DBS-C01 Workload-Specific Database Design Practice Test 10 — 15 Questions

Question 1

A company is designing a disaster recovery plan for an Amazon DynamoDB table that stores critical session data. The table is provisioned with on-demand capacity. The recovery objective is to have the data available in another AWS Region within 15 minutes of a regional outage. Which design should they choose?

Accepted Answer

Use DynamoDB Global Tables to replicate data across Regions.. Option C is correct because DynamoDB Global Tables provide multi-Region, fully replicated tables with automatic conflict resolution, enabling active-active replication that meets the 15-minute recovery objective without manual intervention. Global Tables replicate data across Regions in sub-second latency, ensuring data availability within the required RTO during a regional outage.

Answer

Use DynamoDB on-demand backups and restore to another Region.

Answer

Use DynamoDB Streams to replicate data to a table in another Region via AWS Lambda.

Answer

Create cross-Region Read Replicas for DynamoDB.

Question 2

A company is designing an Amazon RDS for MySQL database for an e-commerce application. Which TWO design strategies will help ensure high availability and automatic failover in the event of a primary instance failure?

Accepted Answer

Enable Multi-AZ deployment.. Option C is correct because enabling Multi-AZ deployment for Amazon RDS MySQL automatically provisions and maintains a synchronous standby replica in a different Availability Zone. In the event of a primary instance failure, Amazon RDS automatically fails over to the standby replica, providing high availability with minimal downtime.

Answer

Use Amazon Aurora instead of RDS MySQL.

Answer

Create a read replica in the same Availability Zone.

Answer

Provision read replicas in different Availability Zones.

Answer

Enable automatic backups.

Question 3

A company is using Amazon DynamoDB to store IoT sensor data. The application writes a large volume of data and needs to read recent data by timestamp. The table has a partition key of device_id and a sort key of timestamp. The access pattern is to read the latest data for a specific device. Which TWO design patterns will optimize read performance and reduce costs?

Accepted Answer

Use adaptive capacity to evenly distribute traffic across partitions.. Option A is correct because adaptive capacity allows DynamoDB to automatically manage partition traffic distribution, preventing hot partitions when a single device_id receives a high volume of writes. This ensures consistent read performance without manual partition management. Option B is correct because DAX provides an in-memory cache for the most frequently accessed data, reducing read latency and read capacity unit consumption for repeated queries of recent sensor data.

Answer

Enable auto scaling for the table to handle spikes.

Answer

Use DynamoDB Transactions for consistent reads.

Answer

Create a global secondary index (GSI) with device_id as partition key and timestamp as sort key.

Question 4

A company is designing a document database on Amazon DocumentDB. The workload requires high write throughput and needs to support complex queries on nested attributes. Which THREE design considerations should the company evaluate to meet these requirements?

Accepted Answer

Denormalize data to reduce the number of joins.. Option A is correct because denormalizing data in Amazon DocumentDB reduces the need for joins, which are expensive and can degrade write throughput. By embedding related data into a single document, the database can perform complex queries on nested attributes more efficiently, as DocumentDB is optimized for document-level operations. This design aligns with the workload's requirement for high write throughput and complex query support.

Answer

Enable Multi-AZ deployment for high availability.

Answer

Use change streams to capture and process data changes.

Question 5

A company is running an Oracle database on Amazon RDS with the configuration shown in the exhibit. The application is experiencing high latency for write operations. The storage is consistently showing high queue depth and write latency. Which change will most improve write performance?

Accepted Answer

Migrate to io1 or io2 storage with higher provisioned IOPS.. Option D is correct because migrating to io1 or io2 block storage with higher provisioned IOPS directly addresses the root cause of high queue depth and write latency. Unlike gp2, which has a baseline IOPS of 3 per GB (up to 16,000 IOPS at 5,334 GB) and a burst bucket that depletes under sustained load, io1/io2 provide consistent, provisioned IOPS independent of volume size. This ensures the storage subsystem can keep up with the write workload, reducing queue depth and latency.

