DBS-C01 Workload-Specific Database Design Practice Test 3 — 15 Questions

Question 1

Match each RDS storage type to its description.

Question 2

A company runs a customer-facing application on Amazon RDS for MySQL. The application experiences frequent read replicas lagging behind the primary due to long-running analytics queries. The analytics team runs complex SELECT queries that scan large tables. Which design change would minimize replica lag without affecting production writes?

Accepted Answer

Create a cross-Region read replica for analytics queries.. Creating a cross-Region read replica for analytics queries offloads the long-running SELECT statements to a separate read replica in a different AWS Region, isolating the analytics workload from the primary instance and its in-Region replicas. This prevents the analytics queries from competing for I/O and CPU resources on the primary or its local replicas, thereby minimizing replica lag without affecting production writes. Cross-Region replicas use asynchronous replication, so they can handle heavy read traffic without impacting the primary's write performance.

Answer

Use Amazon DynamoDB Accelerator (DAX) for caching.

Answer

Increase the instance size of the primary and all read replicas.

Answer

Enable Multi-AZ on the primary instance.

Question 3

A startup is building a real-time leaderboard for a mobile game using Amazon DynamoDB. The leaderboard must update frequently and support global access with low latency. Which database design approach is most suitable?

Accepted Answer

Use Amazon DynamoDB global tables with appropriate partition key design.. Amazon DynamoDB global tables provide multi-Region, fully managed, multi-master replication, which is ideal for a real-time leaderboard requiring frequent updates and low-latency global access. By designing an appropriate partition key (e.g., game ID or time-based composite key), you can distribute write traffic evenly and avoid hot partitions, ensuring consistent performance under high update frequency.

Answer

Use Amazon ElastiCache for Redis with replication across Regions.

Answer

Use Amazon Aurora Global Database with a single writer and multiple readers.

Answer

Use Amazon S3 with event notifications to update a leaderboard file.

Question 4

A company needs to store and query time-series data from IoT sensors. The data is written continuously and queried by time range for dashboards. Which AWS database service is most cost-effective and scalable for this workload?

Accepted Answer

Amazon Timestream.. Amazon Timestream is purpose-built for time-series data, offering automatic tiering between in-memory and magnetic stores for cost efficiency, and built-in functions for time-based aggregations and windowed queries. It is serverless and scales automatically to handle continuous writes from IoT sensors and low-latency dashboard queries by time range, making it the most cost-effective and scalable choice.

Answer

Amazon ElastiCache for Redis with time-series data structures.

Answer

Amazon DynamoDB with time-based partition keys.

Answer

Amazon RDS for PostgreSQL with time-based indexing.

Question 5

A company uses Amazon DynamoDB for a shopping cart application. During a flash sale, write requests are throttled due to hot partitions. The access pattern is evenly distributed across items, but the partition key is the customer ID. Which design change would best mitigate throttling?

Accepted Answer

Enable DynamoDB adaptive capacity.. Option A is correct because DynamoDB adaptive capacity automatically adjusts throughput capacity based on traffic patterns, which helps mitigate hot partitions by redistributing unused capacity from less-accessed partitions to heavily accessed ones. This is ideal for the flash sale scenario where write requests are throttled due to uneven access across customer ID partitions, even though the overall access pattern is evenly distributed.

Answer

Change the partition key to a single value for all items.

Answer

Increase the provisioned write capacity to a higher fixed value.

Answer

Add a DAX cluster in front of DynamoDB.

Question 6

A company is migrating an on-premises MongoDB database to AWS. Which AWS database service is most compatible and requires minimal application changes?

Accepted Answer

Amazon DocumentDB (with MongoDB compatibility).. Amazon DocumentDB (with MongoDB compatibility) is the most compatible AWS database service for migrating an on-premises MongoDB database because it is purpose-built to emulate the MongoDB wire protocol and data model, allowing existing MongoDB drivers and tools to connect with minimal or no application code changes. This makes it the ideal choice for a lift-and-shift migration that preserves the document-oriented structure and query patterns of MongoDB.

Answer

Amazon DynamoDB.

Answer

Amazon Neptune.

Answer

Amazon RDS for MySQL.

Question 7

A company needs to implement a database solution for a global e-commerce platform that requires strongly consistent reads and writes with automatic failover across AWS Regions. Which service should be used?

Accepted Answer

Amazon Aurora Global Database.. Amazon Aurora Global Database is the correct choice because it provides strongly consistent reads and writes across multiple AWS Regions with automatic failover. It uses a primary cluster in one Region and up to five secondary read-only clusters in other Regions, with replication typically under one second. Failover to a secondary Region can be promoted in as little as one minute, meeting the requirements for a global e-commerce platform.

Answer

Amazon DynamoDB global tables.

Answer

Amazon RDS for MySQL with Multi-AZ and cross-Region read replicas.

Answer

Amazon ElastiCache for Redis with Global Datastore.

Question 8

A company runs a critical OLTP workload on Amazon RDS for PostgreSQL. The database size is 2 TB and growing. To reduce storage costs, the company wants to archive old data that is rarely accessed. Which approach is most cost-effective and minimally impacts performance?

Accepted Answer

Implement table partitioning and use S3 as an external table for old partitions.. Option C is correct because it uses PostgreSQL table partitioning (e.g., range partitioning by date) combined with the `postgres_fdw` or `pg_parquet` extension to treat old partitions as foreign tables stored in Amazon S3. This keeps the hot data in RDS for fast OLTP access while offloading cold data to low-cost S3 storage, minimizing performance impact and reducing storage costs without requiring a full migration or schema redesign.

