DBS-C01 Workload-Specific Database Design Practice Test 20 — 15 Questions

Question 1

A startup is building a social media application. User profiles, posts, and comments have relationships but the team expects rapid growth and wants to scale horizontally with no single points of failure. They need a database that supports flexible schemas for different content types. Which database service is most appropriate?

Accepted Answer

Amazon DynamoDB. Amazon DynamoDB is a fully managed NoSQL key-value and document database that scales horizontally, is highly available, and supports flexible schemas. RDS is relational with fixed schemas. Redshift is for analytics. Neptune is for graph databases, not general purpose.

Answer

Amazon Neptune

Answer

Amazon Redshift

Answer

Amazon RDS for MySQL

Question 2

A company is running a MongoDB-compatible workload on Amazon DocumentDB. They are experiencing high write latency during peak hours. The current cluster has one instance (db.r5.large) with 100 GB storage. Which change is most likely to improve write performance?

Accepted Answer

Increase the instance size to db.r5.xlarge. DocumentDB writes are handled by the primary instance. Since the instance is r5.large, increasing instance size provides more CPU and memory, improving write throughput. Adding read replicas does not help writes. Scaling storage does not directly improve write latency. Enabling Multi-AZ adds a standby but does not improve write performance.

Answer

Increase storage to 500 GB

Answer

Enable Multi-AZ deployment

Answer

Add a read replica in a different Availability Zone

Question 3

A company uses Amazon RDS for PostgreSQL to store sensor data. Each sensor sends a row every second. The table has grown to 500 GB and queries filtering on a timestamp column are slow even with an index. The team wants to improve query performance while keeping the data online. Which approach should they take?

Accepted Answer

Partition the table by time using PostgreSQL table partitioning. Partitioning the table by time (e.g., by day or month) allows PostgreSQL to prune partitions, reducing the amount of data scanned. This is a common pattern for time-series data. Adding more indexes can slow writes. Read replicas help with read scaling but not query performance on the primary. Changing to Aurora PostgreSQL with parallel query can help but partitioning is a more direct solution.

Answer

Migrate to Amazon Aurora PostgreSQL and enable parallel query

Answer

Add more indexes on the timestamp column

Answer

Create a read replica and direct queries to the replica

Question 4

A mobile gaming company needs a database to store player scores and leaderboards. The data must be updated in real time as players finish games. The database must support high write throughput and provide sub-millisecond read latency for leaderboard queries. Which database is best suited?

Accepted Answer

Amazon DynamoDB. Amazon DynamoDB is a NoSQL database that provides single-digit millisecond latency and can handle high write throughput. It also supports sorted data for leaderboards via Local Secondary Indexes or Global Secondary Indexes. RDS is slower and not designed for sub-millisecond reads. Redshift is for analytics. ElastiCache is a cache, not a primary database.

Answer

Amazon RDS for MySQL with read replicas

Answer

Amazon Redshift

Answer

Amazon ElastiCache for Redis

Question 5

A company uses Amazon RDS for Oracle with a Multi-AZ deployment for a critical OLTP application. During a recent failover test, they noticed that the application experienced a two-minute downtime. The team wants to reduce downtime to under 30 seconds during automatic failovers. What should they do?

Accepted Answer

Migrate to Amazon Aurora with Multi-AZ and use the Aurora auto-failover feature. Amazon RDS Multi-AZ failovers typically take 1-2 minutes. To reduce failover times, use Multi-AZ with two readable standbys in RDS for Oracle (if supported) or migrate to Amazon Aurora, which has faster failover (typically under 30 seconds) and also provides read replicas. Option A does not help. Option B reduces failover detection time but not the failover process itself. Option D is about read replicas, not failover.

