Data Store Management

Choosing optimal data stores, understanding data cataloging systems, managing data lifecycles, and designing data models with schema evolution on AWS.

5 minutes 5 Questions

Data Store Management is a critical domain in the AWS Certified Data Engineer - Associate certification that focuses on designing, implementing, and maintaining various data storage solutions on AWS. It encompasses selecting appropriate data stores based on requirements such as performance, cost, s…

Concepts covered
Data Versioning and TTL ManagementAmazon Redshift Architecture and Access PatternsAmazon RDS and Relational Database SelectionOpen Table Formats with Apache IcebergData Migration with AWS Transfer FamilyAWS Glue Data Catalog and CrawlersS3 Lifecycle Policies and Storage TieringSchema Conversion and Evolution with AWS SCT and DMSChoosing Storage Services for Cost and PerformanceAmazon DynamoDB for NoSQL Data StorageData Lakes with Lake Formation and Amazon S3Vector Databases and Indexing (HNSW, IVF)Federated Queries and Materialized Views in RedshiftSchema Discovery and Partition SynchronizationData Models and Schema Design for Redshift and DynamoDBData Lineage and Optimization Techniques
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AWS DEA-C01 - Data Store Management Example Questions

Test your knowledge of Data Store Management

Question 1

A team has an AWS Glue ETL pipeline with three jobs. They want to understand where their data comes from and where it goes after processing. What does data lineage help them see?

Correct Answer: The flow and transformations of data from its sources through each processing step to its final destinations

Data lineage tracks the flow and transformations of data from its original sources through each processing step to its final destinations, providing a complete picture of how data moves and changes across a pipeline.

The billing and cost-focused answer confuses data lineage with cost management. The security and access permissions answer confuses data lineage with access control and governance policies. The scheduling and timestamps answer confuses data lineage with orchestration and job scheduling metadata.

The key concept tested is understanding data lineage as a mechanism for tracking data origin, movement, and transformation across an ETL pipeline.

Question 2

A team is new to DynamoDB and needs to store user profile data. A colleague mentions that DynamoDB uses a specific data model. What type of database is Amazon DynamoDB?

Correct Answer: A fully managed key-value and document NoSQL database service

Amazon DynamoDB is a fully managed key-value and document NoSQL database service that stores data in flexible, schema-less items identified by primary keys, making it ideal for use cases like user profiles.

A relational database with SQL support describes services like Amazon RDS or Aurora. A graph-based NoSQL database describes Amazon Neptune, which is designed for highly connected datasets. A columnar analytical database for data warehousing describes Amazon Redshift.

The key concept tested is understanding DynamoDB's fundamental data model as a key-value and document store within the broader landscape of AWS database services.

Question 3

A team adds new CSV files to an S3 bucket organized by date partitions (year/month/day). They notice that their AWS Glue Data Catalog table does not reflect the newly added partitions. They want the simplest way to detect and register these new partitions. What should they do?

Correct Answer: Run an AWS Glue crawler on the S3 path to discover and add the new partitions to the Data Catalog

An AWS Glue crawler is specifically designed to scan S3 paths, detect new partitions, and automatically register them in the AWS Glue Data Catalog, making it the simplest solution for this use case.

A Glue ETL job is meant for data transformation, not partition discovery. Creating a new Athena table definition does not automatically detect partitions and is unnecessarily complex compared to a crawler (though MSCK REPAIR TABLE could work, that is not the option presented). A Glue classifier determines the format and schema of data files, not partition detection or registration.

Key concept tested: AWS Glue crawler functionality for automatic partition discovery and Data Catalog updates.

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