Answer

Move the database to Amazon Aurora PostgreSQL with storage auto-scaling.

Answer

Migrate the entire database to Amazon DynamoDB.

Answer

Delete old rows and run VACUUM FULL to reclaim space.

Question 9

A company runs an application that requires a relational database with high availability across multiple Availability Zones. The database must automatically failover with minimal downtime. Which AWS service meets these requirements?

Accepted Answer

Amazon RDS for MySQL with Multi-AZ deployment.. Amazon RDS for MySQL with Multi-AZ deployment automatically provisions and maintains a synchronous standby replica in a different Availability Zone. If the primary instance fails, Amazon RDS automatically fails over to the standby, typically within 60–120 seconds, providing high availability with minimal downtime. This meets the requirement for a relational database with automatic failover across multiple Availability Zones.

Answer

Amazon DynamoDB with global tables.

Answer

Amazon Redshift with cross-Region snapshots.

Answer

Amazon RDS for MySQL with a single instance.

Question 10

A company uses Amazon DynamoDB for a session management workload. The access pattern is random and requires single-digit millisecond latency. The table has a read capacity of 5000 RCU. During peak hours, read requests occasionally exceed this capacity, causing throttling. Which design change is most appropriate to handle traffic spikes?

Accepted Answer

Enable DynamoDB auto scaling for reads.. Option C is correct because DynamoDB auto scaling allows the table to dynamically adjust its provisioned read capacity (RCU) in response to traffic spikes, preventing throttling while maintaining single-digit millisecond latency. This is the most appropriate design change for a session management workload with random access patterns, as it handles occasional bursts without requiring manual intervention or architectural changes.

Answer

Switch to DynamoDB on-demand mode.

Answer

Add a global secondary index with different partition key.

Answer

Add a DAX cluster to cache read requests.

Question 11

A company is designing a document database on Amazon DocumentDB for a content management system. Which TWO design practices improve query performance and reduce costs?

Accepted Answer

Shard data based on access patterns to distribute load.. Sharding data based on access patterns (Option A) improves query performance by distributing read/write load across multiple shards, reducing contention and latency. In Amazon DocumentDB, sharding is achieved through a cluster with multiple instances, and aligning shard keys with access patterns ensures even data distribution and efficient query routing. This also reduces costs by allowing you to scale horizontally only when needed, avoiding over-provisioning of larger instances.

Answer

Design documents to avoid joins by frequently using $lookup.

Answer

Avoid denormalization to maintain strict normal forms.

Answer

Store all documents in a single collection without indexes to reduce overhead.

Question 12

A company needs to choose a database for a real-time analytics workload that requires sub-second query latency on streaming data. Which TWO AWS services are most suitable?

Accepted Answer

Amazon Redshift with streaming ingestion from Kinesis.. Amazon Timestream is purpose-built for time-series data and provides sub-second query latency on streaming data via its dedicated query engine and automatic tiering between in-memory and magnetic stores. Amazon Redshift with streaming ingestion from Kinesis enables real-time analytics by directly consuming Kinesis data streams into Redshift materialized views, allowing sub-second queries on fresh data without batch loading.

Answer

Amazon Neptune.

Answer

Amazon RDS for PostgreSQL with materialized views.

Answer

Amazon DynamoDB Accelerator (DAX).

Question 13

A company is migrating a large Oracle data warehouse to Amazon Redshift. Which THREE design considerations are important for performance optimization?

Accepted Answer

Choose appropriate distribution styles (KEY, ALL, EVEN).. Option A is correct because distribution styles (KEY, ALL, EVEN) control how data is distributed across compute nodes in Amazon Redshift. Choosing the right distribution style minimizes data movement during joins and aggregations, which is critical for performance in a large data warehouse migration from Oracle. For example, using KEY distribution on a frequently joined column keeps related rows on the same node, reducing network traffic.

Answer

Enable cross-Region replication for data locality.

Answer

Use row-based storage for fact tables.

Question 14

A company is designing a database for a global e-commerce application with millions of users. The workload requires single-digit millisecond read latency, high availability across multiple AWS Regions, and strong consistency. Which database service should the company use?

Accepted Answer

Amazon DynamoDB with global tables. Amazon DynamoDB with global tables is the correct choice because it provides single-digit millisecond read latency at any scale, supports multi-Region active-active replication for high availability, and offers strongly consistent reads (when using the ConsistentRead parameter) across regions via its distributed, multi-leader architecture. This combination uniquely satisfies all three requirements—low latency, global HA, and strong consistency—for a high-traffic e-commerce workload.

Answer

Amazon ElastiCache for Redis

Answer

Amazon Aurora Global Database

Answer

Amazon Neptune

Question 15

A media company is storing large video files (up to 10 GB each) in Amazon S3 and needs to maintain metadata about each file, including title, duration, and upload timestamp. The workload involves frequent writes (1000+ per second) and occasional read queries by title. Which database is best suited for this metadata store?

Accepted Answer

Amazon DynamoDB. Amazon DynamoDB is the best choice because it supports single-digit millisecond latency at any scale, handles over 1000 writes per second with auto-scaling, and can efficiently serve occasional read queries by title using a global secondary index (GSI) on the title attribute. Its fully managed, serverless nature eliminates operational overhead for high-throughput metadata storage.

Answer

Amazon Neptune

Answer

Amazon RDS for MySQL

Answer

Amazon ElastiCache for Memcached