DBS-C01 Workload-Specific Database Design Practice Test 10 — 15 Questions

Question 1

A gaming company uses Amazon DynamoDB with global tables across two regions. They notice increased write latency and throttling during peak hours. The access pattern is mostly writes to a small set of hot partitions. Which design change would best address this?

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

Accepted Answer

Implement write sharding using a random suffix on the partition key. The issue is hot partitions caused by a small set of partition keys receiving the majority of writes. By implementing write sharding with a random suffix on the partition key, you distribute writes across multiple partitions, reducing throttling and write latency. This directly addresses the root cause of uneven access patterns, unlike the other options that either cache reads, adjust capacity mode, or scale capacity without solving the partition-level bottleneck.

Answer

Enable DynamoDB Accelerator (DAX)

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Switch to DynamoDB on-demand capacity mode

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Increase write capacity using auto scaling

Question 2

A company needs to store JSON documents that are frequently accessed by a web application. The documents have varying attributes and the query pattern includes filtering on multiple fields. Which AWS database service is most suitable?

Accepted Answer

Amazon DynamoDB. Amazon DynamoDB is the most suitable choice because it is a fully managed NoSQL key-value and document database that natively supports JSON documents with varying attributes. Its flexible schema allows each item to have different attributes, and its support for secondary indexes (Local Secondary Indexes and Global Secondary Indexes) enables efficient filtering and querying on multiple fields without requiring predefined schemas or complex joins.

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Amazon Neptune

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Amazon ElastiCache for Redis

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Amazon RDS for MySQL

Question 3

A company is building a real-time analytics dashboard from IoT sensor data. Data arrives as time-series with millions of writes per second. The dashboard queries the last hour of data with aggregations. Which database design is most cost-effective?

Accepted Answer

Amazon Timestream. Amazon Timestream is purpose-built for time-series data, offering automatic tiered storage (in-memory for recent data and magnetic for historical) and built-in aggregation functions optimized for time-based queries. This design handles millions of writes per second cost-effectively, as it eliminates the need for manual sharding or TTL management, and its serverless model charges only for data written and queried, making it ideal for real-time analytics on the last hour of IoT sensor data.

Answer

Amazon DynamoDB with TTL and DAX

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Amazon Redshift with streaming ingestion

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Amazon RDS for PostgreSQL with TimescaleDB extension

Question 4

A company uses Amazon RDS for PostgreSQL for its e-commerce platform. The application team reports increasing read latency on the primary instance during sales events. Which action should be taken to reduce read load on the primary?

Accepted Answer

Create one or more read replicas. Creating one or more read replicas offloads read traffic from the primary RDS for PostgreSQL instance, directly addressing the increased read latency during sales events. Read replicas are asynchronous replicas that can serve SELECT queries, reducing the load on the primary without requiring application changes to the write path. This is the standard AWS solution for scaling read-heavy workloads in RDS.

Answer

Enable Multi-AZ deployment

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Migrate to Amazon DynamoDB

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Increase the instance size of the primary

Question 5

A company is designing a database for a social media application that needs to handle friend-of-friend queries and recommendation engine traversals. Which AWS database is best suited?

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 ideal for friend-of-friend queries and recommendation engine traversals that require navigating complex relationships with low latency.

Answer

Amazon DynamoDB

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Amazon DocumentDB (with MongoDB compatibility)

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Amazon ElastiCache for Redis

Question 6

A startup needs a fully managed relational database with automated backups and scaling. They expect unpredictable workloads. Which AWS service meets these requirements?

Accepted Answer

Amazon Aurora Serverless. Amazon Aurora Serverless is a fully managed relational database that automatically scales capacity up or down based on application demand, making it ideal for unpredictable workloads. It also provides automated backups, continuous backups to Amazon S3, and point-in-time recovery, meeting all stated requirements.

Answer

Amazon DynamoDB

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Amazon Redshift

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Amazon ElastiCache

Question 7

A company uses Amazon DynamoDB for a session management system. They need to store session data with a TTL of 24 hours. However, they notice that expired items are not being deleted promptly, causing storage costs to increase. What is the most likely cause?

Accepted Answer

DynamoDB typically deletes expired items within 48 hours. DynamoDB's TTL mechanism typically deletes expired items within 48 hours, not immediately. The service processes TTL deletions as a background process, and while items are marked as expired at the TTL time, actual deletion can be delayed up to 48 hours. This explains why expired session data persists and increases storage costs despite TTL being properly configured.

Answer

The table has insufficient write capacity

Answer

TTL is not enabled on the table

Answer

The TTL attribute is set as a string instead of a number

Question 8

A company wants to run a MongoDB-compatible database on AWS with automated patching and backups. Which service should they choose?

Accepted Answer

Amazon DocumentDB (with MongoDB compatibility). Amazon DocumentDB with MongoDB compatibility is the correct choice because it is a fully managed, MongoDB-compatible database service that provides automated patching and backups. It supports the MongoDB wire protocol and drivers, allowing existing MongoDB applications to migrate with minimal changes while offloading administrative tasks like patching, backup, and replication to AWS.

Answer

Self-managed MongoDB on Amazon EC2

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Amazon DynamoDB

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Amazon RDS for MySQL

Question 9

Which TWO design patterns are commonly used to handle hot partitions in Amazon DynamoDB? (Choose 2.)

