DBS-C01 Workload-Specific Database Design Practice Test 27 — 15 Questions

Question 1

A company is designing a document database for a content management system using Amazon DocumentDB. The application requires high availability and low-latency reads across multiple AWS Regions. Which TWO design choices meet these requirements?

Accepted Answer

Configure read replicas in each region to offload read traffic.. Option A (global cluster) provides multi-region replication with low-latency reads. Option D (read replicas) enhances read capacity. Option B is wrong because one instance per region doesn't provide high availability. Option C is wrong because sharding is not native in DocumentDB. Option E is wrong because local secondary indexes don't help cross-region.

Answer

Use a single instance in each region with Multi-AZ disabled.

Answer

Use local secondary indexes to improve query performance in each region.

Answer

Shard the data across multiple clusters in different regions.

Question 2

A company is migrating an on-premises MongoDB database to Amazon DocumentDB. The current MongoDB workload uses aggregation pipelines with the $lookup stage and geospatial queries. The migration tool reports that some aggregation stages are not supported. Which THREE actions should the company take to address the incompatibilities?

Accepted Answer

Use $geoNear for geospatial queries as it is supported in DocumentDB.. Option A (rewrite $lookup as application-side joins) is correct because DocumentDB does not support $lookup. Option B (use $geoWithin operator) is correct because DocumentDB supports it. Option C (use $geoNear for geospatial) is correct because DocumentDB supports $geoNear. Option D is wrong because $graphLookup is not supported. Option E is wrong because storing pre-joined documents can be an alternative for $lookup but is not a direct action for unsupported stages.

Answer

Replace $lookup with $geoWithin for location-based queries.

Answer

Convert $graphLookup to use recursive queries in DocumentDB.

Question 3

A company is selecting a database for a time-series application that collects sensor data from thousands of devices. The data is written at a high velocity (millions of data points per second). The application needs to query recent data (last hour) with sub-second latency and perform long-term analysis on months of data. Which TWO AWS database services best meet these requirements?

Accepted Answer

Amazon Timestream for both real-time and historical queries.. Option A (Timestream) is built for time-series data with fast queries. Option D (DynamoDB with TTL) can handle high write throughput and automatically expire old data to S3 for analysis. Option B is wrong because Redshift is not designed for sub-second real-time queries. Option C is wrong because ElastiCache is primarily a cache. Option E is wrong because QLDB is for ledger data.

Answer

Amazon ElastiCache for Redis with Time Series module.

Answer

Amazon Redshift for real-time queries and historical analysis.

Answer

Amazon Quantum Ledger Database (QLDB) for immutable time-series records.

Question 4

A company is designing a database for a real-time leaderboard in a mobile game. The leaderboard updates thousands of times per second and must return the top 100 scores with minimal latency. Which AWS database service is most suitable for this workload?

Accepted Answer

Amazon ElastiCache for Redis. Amazon ElastiCache for Redis with sorted sets is optimized for real-time leaderboards because it provides in-memory operations with O(log N) complexity for adding scores and O(log N+m) for retrieving top N items. Option B (DynamoDB) is wrong because it is not designed for sorted range queries with frequent updates at high throughput without secondary indexes and provisioned throughput. Option C (Aurora) is wrong because it is a relational database with higher latency and not optimized for this specific pattern. Option D (Neptune) is wrong because it is a graph database not suited for leaderboard operations.

Answer

Amazon Neptune

Answer

Amazon DynamoDB

Answer

Amazon Aurora

Question 5

A company runs a MySQL-compatible database on Amazon RDS with a 3 TB dataset. They need to run complex analytical queries that involve joins and aggregations on millions of rows. The current RDS instance is a db.r5.8xlarge with 32 vCPUs and 256 GB RAM, but complex queries take over an hour. Which design change would most improve query performance for this workload?

Accepted Answer

Use Amazon Redshift for the analytical workload. Using Amazon Redshift, a columnar data warehouse, would dramatically improve analytical query performance because it is optimized for complex joins and aggregations. Option A (Aurora) is wrong because it is still row-based and not optimized for analytical workloads. Option B (ElastiCache) is wrong because it is an in-memory cache not designed for complex analytical queries. Option D (DynamoDB Accelerator) is wrong because it is a cache for DynamoDB, not for relational databases.