Answer

Increase allocated storage to 1,000 GB to get higher gp2 baseline IOPS.

Answer

Enable storage auto scaling and increase storage throughput to 500 MB/s.

Answer

Change the DB instance class to db.r5.2xlarge.

Question 6

A company uses the IAM policy shown in the exhibit to control access to a DynamoDB table. The table has a partition key user_id and a sort key timestamp. The application uses the AWS SDK to query items. When a user tries to query items with a filter condition, they receive an AccessDeniedException. What is the most likely cause?

Accepted Answer

The query does not specify a partition key that matches the user's LeadingKeys condition.. The IAM policy uses a `Condition` block with `ForAllValues:StringEquals` on `dynamodb:LeadingKeys` to restrict access to items where the partition key (`user_id`) matches the caller's IAM user ID (`${aws:userid}`). When a query does not specify a partition key that satisfies this condition, the request fails with an `AccessDeniedException`. The error occurs because the query must include a partition key equal to the user's ID to pass the leading keys restriction.

Answer

The aws:userid variable is not being resolved correctly.

Answer

The policy is missing a Condition element with dynamodb:Select.

Answer

The policy does not allow the Query action.

Question 7

A company's application is logging the error shown in the exhibit. The application is deployed on Amazon EC2 and connects to an Amazon RDS for MySQL Multi-AZ DB instance. Which configuration change is most likely to resolve this issue?

Accepted Answer

Increase the DB instance class to handle more concurrent connections.. The error log indicates that the application is hitting the maximum number of connections allowed by the RDS DB instance. Increasing the DB instance class (Option D) provides more memory and CPU resources, which allows the instance to support a higher `max_connections` value (calculated as `DBInstanceClassMemory / 12582880` for MySQL). This directly resolves the connection limit issue without changing the application's connection pool behavior or architecture.

Answer

Add an additional standby instance in a third Availability Zone.

Answer

Increase the connection pool timeout in the application configuration.

Answer

Create a read replica and direct write traffic to it.

Question 8

A company is designing a new application that requires a relational database with sub-millisecond read latency for a global user base. The workload is read-heavy with occasional writes. Which database solution should they choose?

Accepted Answer

Amazon Aurora with Auto Scaling. Amazon Aurora with Auto Scaling is the correct choice because it provides a relational database (MySQL/PostgreSQL-compatible) with sub-millisecond read latency via its distributed storage layer and read replicas. The read-heavy workload benefits from Aurora's automatic scaling of read capacity, while occasional writes are efficiently handled by the cluster volume. Aurora's architecture decouples compute and storage, enabling fast failover and consistent performance for global users.

Answer

Amazon DynamoDB with DAX

Answer

Amazon RDS for MySQL with Multi-AZ

Answer

Amazon ElastiCache for Redis

Question 9

A social media application stores user posts in an Amazon RDS for PostgreSQL instance. The application experiences a sudden spike in read traffic during peak hours, causing database bottlenecks. The team needs to improve read scalability without changing the application code. Which solution is MOST cost-effective?

Accepted Answer

Use Amazon RDS for PostgreSQL Read Replicas. Option C is correct because Amazon RDS for PostgreSQL Read Replicas allow you to offload read traffic from the primary DB instance to one or more read-only replicas, improving read scalability without any application code changes. This is the most cost-effective solution as it leverages the existing PostgreSQL engine and requires only minimal additional compute and storage costs for the replicas.

Answer

Migrate to Amazon DynamoDB with DAX

Answer

Enable Multi-AZ on the RDS instance

Answer

Use Amazon ElastiCache to cache query results

Question 10

A company runs a time-series application that records sensor data every second. The data volume is 500 GB per month and grows continuously. They need to query the last 30 days of data frequently and older data rarely. Which database design is MOST appropriate?

Accepted Answer

Amazon Timestream. Amazon Timestream is purpose-built for time-series data, automatically storing recent data in memory for fast queries and moving older data to a cost-optimized store. This matches the workload of frequent queries on the last 30 days and rare queries on older data, with continuous growth at 500 GB/month.