Accepted Answer

Write sharding. Write sharding distributes writes across multiple partition keys to prevent a single partition from exceeding the 1,000 WCU limit. Adding random suffixes to partition keys is a specific write sharding technique that spreads writes across many partitions, avoiding hot spots.

Answer

Decreasing write capacity units

Answer

Using a single partition key

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Increasing read capacity units

Question 10

Which THREE factors should be considered when choosing between Amazon RDS and Amazon DynamoDB for a new application? (Choose 3.)

Accepted Answer

Access patterns (predictable vs. ad-hoc). Amazon RDS is a relational database service that excels at complex queries involving joins and aggregations, while DynamoDB is a NoSQL key-value and document database optimized for predictable, high-scale access patterns. The choice between them hinges on whether the application requires relational features (RDS) or can tolerate denormalized schemas for low-latency, horizontal scaling (DynamoDB). Option C is correct because DynamoDB is designed for ad-hoc, single-key lookups and simple queries, whereas RDS supports complex, ad-hoc SQL queries with joins.

Answer

Cost of storage

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Need for encryption at rest

Question 11

Which TWO techniques can reduce read latency for frequently accessed data in Amazon DynamoDB? (Choose 2.)

Accepted Answer

Add Global Secondary Indexes (GSI) for common query patterns. Adding Global Secondary Indexes (GSI) allows you to pre-materialize alternative query patterns, enabling efficient lookups on non-key attributes without scanning the entire table. This reduces read latency for frequently accessed data by providing a pre-sorted and partitioned index that DynamoDB can query directly, avoiding expensive full-table scans.

Answer

Use strongly consistent reads

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Increase write capacity units

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Decrease read capacity units

Question 12

A financial services company runs an Amazon Aurora MySQL database. The application performs complex joins and aggregations on large tables, causing high CPU utilization on the writer instance. The team wants to reduce load without changing the application code. Which solution would best address this issue?

Accepted Answer

Create one or more Aurora Replicas and route read traffic to them.. Creating one or more Aurora Replicas and routing read traffic to them offloads the complex joins and aggregations from the writer instance, reducing CPU utilization without requiring application code changes. Aurora Replicas share the same underlying storage volume as the writer, so they serve read queries with minimal replication lag while the writer focuses on write operations.

Answer

Switch to asynchronous replication to reduce load on the primary.

Answer

Enable Aurora Auto Scaling to increase storage capacity.

Answer

Migrate to Aurora Serverless v2 for automatic scaling.

Question 13

A gaming company uses Amazon DynamoDB as the database for user profiles and game state. The application requires strongly consistent reads for the user's own profile, but eventually consistent reads for leaderboard queries. How should the company design the table and queries?

Accepted Answer

Use the ConsistentRead parameter set to true for profile queries and false for leaderboard queries.. DynamoDB supports both strongly consistent reads and eventually consistent reads on the same table, controlled by the `ConsistentRead` parameter in the `GetItem`, `Query`, or `Scan` API calls. Setting `ConsistentRead=true` for profile queries ensures the most up-to-date data, while `ConsistentRead=false` (the default) for leaderboard queries provides lower latency and higher throughput, which is ideal for read-heavy, non-critical data. This design avoids the cost and complexity of multiple tables or additional services.

Answer

Create two separate tables: one for strong consistency and one for eventual consistency.

Answer

Enable DynamoDB Accelerator (DAX) for strong consistency on all reads.

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Configure DynamoDB Streams to replicate data to a second table for strong consistency.

Question 14

A media company uses Amazon ElastiCache for Redis to cache database query results and reduce load on the primary database. The cache hit ratio is low because the cache is purged frequently. The team wants to improve the hit ratio without increasing the cache size. Which strategy should they implement?

Accepted Answer

Increase the TTL for cached entries to reduce early evictions.. Increasing the TTL (Time-To-Live) for cached entries allows them to remain in the cache longer, reducing the frequency of evictions due to expiration. Since the cache is purged frequently, a low TTL is likely causing entries to expire before they can be reused, which directly lowers the hit ratio. By extending the TTL, the team can retain popular entries longer without needing to increase the cache size, as the existing memory is used more efficiently.

Answer

Implement lazy loading to populate cache only on demand.

Answer

Use write-through caching to update cache on every database write.

Answer

Set eviction policy to allkeys-random to spread evictions evenly.

Question 15

A company is designing a global e-commerce application that requires a relational database with sub-10ms read latency across multiple AWS Regions. The database will store inventory and product catalog data. Which database design should they choose?

Accepted Answer

Use Amazon Aurora Global Database with a primary cluster in one Region and secondary clusters in other Regions.. Amazon Aurora Global Database is designed for low-latency global reads, with typical replication lag of under 1 second and read latency in the single-digit milliseconds from secondary clusters. It uses a dedicated storage-based replication mechanism that does not impact the performance of the primary cluster, making it ideal for a global e-commerce application requiring sub-10ms reads across multiple AWS Regions.

Answer

Use Amazon DynamoDB Global Tables with eventual consistency.

Answer

Deploy Multi-AZ for Amazon RDS and use Route 53 latency-based routing.

Answer

Set up Cross-Region Read Replicas for Amazon RDS MySQL.