Answer

Migrate to Amazon Aurora with parallel query

Answer

Add an Amazon ElastiCache cluster to cache query results

Answer

Enable DynamoDB Accelerator (DAX) on the RDS instance

Question 6

A company is building a document management system where each document can have multiple tags and users need to query documents by any combination of tags. The number of tags per document is up to 20, and the total number of documents is expected to be 50 million. Which database design is most appropriate for this flexible tag-based querying?

Accepted Answer

Amazon DynamoDB with a global secondary index on the tag attribute. Amazon DynamoDB with a global secondary index on the tag attribute allows efficient querying by tag. To query multiple tags, application-side intersection is needed. Option A (RDS with multiple join tables) is wrong because it introduces complex joins and is less scalable. Option C (ElastiCache) is wrong because it is not a persistent database for primary storage. Option D (Neptune) is wrong because although graph databases can handle tags, they are not the most straightforward for simple tag queries.

Answer

Amazon RDS for MySQL with a normalized schema

Answer

Amazon Neptune

Answer

Amazon ElastiCache for Memcached

Question 7

A financial services company needs to store trade data with strong consistency, high durability, and the ability to run complex SQL queries on the data. The data volume is 10 TB and grows by 1 GB per day. Queries must return results in less than 5 seconds. Which database solution best meets these requirements?

Accepted Answer

Amazon Aurora. Amazon Aurora is a MySQL/PostgreSQL-compatible relational database that provides strong consistency, high durability (6 copies across 3 AZs), and supports complex SQL queries. Option B (DynamoDB) is wrong because it does not natively support complex SQL queries (though PartiQL exists, it's not as powerful). Option C (Redshift) is wrong because it is optimized for data warehousing and may have higher latency for single-row operations. Option D (DocumentDB) is wrong because it is MongoDB-compatible and not as strong on complex SQL joins.

Answer

Amazon DynamoDB

Answer

Amazon DocumentDB

Answer

Amazon Redshift

Question 8

A company has a high-traffic e-commerce application that uses Amazon RDS for MySQL. During flash sales, the database experiences high read load causing slow page loads. The application is read-heavy with occasional writes. Which design change would provide the most immediate performance improvement?

Accepted Answer

Create read replicas of the RDS instance. Adding read replicas offloads read traffic from the primary instance, improving performance for read-heavy workloads. Option B (Multi-AZ) is wrong because it provides high availability, not read scaling. Option C (ElastiCache) is wrong because it caches only data that is explicitly cached, but read replicas reduce load on the primary. Option D (instance upgrade) is wrong because it can help but is more expensive and less scalable than adding replicas.

Answer

Add an Amazon ElastiCache layer

Answer

Enable Multi-AZ deployment

Answer

Upgrade to a larger instance type

Question 9

A startup is building a social media application that needs to store user profiles, posts, comments, and likes. The data is highly interconnected, and the team wants to query relationships efficiently, such as 'find all friends of a user who liked a post'. Which database service is best suited for this workload?

Accepted Answer

Amazon Neptune. Amazon Neptune is a graph database designed for highly connected data, making it ideal for social graphs. Option A (DynamoDB) is wrong because it is a key-value/document database that requires additional application logic for graph queries. Option B (RDS) is wrong because relational databases can model graphs but are less efficient for deep relationship queries. Option D (ElastiCache) is wrong because it is a cache, not a primary database.

Answer

Amazon ElastiCache for Redis

Answer

Amazon DynamoDB

Answer

Amazon RDS for PostgreSQL

Question 10

A company uses Amazon DynamoDB to store IoT sensor data. Each sensor writes a record every second, and the application needs to query the last 24 hours of data for a specific sensor. The query must be very fast. Which table design and query pattern will minimize cost and latency?

Accepted Answer

Use a composite primary key: partition key = sensor ID, sort key = timestamp. Using a composite primary key with sensor ID as partition key and timestamp as sort key allows efficient querying of latest data with a Query operation on the partition key and a sort key condition. Option B (GSI on timestamp) is wrong because it would be less efficient for querying a specific sensor. Option C (scan) is wrong because it is expensive and slow. Option D (LSI on timestamp) is wrong because the sort key can be used directly without an LSI.

