DBS-C01 Workload-Specific Database Design Practice Test 29 — 15 Questions

Question 1

A company is running a PostgreSQL database on Amazon RDS. They need to improve read performance for a reporting application that runs complex queries. The reporting application can tolerate slightly stale data. Which THREE actions should they take? (Choose three.)

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

Accepted Answer

Modify the DB parameter group to optimize settings for reporting workloads, such as increasing shared_buffers and work_mem.. Modifying the DB parameter group to increase shared_buffers and work_mem can significantly improve the performance of complex reporting queries by allowing more data to be cached in memory and larger sort operations to be performed in memory rather than on disk. Option B is correct because upgrading to a larger instance class with more vCPUs and memory directly addresses the compute and memory demands of complex queries, reducing query execution time. Option C is correct because creating read replicas offloads reporting traffic from the primary database, and since the reporting application can tolerate slightly stale data, the asynchronous replication lag is acceptable.

Answer

Enable Multi-AZ on the RDS instance.

Answer

Implement an Amazon ElastiCache cluster to cache query results.

Question 2

Refer to the exhibit. A developer runs a DynamoDB query against a global secondary index. The index's partition key is 'status' and sort key is 'created_at'. There are many items with status 'PENDING' in the table. Why does the query return zero items?

Accepted Answer

The global secondary index is not yet backfilled with data from the base table.. When a global secondary index (GSI) is created on an existing DynamoDB table, the index is populated asynchronously with data from the base table. If the query returns zero items, it likely means the backfill process has not yet completed, so the index does not yet contain the items that match the query. This is a common scenario when a GSI is newly added and the table contains a large number of items.

Answer

The query is incorrectly targeting the base table instead of the index.

Answer

The query must include a condition on the sort key to return results.

Answer

The key-condition-expression uses the wrong attribute name placeholder.

Question 3

A company runs a multi-tenant SaaS platform on AWS. Each tenant has their own database schema within a shared PostgreSQL database on Amazon RDS. The platform has grown to thousands of tenants, and the single RDS instance is experiencing performance degradation due to resource contention. Queries from one tenant can impact others. The company needs a solution that isolates tenants, provides predictable performance, and allows easy scaling. They also want to minimize application changes. The application uses an ORM that dynamically constructs SQL queries based on the tenant ID. Which solution is BEST?

Accepted Answer

Create separate RDS instances for each tenant and use RDS Proxy to pool connections per tenant. Modify the application to select the appropriate database instance based on tenant ID.. The best solution because it provides full tenant isolation by assigning each tenant a separate RDS instance, eliminating resource contention and ensuring predictable performance. RDS Proxy efficiently manages connection pooling for each instance, reducing overhead. The application change is minimal: the ORM can be configured to dynamically select the correct database instance based on the tenant ID, preserving the existing SQL-based logic. In contrast, Option A (Aurora Auto Scaling) still shares a single database, offering no isolation. Option C (RDS Proxy on the existing instance) also fails to isolate tenants. Option D (DynamoDB) would require a complete rewrite of the data layer, violating the requirement to minimize application changes.

Answer

Migrate to Amazon Aurora PostgreSQL and use Aurora Auto Scaling to add reader nodes as needed.

Answer

Implement Amazon RDS Proxy in front of the existing RDS instance to manage connections and reduce contention.

Answer

Migrate the application to use Amazon DynamoDB with tenant ID as the partition key, using global tables for scalability.

Question 4

A company is designing a new application that requires a relational database with strong consistency and support for transactions. The application will be accessed by users worldwide, and the database must provide low-latency reads in multiple regions. The company expects the workload to be unpredictable, with periods of very low activity followed by sudden spikes. They want to minimize operational overhead and only pay for the resources they use. Which AWS database solution should they choose?

Accepted Answer

Amazon Aurora Serverless v2 with Aurora Global Database.. Amazon Aurora Serverless v2 is a good choice for unpredictable workloads because it auto-scales capacity based on demand and you pay only for what you use. It also supports Aurora Global Database for low-latency reads in multiple regions. RDS does not have serverless capability. DynamoDB is serverless but not relational. Redshift is for analytics. So the best is Aurora Serverless v2 with Global Database.

Answer

Amazon Redshift with cross-Region snapshots.

Answer

Amazon RDS for PostgreSQL with read replicas in multiple regions.

Answer

Amazon DynamoDB with Global Tables.

Question 5

A company is designing a database for an IoT application that ingests millions of small sensor readings per second. The data is append-only and queries are primarily time-based aggregations with low latency requirements (under 10 ms). Which AWS database service is most suitable for this workload?

