Free Data-Engineer-Associate Questions Attempt

For near real-time processing of new files in S3, event-driven ingestion is optimal. S3 Event Notifications can trigger AWS Lambda to immediately load data into Amazon Redshift, eliminating latency associated with batch scheduling.

“Event-based triggers using S3 notifications and Lambda functions are effective for near real-time ingestion pipelines into Amazon Redshift.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Option C (Glue bookmarks) is best for batch jobs, and zero-ETL applies to Aurora to Redshift, not S3.

Option A is correct. The MERGE INTO statement is used to conditionally update, insert, or delete rows based on a match condition between a target table and a source table. In this statement, the target table is accounts and the source table is monthly_accounts_update. The join condition is t.customer = s.customer. Because the statement uses WHEN MATCHED THEN DELETE, every row in accounts that has a matching customer value in monthly_accounts_update will be deleted from the target table.

AWS documentation for MERGE INTO states that it conditionally updates, deletes, or inserts rows into an Apache Iceberg table, and the syntax explicitly includes WHEN MATCHED THEN DELETE. AWS Glue guidance and examples for Iceberg also show MERGE INTO as a supported pattern for row-level changes in Glue ETL workflows. This means the syntax is valid for supported Glue-Iceberg use cases, so option D is incorrect. Options B and C are also incorrect because the statement does not retain only matching rows and does not delete the entire table. It deletes only the rows in the target table that satisfy the match condition.

The most cost-effective solution in this case is to apply a lifecycle policy to transition records to Amazon S3 Standard-IA storage after 30 days. Here’s why:

Amazon S3 Lifecycle Policies: Amazon S3 offers lifecycle policies that allow you to automatically transition objects between different storage classes to optimize costs. For data that is frequently accessed in the first 30 days and infrequently accessed after that, transitioning from the S3 Standard storage class to S3 Standard-Infrequent Access (S3 Standard-IA) after 30 days makes the most sense. S3 Standard-IA is designed for data that is accessed less frequently but still needs to be retained, offering lower storage costs than S3 Standard with a retrieval cost for access.

Cost Optimization: S3 Standard-IA offers a lower price per GB than S3 Standard. Since the data will be accessed infrequently after 30 days, using S3 Standard-IA will lower storage costs while still allowing for immediate retrieval when necessary.

Compliance with Regulations: Since the records need to be immediately accessible for the first 30 days, the use of S3 Standard for that period ensures compliance with regulatory requirements. After 30 days, transitioning to S3 Standard-IA continues to meet access requirements for infrequent access while reducing storage costs.

Alternatives Considered:

Option B (S3 Intelligent-Tiering): While S3 Intelligent-Tiering automatically moves data between access tiers based on access patterns, it incurs a small monthly monitoring and automation charge per object. It could be a viable option, but transitioning data to S3 Standard-IA directly would be more cost-effective since the pattern of access is well-known (frequent for 30 days, infrequent thereafter).

Option C (S3 Glacier Deep Archive): Glacier Deep Archive is the lowest-cost storage class, but it is not suitable in this case because the data needs to be accessed immediately within 30 days and on an infrequent basis thereafter. Glacier Deep Archive requires hours for data retrieval, which is not acceptable for infrequent access needs.

Option D (S3 Standard-IA for all records): Using S3 Standard-IA for all records would result in higher costs for the first 30 days, as the data is frequently accessed. S3 Standard-IA incurs retrieval charges, making it less suitable for frequently accessed data.

Amazon S3 Lifecycle Policies

S3 Storage Classes

Cost Management and Data Optimization Using Lifecycle Policies

AWS Data Engineering Documentation

To create a new table named cities_usa in Amazon Athena based on a subset of data from the existing cities_world table, you should use an INSERT INTO statement combined with a SELECT statement to filter only the records where the country is ' usa ' . The correct SQL syntax would be:

Option A: INSERT INTO cities_usa (city, state) SELECT city, state FROM cities_world WHERE country= ' usa ' ;This statement inserts only the cities and states where the country column has a value of ' usa ' from the cities_world table into the cities_usa table. This is a correct approach to create a new table with data filtered from an existing table in Athena.

Options B, C, and D are incorrect due to syntax errors or incorrect SQL usage (e.g., the MOVE command or the use of UPDATE in a non-relevant context).