AWS Data Quality and Monitoring for Engineers

1. How does AWS Lake Formation improve data quality governance?

By compressing data automatically

By providing centralized metadata management and access controls

By deleting duplicate records only

By limiting data storage

2. How can AWS Step Functions enhance data quality monitoring workflows?

By orchestrating complex quality checks across multiple services

By storing data only

By encrypting pipelines

By limiting access

3. Which metric measures how often data is updated relative to business requirements?

Accuracy

Timeliness

Validity

Completeness

4. What is the primary purpose of implementing data reconciliation checks?

To verify data consistency between source and target systems

To increase data volume

To reduce query latency

To eliminate backups

5. How does Amazon Athena support data quality validation?

By running SQL queries to detect anomalies

By encrypting data only

By limiting query access

By archiving old data

6. Which approach best handles missing or null values in data quality checks?

Ignore them completely

Define acceptable null thresholds and handle appropriately

Delete all rows with nulls

Convert to random values

7. What is a key benefit of implementing data quality frameworks early in pipeline development?

Reduces debugging time and prevents data issues downstream

Increases data storage

Slows down processing

Eliminates all errors

8. How can you monitor data pipeline performance using CloudWatch metrics?

By setting up custom metrics and dashboards

By manually reviewing logs

By using only default metrics

By disabling monitoring

9. Which AWS service is used to catalog and track metadata for data quality purposes?

AWS Glue Data Catalog

Amazon Kinesis

AWS CodePipeline

Amazon QuickSight

10. What is the benefit of implementing data quality rules before data enters the data warehouse?

Prevents poor-quality data from being loaded

Increases query speed

Reduces storage requirements

Simplifies data backup

11. Which AWS service is best suited for continuous data quality monitoring across distributed data sources?

AWS Glue Data Catalog

Amazon CloudWatch

AWS Deequ

AWS Lambda

12. Which metric indicates whether data values conform to expected formats or ranges?

Uniqueness

Validity

Timeliness

Accessibility

13. What does data lineage tracking help achieve in a data pipeline?

Faster data compression

Understanding data origin and transformations

Reducing storage costs

Automatic data deletion

14. How can you implement automated data quality checks in AWS Glue jobs?

Using Glue Triggers only

Using Deequ with Glue for rule-based validation

Manual SQL queries

External Python scripts only

15. Which AWS service provides real-time monitoring and alerting for data pipeline failures?

Amazon S3

AWS CloudTrail

Amazon EventBridge

AWS Backup

16. What is data freshness in the context of quality monitoring?

The encryption level of data

How recently data was updated

The storage format used

The number of data copies

17. Which approach best detects outliers and anomalies in streaming data?

Batch processing only

Statistical baselines and threshold monitoring

Manual review

Data archival

18. How does AWS Glue handle schema validation during ETL operations?

Manual inspection only

Automatic schema inference and validation

No schema checking

Third-party tools only

19. Which metric measures the completeness of data in a dataset?

Freshness