Accepted Answer

Amazon Timestream. Amazon Timestream is a purpose-built time-series database designed for IoT and operational applications that ingest high volumes of append-only data. It automatically manages storage tiers (in-memory and magnetic) and provides built-in time-based aggregation functions, enabling queries with sub-10 ms latency for recent data. This makes it the most suitable choice for the described workload of millions of sensor readings per second with low-latency aggregation queries.

Answer

Amazon DynamoDB

Answer

Amazon ElastiCache

Answer

Amazon Aurora

Question 6

A financial services company uses Amazon DynamoDB to store transaction records. The table has a partition key of 'account_id' and a sort key of 'transaction_time'. Recent queries for a specific account's transactions within a time range are experiencing high latency. The table has read capacity units set to auto-scaling. Which design change would most improve query performance?

Accepted Answer

Create a global secondary index with a different partition key.. Creating a global secondary index (GSI) with a different partition key can distribute read traffic across multiple partitions, avoiding hot partitions caused by frequent access to the same account_id. This improves query performance for time-range queries on a specific account. Option A (changing sort key) would not help if the partition itself is overloaded. Option B (DAX) caches results but does not address hot partitions, and may not help if the queries are not cacheable. Option C (increasing RCUs) may not help if the existing partition is hot due to throttling at the partition level.

Answer

Change the sort key to a composite attribute for better filtering.

Answer

Enable DynamoDB Accelerator (DAX) for the table.

Answer

Increase the read capacity units for the table.

Question 7

A startup is building a mobile app backend with user profiles and social features. They need a database that can handle flexible schemas, high read throughput for user profiles, and strong consistency for friend requests. Which database service should they choose?

Accepted Answer

Amazon DynamoDB. Amazon DynamoDB is the correct choice because it provides flexible schemas (schema-less tables) ideal for user profiles that may vary in attributes, supports high read throughput via auto-scaling and DAX caching, and offers strongly consistent reads (when requested) to ensure friend requests are processed reliably. Its fully managed nature and single-digit millisecond latency align with the startup's need for a scalable, consistent database.

Answer

Amazon RDS for MySQL

Answer

Amazon Neptune

Answer

Amazon DocumentDB

Question 8

A company is designing a database for an e-commerce platform that requires ACID transactions for order processing, complex joins for inventory reporting, and the ability to scale read replicas across multiple AWS regions. Which database service best meets these requirements?

Accepted Answer

Amazon Aurora. Amazon Aurora is the correct choice because it provides full ACID compliance for transactional workloads, supports complex joins via its MySQL/PostgreSQL-compatible relational engine, and offers up to 15 low-latency read replicas that can be placed in multiple AWS Regions using Aurora Global Database. This combination of strong consistency, relational query capabilities, and cross-region read scaling directly matches the e-commerce platform's requirements.

Answer

Amazon DynamoDB

Answer

Amazon RDS for SQL Server

Answer

Amazon Redshift

Question 9

A gaming company uses Amazon DynamoDB to store player scores. The table has a partition key of 'game_id' and a sort key of 'player_id'. They notice that during peak hours, write requests for a popular game 'g123' are throttled, while other games are unaffected. What is the most likely cause and solution?

Accepted Answer

Use a composite partition key with a random suffix to distribute writes.. The throttling is caused by a hot partition: all writes for the popular game 'g123' target the same partition key, exceeding the 1,000 WCU per partition limit. Adding a random suffix to the partition key (e.g., 'g123-1', 'g123-2') distributes writes across multiple partitions, eliminating the bottleneck without changing the access pattern.

Answer

Enable DynamoDB Accelerator (DAX) to cache writes.

Answer

The write capacity is too low; increase the table's write capacity units.

Answer

Enable auto-scaling on the table to handle burst write traffic.

Question 10

A company needs to store and analyze log data from thousands of servers. The logs are timestamped and rarely updated. Queries are mostly time-range aggregations. Which database service is best suited for this workload?

Accepted Answer

Amazon CloudWatch Logs. Amazon CloudWatch Logs is purpose-built for ingesting, storing, and analyzing timestamped log data from distributed sources. It supports real-time and historical time-range aggregations via Logs Insights, which uses a query language optimized for pattern matching and aggregation over time windows. The service automatically handles high-throughput ingestion from thousands of servers and is cost-effective for append-only, rarely updated log data.

Answer

Amazon ElastiCache for Redis

Answer

Amazon DynamoDB

Answer

Amazon RDS for PostgreSQL

Question 11

A company is migrating an on-premises MongoDB database to AWS. They need a managed database service that is compatible with MongoDB and supports automated backups, scaling, and high availability. Which service should they use?