Answer

Use a simple primary key (sensor ID) and scan the table filtering by timestamp

Answer

Use a composite primary key: partition key = timestamp, sort key = sensor ID

Answer

Use a simple primary key (sensor ID) and a global secondary index on timestamp

Question 11

A company is migrating an on-premises Oracle database to AWS. The database is 5 TB and supports a critical OLTP application with high transaction rates. The application requires stored procedures, triggers, and strong consistency. Which AWS database service is most appropriate with minimal application changes?

Accepted Answer

Amazon RDS for Oracle. Amazon RDS for Oracle is the most direct migration path with minimal changes because it is fully compatible with Oracle and supports stored procedures, triggers, and strong consistency. Option A (Aurora PostgreSQL) is wrong because it requires converting Oracle-specific features to PostgreSQL. Option C (DynamoDB) is wrong because it does not support stored procedures or triggers natively (except for DynamoDB Streams and Lambda). Option D (Redshift) is wrong because it is a data warehouse, not for OLTP.

Answer

Amazon Aurora PostgreSQL

Answer

Amazon DynamoDB

Answer

Amazon Redshift

Question 12

A company needs to store session state for a web application that runs on Amazon EC2 instances behind an Application Load Balancer. The session data is small (less than 1 KB per user) and must be highly available with low latency. Which AWS database service is best for this use case?

Accepted Answer

Amazon ElastiCache for Redis. Amazon ElastiCache for Redis is ideal for storing session state because it is an in-memory key-value store with low latency and supports features like TTL and replication for high availability. Option B (DynamoDB) is wrong because it is not as fast for session state and is more expensive for small data. Option C (RDS) is wrong because it is a relational database with higher latency. Option D (S3) is wrong because it is object storage with higher latency.

Answer

Amazon S3

Answer

Amazon DynamoDB

Answer

Amazon RDS for MySQL

Question 13

A company is designing a database for a global e-commerce platform that requires low-latency reads and writes from multiple AWS regions. The data must be strongly consistent within a region but can be eventually consistent across regions. Which TWO services should the company consider?

Accepted Answer

Amazon DynamoDB Global Tables. DynamoDB Global Tables provides multi-region replication with eventual consistency across regions and strong consistency within a region. Aurora Global Database also provides low-latency reads across regions and can be configured for cross-region replication. Option C (Redshift) is wrong because it is not designed for multi-region active-active workloads. Option D (ElastiCache Global Datastore) is wrong because it is for Redis and provides cross-region replication but with eventual consistency and is not a primary database. Option E (RDS Cross-Region Read Replicas) is wrong because they are read-only and do not support writes from multiple regions.

Answer

Amazon ElastiCache for Redis Global Datastore

Answer

Amazon RDS Cross-Region Read Replicas

Answer

Amazon Redshift

Question 14

A company is moving a large-scale time-series application from Cassandra to a managed AWS service. The workload involves high-frequency writes (millions per second) and queries that aggregate data over time windows. Which THREE AWS services are suitable for this time-series workload?

Accepted Answer

Amazon OpenSearch Service. Amazon Timestream is purpose-built for time-series data. Amazon DynamoDB with time-series partitioning (using partition key as entity ID and sort key as timestamp) can handle high-volume writes and support time-based queries. Amazon OpenSearch Service can ingest time-series data and provide aggregation queries. Option D (Redshift) is wrong because it is optimized for OLAP on structured data, not high-frequency writes. Option E (Aurora) is wrong because it is not designed for time-series ingestion at millions of writes per second.

Answer

Amazon Aurora

Answer

Amazon Redshift

Question 15

A company is building a content management system that stores articles, images, and user comments. Articles are text-heavy and need full-text search. Images are binary files. Comments are relational with user IDs. Which TWO AWS services should be combined to best support this workload?

Accepted Answer

Amazon OpenSearch Service for full-text search. Amazon S3 is ideal for storing images (binary objects). Amazon OpenSearch Service provides full-text search capabilities for articles. Option C (RDS) is wrong because while it can store text and images, it is not optimal for search or binary storage. Option D (DynamoDB) is wrong because it does not support full-text search natively. Option E (ElastiCache) is wrong because it is a cache, not a primary store.

Answer

Amazon ElastiCache for Redis for caching

Answer

Amazon DynamoDB for articles and comments

Answer

Amazon RDS for MySQL for articles and comments