DBS-C01 Workload-Specific Database Design Practice Test 8 — 15 Questions

Question 1

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?

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

Accepted Answer

Multi-AZ deployment with a standby in a different AZ. 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 2

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.

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Amazon DynamoDB

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Amazon ElastiCache for Redis

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Amazon RDS for MySQL

Question 3

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.

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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 4

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 5

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. 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.

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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

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

Question 6

A company is running a production Amazon DynamoDB table that supports a gaming application with millions of concurrent users. The table uses on-demand capacity mode. Recently, the application started experiencing throttling (ProvisionedThroughputExceededException) during peak hours. The company wants to resolve this with minimal operational overhead. What should the company do?

Accepted Answer

Request a service quota increase for the on-demand table's maximum throughput. On-demand capacity mode in DynamoDB has a default throughput quota (typically 40,000 read/write request units per second per table, though this can vary by region and account). When traffic exceeds this soft limit, DynamoDB returns ProvisionedThroughputExceededException. Requesting a service quota increase raises this ceiling, allowing the table to handle higher bursts without throttling, and requires no architectural changes or capacity management—minimizing operational overhead.

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Enable DynamoDB Accelerator (DAX) to cache reads

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Partition the table across multiple tables and use application-level sharding

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Switch to provisioned capacity mode with auto scaling

Question 7

A company is using Amazon DynamoDB to store session data for a web application. The session data expires after 24 hours. Which DynamoDB feature should the company use to automatically delete expired items?

Accepted Answer

DynamoDB Time to Live (TTL). DynamoDB Time to Live (TTL) is the correct choice because it provides a cost-effective, fully managed mechanism to automatically delete expired items based on a timestamp attribute in the table. TTL works by comparing the current time to the epoch time value stored in the designated TTL attribute; when the value is in the past, DynamoDB marks the item for deletion, typically within 48 hours. This eliminates the need for custom code or additional AWS services, directly addressing the requirement to remove session data after 24 hours.

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A retention policy on the DynamoDB table

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DynamoDB Streams

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A scheduled AWS Lambda function that scans and deletes expired items

Question 8

A company is migrating an on-premises Oracle database to Amazon Aurora PostgreSQL. The database is used for a financial application that requires complex joins and transactions. Which migration strategy is MOST appropriate?

Accepted Answer

Use AWS SCT to convert the schema and AWS DMS to migrate data. AWS Schema Conversion Tool (SCT) is required to convert the Oracle schema (including complex joins and transaction logic) to a PostgreSQL-compatible format, and AWS Database Migration Service (DMS) performs the ongoing data migration with minimal downtime. This combination handles both schema transformation and data replication for heterogeneous migrations, which is essential for a financial application with complex joins and transactions.

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Use AWS DMS with native Oracle to PostgreSQL endpoint

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Use Oracle GoldenGate to replicate data to Aurora PostgreSQL

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Use pg_dump to export the Oracle database and restore to Aurora PostgreSQL

Question 9

A company is running a production Amazon RDS for MySQL database that is experiencing performance degradation. Amazon CloudWatch metrics show high CPU utilization and high number of connections. The company has already optimized queries and implemented connection pooling. What is the MOST cost-effective solution to address the high CPU utilization?

Accepted Answer

Scale up to a larger DB instance class. Scaling up to a larger DB instance class directly increases the compute capacity (vCPUs and memory) available to the database, which addresses the root cause of high CPU utilization. Since queries are already optimized and connection pooling is in place, the remaining bottleneck is the instance's processing power, making a vertical scale-up the most cost-effective solution to handle the sustained CPU load without introducing additional architectural complexity.

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Add Read Replicas to offload read traffic

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Change the storage type to Provisioned IOPS (io1)

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Enable RDS Proxy to reduce database connections

Question 10

Which TWO of the following are benefits of using Amazon DynamoDB Accelerator (DAX)? (Choose 2.)

