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| Section | Weight | Objectives |
|---|---|---|
| Data Transformation & Processing | 30% | - Implement data quality, validation, and schema evolution - Use streams, tasks, dynamic tables for pipelines and CDC - Design and implement transformations: SQL, stored procedures, user-defined functions - Work with semi-structured and unstructured data |
| Performance Optimization & Scalability | 15% | - Optimize warehouses, clustering, partitioning, and query performance - Design scalable compute and storage solutions - Monitor and tune workloads and resource usage |
| Data Ingestion | 25% | - Use COPY INTO, external tables, Snowpipe, Snowpipe Streaming - Handle file formats, compression, encoding, and schema detection - Ingest from various sources: cloud storage, on-premises, APIs, data lakes |
| Data Governance, Security & Sharing | 10% | - Share data securely across accounts and platforms - Apply access control, masking, encryption, and compliance - Track lineage, audit, and maintain data integrity |
| Data Pipeline Orchestration & Automation | 20% | - Error handling, retry logic, and dependency management - Build, schedule, monitor, and manage workflows - Integrate with external orchestration tools |
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295. Frage
A data engineer is building a new data pipeline that stores metadata in an Amazon DynamoDB table. The data engineer must ensure that all items that are older than a specified age are removed from the DynamoDB table daily.
Which solution will meet this requirement with the LEAST configuration effort?
Antwort: B
Begründung:
DynamoDB Time to Live (TTL) is a fully managed feature that automatically expires and deletes items based on a timestamp attribute you define. By enabling TTL and setting the TTL attribute in the application code, items older than the specified age are removed without the need for custom scheduling or Lambda functions, offering the least configuration effort.
296. Frage
A retail company wants to implement real-time analytics for an ecommerce platform. The company needs to collect clickstream data from the company's website and mobile apps. The company needs to store the data for analytics. Which solution will meet these requirements with the LEAST ongoing maintenance?
Antwort: D
Begründung:
Amazon Kinesis Data Streams is well suited to collect high-volume real-time clickstream data from websites and mobile apps, and Amazon Data Firehose can then deliver that streaming data to Amazon S3 with very little operational effort. This approach is highly managed and avoids the ongoing administration required for EC2-based collectors, self-managed Kafka patterns, or maintaining a provisioned Redshift cluster for raw landing storage.
Reference:
https://docs.aws.amazon.com/firehose/latest/dev/what-is-this-service.html
https://docs.aws.amazon.com/firehose/latest/dev/basic-deliver.html
297. Frage
A data engineer needs to debug an AWS Glue job that reads from Amazon S3 and writes to Amazon Redshift. The data engineer enabled the bookmark feature for the AWS Glue job.
The data engineer has set the maximum concurrency for the AWS Glue job to 1.
The AWS Glue job is successfully writing the output to Amazon Redshift. However, the Amazon S3 files that were loaded during previous runs of the AWS Glue job are being reprocessed by subsequent runs.
What is the likely reason the AWS Glue job is reprocessing the files?
Antwort: D
Begründung:
https://docs.aws.amazon.com/glue/latest/dg/glue-troubleshooting-errors.html#error-job- bookmarks-reprocess-data
298. Frage
You as Data engineer might want to consider disabling auto-suspend for a warehouse if?
Antwort: A,D
Begründung:
Explanation
Automating Warehouse Suspension
Data Engineer might want to consider disabling auto-suspend for a warehouse if:
He/She have a heavy, steady workload for the warehouse.
He/She require the warehouse to be available with no delay or lag time. Warehouse provisioning is generally very fast (e.g. 1 or 2 seconds); however, depending on the size of the warehouse and the availability of compute resources to provision, it can take longer.
If he/she chose to disable auto-suspend, He/she must carefully consider the costs associated with running a warehouse continually, even when the warehouse is not processing queries. The costs can be significant, especially for larger warehouses (X-Large, 2X-Large, etc.).
To disable auto-suspend, Engineer must explicitly select Never in the web interface, or specify 0 or NULL in SQL.
299. Frage
A company uses Amazon Athena to run SQL queries for extract, transform, and load (ETL) tasks by using Create Table As Select (CTAS). The company must use Apache Spark instead of SQL to generate analytics.
Which solution will give the company the ability to use Spark to access Athena?
Antwort: B
Begründung:
https://docs.aws.amazon.com/athena/latest/ug/notebooks-spark.html
300. Frage
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