DBS-C01 Workload-Specific Database Design Practice Test 11 — 15 Questions

Question 1

A healthcare application stores patient records in Amazon DynamoDB. Each record has a unique patient ID and contains sensitive health information. The application must encrypt data at rest and ensure that only authorized services can access the data. Which combination of design choices meets these requirements?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Enable DynamoDB encryption at rest using AWS KMS and use IAM policies to restrict access.. DynamoDB encryption at rest using AWS KMS provides server-side encryption for sensitive patient data, while IAM policies allow fine-grained access control to ensure only authorized services can access the table. This combination directly meets both the encryption and access control requirements without unnecessary complexity or service mismatches.

Answer

Implement client-side encryption and use Lambda to validate access.

Answer

Enable S3 server-side encryption with AWS KMS and use bucket policies.

Answer

Use AWS CloudHSM for key storage and VPC endpoints for access control.

Question 2

A startup is building a social media application that requires a database to store user relationships (followers, following) and support graph queries. The data volume is expected to grow to tens of terabytes. Which AWS database service is most suitable for this workload?

Accepted Answer

Amazon Neptune.. Amazon Neptune is a fully managed graph database service optimized for storing and querying highly connected data, such as social media user relationships (followers, following). It supports both property graph and RDF models, enabling efficient graph traversal queries using Gremlin or SPARQL, which is ideal for this workload. Neptune scales to tens of terabytes and provides low-latency query performance for complex graph patterns, making it the most suitable choice.

Answer

Amazon RDS for MySQL with self-joins.

Answer

Amazon Redshift.

Answer

Amazon DynamoDB with adjacency list design.

Question 3

A company uses Amazon RDS for PostgreSQL to run a reporting application. The reporting queries are complex and take several minutes to complete, causing performance impact on the primary instance. The company wants to isolate the reporting workload without data staleness. Which solution should they implement?

Accepted Answer

Create a read replica and direct reporting queries to it.. Creating a read replica of the Amazon RDS for PostgreSQL primary instance allows you to offload complex reporting queries to the replica without impacting the primary. Read replicas use asynchronous replication (based on PostgreSQL streaming replication), which provides near-real-time data with minimal staleness, satisfying the requirement to isolate the reporting workload without significant data staleness.

Answer

Implement an Amazon ElastiCache cluster to cache query results.

Answer

Enable Multi-AZ and use the standby instance for reporting.

Answer

Take a manual snapshot of the database and restore it for reporting.

Question 4

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.. 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 5

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

Use Amazon Aurora instead of RDS MySQL.. Amazon Aurora is a MySQL-compatible database engine that provides built-in high availability and automatic failover across multiple Availability Zones, with up to 15 low-latency read replicas. Option C is correct because enabling Multi-AZ deployment for Amazon RDS MySQL provisions a synchronous standby replica in a different Availability Zone, and Amazon RDS automatically fails over to the standby in the event of a primary instance failure. Both strategies ensure automatic failover without manual intervention. Options B and D involve read replicas, which are for read scaling and require manual promotion for failover, not automatic. Option E (automatic backups) provides point-in-time recovery, not high availability or failover.

Answer

Create a read replica in the same Availability Zone.

Answer

Provision read replicas in different Availability Zones.

Answer

Enable automatic backups.

Question 6

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.. 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 7

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.. 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 8

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.. 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 9

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 10

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 11

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 12

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. 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 13

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 14

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 15

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. 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