DBS-C01 Workload-Specific Database Design Practice Test 6 — 15 Questions

Question 1

A startup is building a mobile application that requires a database to store user preferences and session data. The data is accessed by user ID and requires single-digit millisecond latency. The workload is read-heavy with occasional writes. Which database service is MOST cost-effective?

Accepted Answer

Amazon DynamoDB with on-demand capacity. Amazon DynamoDB with on-demand capacity is the most cost-effective choice because it provides single-digit millisecond latency for key-value lookups by user ID, scales automatically to handle read-heavy workloads with occasional writes, and charges only for the reads and writes consumed, avoiding the cost of provisioning for peak capacity. The on-demand mode eliminates the need for capacity planning, making it ideal for unpredictable or variable traffic patterns typical of a startup's mobile application.

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Amazon Aurora Serverless

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Amazon ElastiCache for Memcached

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Amazon RDS for MySQL with provisioned IOPS

Question 2

A company uses Amazon RDS for Oracle for an OLTP application. The database experiences high CPU utilization during peak hours. The application is read-heavy and can tolerate eventually consistent reads. Which solution reduces CPU load on the primary database with minimal application changes?

Accepted Answer

Create an RDS read replica and direct read traffic to it. Option C is correct because creating an RDS Read Replica offloads read traffic from the primary Oracle instance, directly reducing CPU utilization on the primary. Since the application is read-heavy and tolerates eventually consistent reads, the replica’s asynchronous replication lag is acceptable. This solution requires minimal application changes—only modifying the connection string to route SELECT queries to the replica endpoint.

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Implement Amazon ElastiCache to cache frequent queries

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Upgrade to a larger instance type

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Use DynamoDB Accelerator (DAX) as a cache layer

Question 3

A company is designing a database for an IoT application that ingests millions of sensor readings per second. Each reading includes device ID, timestamp, and measurement. The workload requires time-series analytics and data retention for 90 days. Which AWS database solution is MOST appropriate?

Accepted Answer

Amazon Timestream. Amazon Timestream is a purpose-built time-series database designed for IoT and operational applications that ingest millions of sensor readings per second. It automatically manages data retention policies (e.g., 90 days) by storing recent data in memory and historical data in a cost-optimized store, and it supports time-series analytics with built-in functions like interpolation and smoothing.

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Amazon Redshift with auto-copy from S3

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Amazon ElastiCache for Redis with time-series module

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Amazon DynamoDB with TTL

Question 4

A company needs a fully managed graph database for a social networking application that requires real-time recommendations based on friend connections. Which AWS service should they use?

Accepted Answer

Amazon Neptune. Amazon Neptune is the correct choice because it is a fully managed graph database service optimized for storing and querying highly connected data. It supports both property graph (Apache TinkerPop Gremlin) and RDF (SPARQL) models, making it ideal for social networking applications that require real-time friend-of-friend recommendations and traversal queries across complex relationships.

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Amazon DocumentDB

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Amazon ElastiCache

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Amazon DynamoDB

Question 5

A company is designing a disaster recovery strategy for an Amazon RDS for PostgreSQL database. They need a Recovery Point Objective (RPO) of less than 5 seconds and a Recovery Time Objective (RTO) of less than 1 minute. Which TWO actions should they take? (Choose two.)

Accepted Answer

Create a cross-region read replica and configure automatic failover using Amazon Route 53 health checks. Option D is correct because a cross-region read replica can be promoted to a primary instance in under a minute, and with Amazon Route 53 health checks configured for automatic failover, the DNS update occurs automatically, meeting the RTO of less than 1 minute. The asynchronous replication lag is typically sub-second, achieving an RPO of less than 5 seconds. Option E is correct because a Multi-AZ deployment with a synchronous standby in another Availability Zone provides automatic failover with no data loss (RPO of 0) and failover completes in about 30-60 seconds, satisfying both RPO and RTO requirements.

Answer

Use AWS Database Migration Service for continuous replication to a separate instance

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Create a cross-region read replica and manually update DNS in a disaster

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Take hourly snapshots and restore in another region

Question 6

A company is migrating a large Oracle data warehouse to AWS. The warehouse contains 50 TB of data and runs complex analytical queries. The solution must support concurrency of up to 100 users and provide high performance for queries. Which THREE design decisions should the company make? (Choose three.)

Accepted Answer

Use distribution keys based on frequently joined columns. Distribution keys based on frequently joined columns ensure that related data is co-located on the same compute nodes, minimizing data movement across the network during joins. This is critical for complex analytical queries on large datasets in Amazon Redshift, as it reduces shuffle overhead and improves query performance.

Answer

Use Amazon RDS for Oracle with Multi-AZ

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Use Amazon DynamoDB with global tables

Question 7

A company is building a microservices architecture and needs a database for a service that stores JSON documents with variable schema. The database must support high availability and automatic scaling. Which TWO services meet these requirements? (Choose two.)

Accepted Answer

Amazon DynamoDB. Amazon DynamoDB is correct because it is a fully managed NoSQL key-value and document database that natively supports JSON documents with variable schema, offers high availability through multi-AZ replication, and provides automatic scaling via its on-demand capacity mode or auto-scaling policies. It is ideal for microservices architectures that require low-latency, serverless, and elastic throughput.

Answer

Amazon ElastiCache for Redis

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Amazon Neptune

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Amazon RDS for MySQL

Question 8

Refer to the exhibit. An IAM policy is attached to an IAM role used by a Lambda function that writes to a DynamoDB table. The function also needs to read items from the table. What is the outcome of this policy?