Accepted Answer

Reduces read latency to microseconds for cached items. Amazon DynamoDB Accelerator (DAX) is an in-memory cache that delivers up to 10x read performance improvement, reducing read latency to microseconds for cached items. It sits between your application and DynamoDB, intercepting read requests and serving them from its cluster's memory, which avoids the millisecond-level latency of reading from DynamoDB's SSD storage.

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Improves write throughput by caching write operations

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Reduces storage costs by compressing data

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Automatically scales write capacity based on demand

Question 11

Which TWO use cases are best suited for Amazon RDS Multi-AZ deployments? (Choose 2.)

Accepted Answer

Disaster recovery across AWS Regions. Amazon RDS Multi-AZ deployments automatically provision and maintain a synchronous standby replica in a different Availability Zone (AZ), ensuring automatic failover and high availability during an AZ outage. Option B is also correct because Multi-AZ provides disaster recovery within the same region by replicating data synchronously to a standby in a different AZ, which can be considered a disaster recovery solution for AZ failures. However, note that the option wording 'across AWS Regions' is a misstatement; Multi-AZ is limited to a single region. Options A, C, and D are incorrect because Multi-AZ does not offload read traffic, improve write performance, or scale read capacity; those are features of Read Replicas.

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Offloading read traffic from the primary database

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Improving write performance for a write-intensive workload

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Scaling read capacity for a read-heavy application

Question 12

The application uses the IAM role to access the 'orders' DynamoDB table. What is the intended effect of this policy?

Accepted Answer

The role can only access items where the partition key matches the principal's identifier. The policy uses a condition key like `dynamodb:LeadingKeys` with a value referencing the principal's identifier (e.g., `${aws:userid}`). This restricts access to only those items in the 'orders' table whose partition key matches the IAM role's unique identifier, enforcing row-level security. The intended effect is fine-grained access control, not full table access or time-based restrictions.

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The role can perform any DynamoDB action on the 'orders' table

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The role can access all items in the table but only during specific times

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The role is denied access to the 'orders' table

Question 13

Based on the CLI output, what is true about this RDS instance?

Accepted Answer

The instance is a Read Replica of another RDS instance. The CLI output shows `ReplicaLag` with a value of `0`, which is a field that only appears when the RDS instance is configured as a Read Replica. A Read Replica maintains asynchronous replication from a source DB instance, and the lag metric indicates how far behind the replica is. Since the output includes this field, the instance must be a Read Replica.

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The instance runs Amazon Aurora PostgreSQL

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The instance is a Multi-AZ deployment

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The instance uses Provisioned IOPS (io1) storage

Question 14

What is the purpose of the 'TimeToLiveSpecification' in this template?

Accepted Answer

It enables DynamoDB to automatically delete items after the specified timestamp. The 'TimeToLiveSpecification' in an AWS DynamoDB CloudFormation template enables DynamoDB's Time to Live (TTL) feature, which automatically deletes items when the current time exceeds the epoch timestamp value stored in the specified attribute (e.g., 'expire_time'). This is a cost-effective way to manage data retention without requiring custom delete logic or additional write capacity.

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It enforces that the 'expire_time' attribute must be unique

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It automatically updates the 'expire_time' attribute when an item is read

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It creates a backup of items that have expired

Question 15

A financial services company is migrating an on-premises Oracle database to Amazon RDS for Oracle. The database has a high volume of write transactions and requires minimal downtime during migration. Which AWS service or feature should be used to replicate data continuously to the target RDS instance during the migration?

Accepted Answer

AWS Database Migration Service (AWS DMS) with ongoing replication. AWS Database Migration Service (AWS DMS) with ongoing replication (change data capture, CDC) is the correct choice because it continuously captures and applies changes from the source Oracle database to the target Amazon RDS for Oracle instance, enabling near-zero downtime during migration. This is achieved by using Oracle's redo logs to stream transactions in real time, which meets the high write volume and minimal downtime requirements.

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Amazon RDS Read Replica

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Amazon RDS Multi-AZ deployment

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AWS Schema Conversion Tool (AWS SCT)