Accepted Answer

Amazon DocumentDB. Amazon DocumentDB is a fully managed, MongoDB-compatible document database service designed for workloads that require MongoDB's document model, query patterns, and APIs. It supports automated backups (continuous backups to S3 with point-in-time recovery), automatic scaling of storage and compute, and multi-AZ high availability with synchronous replication across three Availability Zones, making it the correct choice for migrating an on-premises MongoDB database to a managed AWS service.

Answer

Amazon DynamoDB

Answer

Amazon RDS for MySQL

Answer

Amazon Neptune

Question 12

A company is designing a database for a social media analytics platform that requires: 1) storing relationships between users, posts, and interests; 2) running complex graph queries like 'find all friends of friends who like topic X'; 3) high availability with multi-region replication. Which TWO AWS services should they consider? (Choose TWO.)

Accepted Answer

Amazon Neptune. Amazon Neptune is a fully managed graph database service optimized for storing and querying highly connected data, such as user, post, and interest relationships. It supports property graph and RDF models, enabling complex graph queries like 'find all friends of friends who like topic X' using Gremlin or SPARQL. This directly satisfies the requirement for graph query capabilities. Amazon Aurora Global Database provides multi-region replication with low-latency global read operations and automatic failover, meeting the high availability and multi-region replication requirement. Together, Neptune handles the graph query workload, while Aurora Global Database provides the global distribution and disaster recovery needed for the analytics platform.

Answer

Amazon Redshift

Answer

Amazon DynamoDB with Global Tables

Answer

Amazon ElastiCache for Redis

Question 13

A company is building a real-time leaderboard for an online game using Amazon DynamoDB. The leaderboard must update scores within seconds and support queries for top 100 players. Which TWO design patterns should be used? (Choose TWO.)

Accepted Answer

Use DynamoDB Streams to trigger a Lambda function that updates a separate leaderboard table.. DynamoDB Streams can capture score updates in near real-time and trigger a Lambda function to maintain a separate leaderboard table optimized for top-100 queries. This decouples the write-heavy game table from the read-heavy leaderboard, ensuring low-latency updates without contention.

Answer

Create a global secondary index on the score attribute for efficient range queries.

Answer

Store the leaderboard in Amazon ElastiCache for Redis for low-latency reads.

Answer

Set the sort key to the score attribute for natural ordering.

Question 14

A company is migrating a large Oracle database to Amazon Aurora PostgreSQL. They need to minimize downtime and validate data consistency after migration. Which THREE steps should they include in their migration plan? (Choose THREE.)

Accepted Answer

Use AWS Database Migration Service (DMS) with ongoing replication to keep the target in sync.. AWS DMS supports ongoing replication (change data capture) from Oracle to Aurora PostgreSQL, allowing the target database to stay synchronized with the source during the migration. This minimizes downtime by enabling a cutover after the initial load, rather than requiring a full outage for the entire migration.

Answer

Create multiple Aurora Replicas for read scaling during migration.

Answer

Perform a homogeneous migration directly from Oracle to Aurora.

Question 15

A company runs an e-commerce platform on Amazon RDS for MySQL with a Multi-AZ deployment. The database has a table 'orders' with 50 million rows. During Black Friday sales, the application experiences severe slowdowns. Analysis shows that the CPU utilization is at 90% and there are many slow queries that perform full table scans on the 'orders' table. The development team has already added indexes on the most queried columns, but the problem persists. The database specialist suspects that the issue is not solely due to missing indexes. They notice that the queries often filter on a combination of 'order_date', 'customer_id', and 'status', and that the data distribution is heavily skewed: 80% of orders are 'completed' status. The 'order_date' range is typically the last 30 days. What should the database specialist do to improve query performance?

Accepted Answer

Partition the 'orders' table by 'status' and 'order_date' and create covering indexes on common query patterns.. Partitioning the 'orders' table by 'status' and 'order_date' can significantly reduce the amount of data scanned, as queries often filter on these columns. With 80% of orders being 'completed', partitioning by status allows queries for non-completed statuses to skip most rows, and range partitioning by order_date (e.g., monthly) further limits scans to relevant time periods. Adding covering indexes on common query patterns (e.g., (status, order_date, customer_id)) can make these partition scans index-only. Option B (read replicas) offloads read traffic but does not fix the slow queries themselves—they would still perform full scans on the replicas. Option C (caching) helps with repeated queries but not with ad-hoc analytical scans that still hit the database. Option D (vertical scaling) provides temporary relief but does not address the root cause of unnecessary full table scans.

Answer

Create multiple read replicas and distribute read traffic.

Answer

Implement an in-memory caching layer using Amazon ElastiCache for frequently accessed data.

Answer

Upgrade the RDS instance to a larger instance class with more vCPUs and memory.