Accepted Answer

The Lambda function can write items but cannot read items. The IAM policy includes an explicit Deny for the `dynamodb:GetItem` action on the table, which overrides any Allow statements due to the explicit deny evaluation logic in AWS IAM. Since the Lambda function assumes a role with this policy, it is denied read access while the Allow for `dynamodb:PutItem` permits write operations. Therefore, the function can write but cannot read items from the DynamoDB table.

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The policy is invalid because it contains both Allow and Deny for the same table

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The Lambda function can both read and write items

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The Lambda function cannot perform any operations on the table

Question 9

Refer to the exhibit. A developer reports that the RDS MySQL instance 'mydb' is experiencing high write latency. The storage is gp2 with 100 GB. What is the MOST likely cause of the write latency?

Accepted Answer

The gp2 volume size is too small, resulting in insufficient baseline IOPS. The gp2 volume's baseline IOPS are determined by the volume size at a ratio of 3 IOPS per GB, up to 16,000 IOPS. With a 100 GB gp2 volume, the baseline IOPS is only 300 (100 × 3). This is insufficient for write-heavy workloads, causing write latency as the volume exhausts its IOPS credit balance and enters a throttled state. Burst credits can temporarily boost performance, but sustained high write throughput will deplete credits and lead to latency.

Answer

There is a read replica causing replication lag

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The instance class db.r5.large does not provide enough memory

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Multi-AZ is not enabled, causing synchronous replication overhead

Question 10

Refer to the exhibit. A developer created a DynamoDB table 'UserSessions' with a simple primary key. The application needs to query by user_id as well. What design change should the developer make to support this query efficiently?

Accepted Answer

Create a Global Secondary Index on user_id. Option D is correct because a Global Secondary Index (GSI) on user_id allows efficient querying by user_id without altering the base table's primary key structure. The base table uses a simple primary key (likely session_id), and a GSI provides a separate index with its own partition key (user_id) to support non-key attribute queries with eventual consistency, enabling the application to query by user_id efficiently without scanning the entire table.

Answer

Use a Scan operation with a filter

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Add a sort key to the table

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Create a Local Secondary Index on user_id

Question 11

A company is migrating an on-premises MySQL database to Amazon RDS. The database is used for a critical e-commerce application that requires high availability with automatic failover. Which RDS deployment option should the company choose to meet these requirements?

Accepted Answer

Multi-AZ deployment with a standby in a different AZ. Option C is correct because a Multi-AZ deployment for Amazon RDS MySQL automatically provisions and maintains a synchronous standby replica in a different Availability Zone (AZ). In the event of an AZ failure or primary instance failure, Amazon RDS automatically fails over to the standby, providing high availability with minimal downtime. This meets the requirement for automatic failover without manual intervention.

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Multi-Region deployment with Read Replicas

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Single-AZ instance with a standby in the same AZ

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Single-AZ instance with a Read Replica

Question 12

A company is designing a database for a social media application that requires low-latency access to user profiles and support for complex graph queries. 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. It supports both property graph (Apache TinkerPop/Gremlin) and RDF (SPARQL) models, making it the ideal choice for social media applications that require low-latency traversal of complex relationships, such as friend-of-friend recommendations or influence paths.

Answer

Amazon DynamoDB

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Amazon ElastiCache for Redis

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Amazon RDS for MySQL

Question 13

A company is running a production Amazon RDS for PostgreSQL database. The database experiences high write latency during peak hours. The company wants to reduce latency without changing the application code. Which solution is MOST cost-effective and scalable?

Accepted Answer

Increase the instance size to a larger DB instance class. Increasing the instance size to a larger DB instance class directly addresses high write latency by providing more CPU and memory resources, which improves the database's ability to process write operations faster. This is the most cost-effective and scalable solution because it does not require application code changes and can be scaled vertically as needed, whereas other options either do not reduce write latency or introduce unnecessary complexity.

Answer

Change the storage type to Provisioned IOPS (io1)

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Enable RDS Proxy to pool database connections

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Add a Multi-AZ standby to offload writes

Question 14

A company is designing a database for an IoT application that ingests millions of time-series data points per second. The database must support high-throughput writes and efficient querying of recent data. Which AWS database service is MOST suitable?

Accepted Answer

Amazon Timestream. Amazon Timestream is purpose-built for time-series data, offering a serverless architecture that ingests millions of data points per second with automatic scaling. It provides efficient storage and querying of recent data through its memory store, while tiering older data to a cost-optimized magnetic store, making it the most suitable choice for high-throughput IoT time-series workloads.

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Amazon RDS for PostgreSQL

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Amazon DynamoDB with TTL

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Amazon Redshift

Question 15

A company is building a mobile application that requires users to be able to query their order history quickly. The data is stored in Amazon DynamoDB, and each user has up to 10,000 orders over time. The application needs to support pagination and filtering by order date. What is the MOST efficient way to model this data in DynamoDB?

Accepted Answer

Use user ID as the partition key and order date as the sort key. Option D is correct because using user ID as the partition key ensures all orders for a user are co-located on a single partition, enabling efficient queries. Adding order date as the sort key allows the application to filter and paginate by date range using the Query API with KeyConditionExpression, which is far more efficient than scanning or using a secondary index.

Answer

Scan the entire table and filter on user ID

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Store all orders as a JSON document in a single item per user

Answer

Use user ID as the partition key and a Global Secondary Index on order date