution or define the UDF as 'SECURITY DEFINER with appropriate role based access controls .
B) Store the API key in a Snowflake table with strict access controls, and retrieve it within the UDF using a SELECT statement. Use 'SECURITY INVOKER to ensure the UDF uses the caller's privileges when accessing the table.
C) Pass the API key as an argument to the UDF when it is called. Rely on the caller to provide the correct key and keep it secure.
D) Hardcode the API key directly into the UDF's JavaScript code, obfuscating it with base64 encoding.
E) Store the API key as an environment variable within the UDF's JavaScript code. Snowflake automatically encrypts environment variables for security.

5. You are using Snowpipe to load data from an AWS S3 bucket into Snowflake. The data files are compressed using GZIP and are being delivered frequently. You have observed that the pipe's backlog is increasing and data latency is becoming unacceptable. Which of the following actions could you take to improve Snowpipe's performance? (Select all that apply)

A) Reduce the number of columns in the target Snowflake table. Fewer columns reduce the overhead of data loading.
B) Increase the virtual warehouse size associated with the pipe.
C) Ensure that the S3 event notifications are correctly configured and that there are no errors in the event delivery mechanism.
D) Optimize the file size of the data files in S3. Smaller files are processed faster by Snowpipe.
E) Check if the target table has any active clustering keys defined which could be causing slow down

Solutions: