DBS-C01 Workload-Specific Database Design Practice Test 9 — 15 Questions

Question 1

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.

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Amazon DynamoDB

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Amazon DocumentDB (with MongoDB compatibility)

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Amazon ElastiCache for Redis

Question 2

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.

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Amazon DynamoDB

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Amazon Redshift

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Amazon ElastiCache

Question 3

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. Option C is correct because 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

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TTL is not enabled on the table

Answer

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

Question 4

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.

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Self-managed MongoDB on Amazon EC2

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Amazon DynamoDB

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Amazon RDS for MySQL

Question 5

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.

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Decreasing write capacity units

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Using a single partition key

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Increasing read capacity units

Question 6

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.

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Cost of storage

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Need for encryption at rest

Question 7

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.

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Use strongly consistent reads

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Increase write capacity units

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Decrease read capacity units

Question 8

A company is migrating an on-premises MongoDB workload to Amazon DocumentDB. The current workload uses secondary indexes heavily for reporting queries. Which design consideration should the company evaluate to ensure optimal performance on DocumentDB?

Accepted Answer

Evaluate using covered queries instead of secondary indexes.. Covered queries retrieve all required data from an index without accessing the underlying documents, reducing I/O and improving performance. In Amazon DocumentDB, which is compatible with MongoDB 4.0, covered queries can often replace secondary indexes for reporting workloads, minimizing index maintenance overhead and storage costs. This design consideration directly addresses the heavy reliance on secondary indexes by optimizing query execution.

Answer

Enable Multi-AZ deployment to improve read performance.

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Shard the collection across multiple DocumentDB clusters.

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Use the MongoDB aggregation pipeline to bypass indexing.

Question 9

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.

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Enable Aurora Auto Scaling to increase storage capacity.

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Migrate to Aurora Serverless v2 for automatic scaling.

Question 10

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 11

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.. Option A is correct because 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.

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

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

Question 12

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.

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

Answer

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

Question 13

A healthcare application stores patient records in Amazon DynamoDB. Each record has a unique patient ID and contains sensitive health information. The application must encrypt data at rest and ensure that only authorized services can access the data. Which combination of design choices meets these requirements?

Accepted Answer

Enable DynamoDB encryption at rest using AWS KMS and use IAM policies to restrict access.. Option C is correct because DynamoDB encryption at rest using AWS KMS provides server-side encryption for sensitive patient data, while IAM policies allow fine-grained access control to ensure only authorized services can access the table. This combination directly meets both the encryption and access control requirements without unnecessary complexity or service mismatches.

Answer

Implement client-side encryption and use Lambda to validate access.

Answer

Enable S3 server-side encryption with AWS KMS and use bucket policies.

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Use AWS CloudHSM for key storage and VPC endpoints for access control.

Question 14

A startup is building a social media application that requires a database to store user relationships (followers, following) and support graph queries. The data volume is expected to grow to tens of terabytes. 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, such as social media user relationships (followers, following). It supports both property graph and RDF models, enabling efficient graph traversal queries using Gremlin or SPARQL, which is ideal for this workload. Neptune scales to tens of terabytes and provides low-latency query performance for complex graph patterns, making it the most suitable choice.

Answer

Amazon RDS for MySQL with self-joins.

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Amazon Redshift.

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Amazon DynamoDB with adjacency list design.

Question 15

A company uses Amazon RDS for PostgreSQL to run a reporting application. The reporting queries are complex and take several minutes to complete, causing performance impact on the primary instance. The company wants to isolate the reporting workload without data staleness. Which solution should they implement?

Accepted Answer

Create a read replica and direct reporting queries to it.. Option D is correct because creating a read replica of the Amazon RDS for PostgreSQL primary instance allows you to offload complex reporting queries to the replica without impacting the primary. Read replicas use asynchronous replication (based on PostgreSQL streaming replication), which provides near-real-time data with minimal staleness, satisfying the requirement to isolate the reporting workload without significant data staleness.

Answer

Implement an Amazon ElastiCache cluster to cache query results.

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Enable Multi-AZ and use the standby instance for reporting.

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Take a manual snapshot of the database and restore it for reporting.