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

1. You are tasked with designing a solution to ingest a continuous stream of unstructured log data from various sources into Snowflake. The log data includes text, JSON, and XML formats. The goal is to efficiently store the data, allow for flexible querying, and minimize storage costs. Which of the following approaches would BEST address these requirements? (Select TWO)

A) Ingest all data 'as is' into a raw staging table. Then create a Task that use Python UDF to parse data and save it to different tables as required
B) Create separate tables for each log data format (text, JSON, XML).
C) Pre-process the log data to convert all formats into a standardized JSON format before ingestion.
D) Ingest all log data into a single VARIANT column in a Snowflake table.
E) Use Snowflake's external functions to parse the log data during query execution.

2. A global e-commerce company, 'GlobalMart', uses Snowflake for its data warehousing needs. They operate primarily in the US (us-east-1) and Europe (eu-west-l). They're implementing cross-region replication for disaster recovery and business continuity. Their requirements are: 1) All data from the US region needs to be replicated to the EU region. 2) The failover to the EU region should have minimal downtime. 3) Replication should be automatic and continuous. Considering these requirements, which of the following Snowflake features and configurations would be the MOST suitable and efficient?

A) Enable database replication using replication groups, configure a primary database in us-east-I , and a secondary database in eu-west-l. Set the replication schedule with 'ALTER REPLICATION GROUP ADD .
B) Create a database replica in the EU region and manually refresh it periodically using 'CREATE DATABASE AS CLONE'
C) Manually unload data from the US region and load it into the EU region using SnowSQL. Automate this process using a scheduled task.
D) Export data from the US region to cloud storage (e.g., AWS S3 or Azure Blob Storage) and then load it into the EU region using Snowpipe.
E) Use Snowflake's Data Sharing feature to share data from the US region with an account in the EU region. This automatically replicates the data.

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

4. You are building a data pipeline in Snowflake that uses an external function to perform sentiment analysis on customer reviews stored in a table named 'CUSTOMER REVIEWS'. The external function 'sentiment_analyzer' is hosted on AWS Lambda and requires an API key for authentication. You want to ensure that the API key is securely passed to the Lambda function and prevent unauthorized access. Which of the following approaches represents the MOST secure and recommended method to manage the API key?

A) Create a Snowflake secret object to store the API key and reference it in the external function definition using the 'USING' clause and 'SYSTEM$GET SECRET function.
B) Store the API key in a Snowflake table with restricted access and retrieve it within the external function's logic.
C) Pass the API key as a parameter to the external function each time it is called.
D) Store the API key directly in the external function definition as a string literal within the 'AS' clause.
E) Embed the API key directly into the AWS Lambda function's environment variables, avoiding any transmission from Snowflake.

5. Consider a scenario where you're optimizing a data pipeline in Snowflake responsible for aggregating sales data from multiple regions. You've identified that the frequent full refreshes of the target aggregated table are causing significant performance overhead and resource consumption. Which strategies could be employed to optimize these full refreshes without sacrificing data accuracy?

A) Schedule the full refreshes during off-peak hours when the Snowflake warehouse is less utilized. This minimizes the impact on other workloads but does not reduce the actual processing time.
B) Leverage Snowflake's search optimization service on the base tables. While costly, this will dramatically speed up full table scans performed in the aggregation.
C) Implement incremental data loading using streams and tasks. This allows you to only process and load the changes that have occurred since the last refresh, reducing the amount of data that needs to be processed.
D) Utilize Snowflake's Time Travel feature to clone the previous version of the aggregated table, apply the necessary changes to the clone, and then swap the clone with the original table using 'ALTER TABLE SWAP WITH'. Note that this will impact data availability during the swap operation.
E) Replace the full refresh with a 'TRUNCATE TABLE' followed by an 'INSERT statement. This approach is faster than 'CREATE OR REPLACE TABLE' and reduces locking.

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