Answer

Add a read replica to offload reads

Answer

Reduce the DNS TTL value to 5 seconds

Answer

Enable Automatic Failover in the RDS console

Question 6

A company runs a web application on Amazon EC2 instances behind an Application Load Balancer. The application uses Amazon RDS for MySQL. Recently, the database CPU utilization spikes to 100% during peak hours. The team observes that the spike is caused by a large number of slow queries. They need to identify and resolve the issue with minimal disruption. Which combination of steps should they take? (Choose two.)

Accepted Answer

Enable RDS Performance Insights to identify the slow queries. Option B: Enabling RDS Performance Insights helps identify the slow queries and their resource consumption. Option D: Creating a read replica for reporting queries offloads read traffic from the primary, reducing CPU load. Option A is not a quick identification step. Option C is a long-term solution. Option E is not directly helpful for identifying slow queries.

Answer

Upgrade to a larger instance type

Answer

Increase the DB instance storage to improve I/O

Answer

Implement connection pooling using Amazon RDS Proxy

Question 7

A company is migrating an on-premises MySQL database to Amazon Aurora MySQL. The database is 2 TB and the migration must have minimal downtime. The network bandwidth between the on-premises data center and AWS is 1 Gbps. Which migration approach is most appropriate?

Accepted Answer

Use AWS Database Migration Service (DMS) with ongoing replication to keep the target in sync. AWS DMS can migrate data with minimal downtime. Using DMS with a full load and ongoing replication (change data capture) allows the source to remain active until the cutover. Option B (Aurora S3) is for importing from S3, not for live migration. Option C (RDS snapshot) is for RDS to Aurora, not on-premises. Option D (Snowball) is for large data volumes with limited bandwidth, but 2 TB at 1 Gbps can be transferred in about 5 hours (theoretical), so DMS is feasible and minimizes downtime.

Answer

Use mysqldump to export the data, upload to Amazon S3, and import into Aurora

Answer

Take a snapshot of the on-premises database and restore it to Aurora

Answer

Use AWS Snowball Edge to transfer the data physically to AWS

Question 8

A company needs to store and analyze JSON documents that contain nested fields. The data is used for real-time dashboards and ad-hoc queries. The team wants a fully managed solution that supports both key-value lookups and SQL-like queries. Which AWS service should they use?

Accepted Answer

Amazon DynamoDB. Amazon DynamoDB is correct because it is a fully managed NoSQL key-value and document database that supports single-digit millisecond latency for key-value lookups, and its PartiQL-compatible query language enables SQL-like queries on JSON documents with nested fields. DynamoDB also integrates with AWS services like Lambda and Kinesis for real-time dashboards, and its adaptive capacity handles ad-hoc query patterns without manual scaling.

Answer

Amazon Redshift

Answer

Amazon RDS for PostgreSQL with JSONB

Answer

Amazon DocumentDB

Question 9

A company uses Amazon DynamoDB for a session management store. The application writes and reads session data frequently. The team notices that write requests occasionally fail with ProvisionedThroughputExceededException. They want a cost-effective solution to handle these bursts. What should they do?

Accepted Answer

Enable DynamoDB Auto Scaling for the table. DynamoDB Auto Scaling adjusts throughput capacity automatically based on traffic patterns, handling bursts without manual intervention. Option B (increase table) would waste capacity. Option C (DAX) does not help with write throughput. Option D (SQS) adds complexity.

Answer

Increase the provisioned write capacity to a higher fixed value

Answer

Use DynamoDB Accelerator (DAX) to cache writes

Answer

Implement an Amazon SQS queue to buffer writes

Question 10

A company has a MySQL database that stores user profile data. The database is 1 TB and growing. The team wants to archive inactive user profiles that haven't been accessed in over 1 year. The archived data must be queryable but at a slower performance tier. Which approach is most cost-effective?

Accepted Answer

Export inactive profiles to Amazon S3 and use S3 Intelligent-Tiering for storage. Amazon S3 Intelligent-Tiering automatically moves data between access tiers to optimize costs. Querying S3 data can be done using Amazon Athena or S3 Select. Option A (RDS read replica) keeps all data in RDS, costly. Option B (DMS to S3) is a one-time load; S3 Intelligent-Tiering optimizes storage costs over time. Option D (DynamoDB TTL) deletes data, not archives.