Answer

Amazon RDS for PostgreSQL with partitioning

Answer

Amazon DynamoDB with TTL

Answer

Amazon S3 with Athena and partitioning

Question 11

A financial services company uses Amazon Aurora MySQL-Compatible Edition for transaction processing. They need to run complex analytical queries on the same data without impacting transactional performance. Which solution meets these requirements?

Accepted Answer

Use Aurora Zero-ETL integration with Amazon Redshift. Aurora Zero-ETL integration with Amazon Redshift allows you to run complex analytical queries on transactional data without impacting Aurora's performance. It eliminates the need for extract, transform, and load (ETL) pipelines by automatically replicating data from Aurora to Redshift in near real-time, ensuring that analytical workloads are offloaded to a separate, optimized analytics engine.

Answer

Enable Performance Insights and use RDS Proxy

Answer

Export data to Amazon S3 and query with Athena

Answer

Create an Aurora Replica and run analytical queries against it

Question 12

An e-commerce application uses Amazon DynamoDB as its primary database. The table stores order data with a partition key of 'OrderID' and a sort key of 'OrderDate'. The application frequently queries orders by customer ID (which is not a key attribute). What design change would improve query performance?

Accepted Answer

Create a Global Secondary Index on CustomerID. Option C is correct because creating a Global Secondary Index (GSI) on CustomerID allows efficient querying by that attribute without scanning the entire table. A GSI has its own partition and sort keys, enabling fast lookups on non-key attributes. This directly addresses the performance issue of frequent queries by CustomerID, which otherwise would require a full table scan.

Answer

Enable DynamoDB Streams and export to Amazon Elasticsearch Service

Answer

Use DynamoDB Accelerator (DAX) to cache queries

Answer

Create a Local Secondary Index on CustomerID

Question 13

A startup needs a cost-effective database for a small application that handles both transactional and analytical workloads. They expect low traffic initially but want the database to automatically scale as the business grows. Which database solution is BEST suited?

Accepted Answer

Amazon Aurora Serverless v2. Amazon Aurora Serverless v2 is the best fit because it automatically scales compute and memory capacity in fine-grained increments (down to 1 ACU) based on actual workload demand, supporting both transactional (OLTP) and analytical (OLAP) queries via the MySQL/PostgreSQL-compatible Aurora engine. It offers a pay-per-ACU model that is cost-effective for low-traffic startups while providing near-instant scaling to handle growth without manual intervention.

Answer

Amazon DynamoDB with on-demand capacity

Answer

Amazon RDS for MySQL with a Single-AZ deployment

Answer

Amazon Redshift Serverless

Question 14

A company runs a document management system using Amazon DocumentDB (with MongoDB compatibility). The application stores large documents (up to 5 MB each) and frequently fetches them by document ID. The team notices increased latency during peak hours. They need to reduce read latency. Which action is MOST effective?

Accepted Answer

Add read replicas to the cluster. Adding read replicas to the DocumentDB cluster is the most effective action because it offloads read traffic from the primary instance, directly reducing read latency during peak hours. DocumentDB supports up to 15 read replicas that are kept in sync via the cluster's replication mechanism, and the application's frequent fetches by document ID are read-heavy operations that benefit from distributing the load across multiple replicas.

Answer

Shard the collection across multiple DocumentDB clusters

Answer

Implement Amazon ElastiCache for Redis in front of DocumentDB

Answer

Increase the instance class of the primary instance

Question 15

A mobile gaming application uses Amazon DynamoDB to store player profiles and game state. The write throughput is high during events, but low otherwise. The company wants to minimize costs while maintaining performance. Which capacity mode should they use?

Accepted Answer

On-Demand capacity mode. On-Demand capacity mode is ideal for this workload because it automatically scales to handle high write throughput during events and scales down to zero when idle, eliminating the need for capacity planning. This minimizes costs by charging only for actual reads and writes, without requiring any provisioning or management of throughput limits.

Answer

Reserved capacity

Answer

Provisioned capacity without auto scaling

Answer

Provisioned capacity with auto scaling