Answer

Create an RDS read replica and delete inactive records from the primary

Answer

Move inactive profiles to Amazon Aurora Serverless and stop the cluster when not in use

Answer

Use Amazon DynamoDB with TTL to expire inactive profiles

Question 11

A company is designing a database for a global application that requires active-active replication across two AWS Regions. The database must support multi-master writes with conflict resolution. Which AWS database service should they use?

Accepted Answer

Amazon DynamoDB Global Tables. Amazon DynamoDB Global Tables provide multi-Region, multi-master replication with conflict resolution based on last writer wins. Aurora Global Database supports replication but is primary-secondary (single master for writes). RDS Multi-AZ is multi-AZ, not multi-Region. Redshift is not designed for this.

Answer

Amazon Aurora Global Database

Answer

Amazon Redshift

Answer

Amazon RDS for MySQL with Multi-AZ

Question 12

A company runs a critical Oracle database on Amazon RDS. The database has a large table that is frequently accessed by multiple applications. The team wants to implement caching to reduce the load on the database. The cached data must be strongly consistent with the database. Which caching strategy should they use?

Accepted Answer

Write-through cache using Amazon ElastiCache. Write-through cache ensures that data is written to the cache and the database simultaneously, providing strong consistency. Lazy loading can lead to stale data. Read-only cache does not help with writes. Eventual consistency is not acceptable.

Answer

Eventual consistency with DynamoDB Accelerator (DAX)

Answer

Read-only cache with Amazon ElastiCache

Answer

Lazy loading with cache-aside pattern

Question 13

A company is building a real-time analytics dashboard for IoT sensor data. The data arrives as JSON and needs to be stored in a way that supports fast ingestion and complex queries. Which database service is best suited?

Accepted Answer

Amazon Timestream. Amazon Timestream is a time-series database optimized for IoT data, with fast ingestion and built-in analytics functions. DynamoDB is not optimized for time-series queries. RDS is not designed for high-velocity time-series data. Redshift is for batch analytics.

Answer

Amazon RDS for PostgreSQL

Answer

Amazon DynamoDB with TTL

Answer

Amazon Redshift

Question 14

A company needs to store and manage user sessions for a web application. The application runs on multiple EC2 instances, and sessions must be accessible from any instance. The team wants a fully managed, highly available, and low-latency solution. Which AWS service should they use?

Accepted Answer

Amazon ElastiCache for Redis. Amazon ElastiCache for Redis is the correct choice because it provides a fully managed, in-memory data store with sub-millisecond latency, making it ideal for storing user session data that must be accessed from any EC2 instance. Redis supports atomic operations and data structures (e.g., TTL-based key expiration) that are well-suited for session management, and its replication and Multi-AZ failover ensure high availability. This meets the requirement for a fully managed, highly available, and low-latency solution without the overhead of managing a database cluster.

Answer

Amazon RDS for MySQL

Answer

Amazon DynamoDB

Answer

Amazon S3

Question 15

A company uses Amazon Aurora MySQL for an OLTP application. The database has a buffer pool cache hit ratio of 99%. However, the application still experiences high latency for some queries. Which of the following is the most likely cause? (Choose two.)

Accepted Answer

The queries are performing full table scans. Option A: Even with a high cache hit ratio, queries that scan large amounts of data (e.g., full table scans) can cause high latency due to I/O and CPU. Option D: Row lock contention can cause queries to wait, increasing latency. Option B: Insufficient write capacity is not a problem with Aurora. Option C: A low cache hit ratio would be an issue, but it's high. Option E: Replica lag affects reads from replicas, not primary.

Answer

The buffer pool cache hit ratio is actually low

Answer

The database instance type is too small for the write workload

Answer

The read replicas are lagging behind the primary