DBS-C01 Practice Test 15 — 15 Questions

Question 1

A company is designing a multi-tenant SaaS application on Amazon RDS for PostgreSQL. Tenants have vastly different data sizes and access patterns. The current design uses a separate schema per tenant, but some tenants experience slow queries while others are fine. Which approach would best isolate tenant performance and simplify management?

Accepted Answer

Use a separate database per tenant.. Option C is correct because using a separate database per tenant provides the strongest resource isolation at the database instance level. This design prevents noisy neighbors—tenants with large data volumes or heavy access patterns from degrading the performance of other tenants—and simplifies management tasks such as backup, restore, and point-in-time recovery on a per-tenant basis. Amazon RDS for PostgreSQL supports multiple databases within a single DB instance, and each database operates with its own catalog, tables, and connection pool, ensuring that query execution and memory allocation are not shared across tenants.

Answer

Use row-level security (RLS) policies within a single schema to restrict tenant data access.

Answer

Keep the current schema design and add a tenant_id index to all tables.

Answer

Use Amazon RDS Proxy to manage connections and improve performance.

Question 2

A company is designing a database for an analytics workload that requires storing 5 TB of data and running complex SQL queries with joins. The workload is read-heavy and requires high concurrency. Which TWO services are suitable for this workload? (Choose two.)

Accepted Answer

Amazon RDS for MySQL with read replicas. Amazon RDS for MySQL with read replicas is suitable because the workload is read-heavy and requires high concurrency. Read replicas offload SELECT traffic from the primary instance, allowing complex SQL queries with joins to scale horizontally while maintaining ACID compliance for the 5 TB dataset.

Answer

Amazon ElastiCache for Redis

Answer

Amazon DynamoDB

Answer

Amazon S3

Question 3

A company is designing a database for a ride-sharing application that needs to store real-time driver locations and trip history. The application requires low-latency updates to driver locations (every few seconds) and the ability to query nearby drivers within a radius. The company expects millions of drivers and trips. Which AWS database service should the database specialist recommend for storing real-time driver locations and supporting proximity queries?

Accepted Answer

Amazon DynamoDB with a Geohash-based partition key and a Global Secondary Index. Amazon DynamoDB with a Geohash-based partition key and a Global Secondary Index is the correct choice because it provides the low-latency writes (single-digit milliseconds) required for updating driver locations every few seconds, while the Geohash-based key enables efficient proximity queries by grouping nearby drivers into the same partition. The Global Secondary Index allows querying by geohash prefix to find drivers within a radius, scaling to millions of drivers and trips with DynamoDB's auto-scaling and fully managed infrastructure.

Answer

Amazon RDS for PostgreSQL with PostGIS extension

Answer

Amazon ElastiCache for Redis with geospatial data types

Answer

Amazon Timestream

Question 4

A company uses Amazon DynamoDB for a time-series IoT workload. Each device sends a data point every minute. The primary key consists of device_id (partition key) and timestamp (sort key). The company wants to efficiently retrieve the latest 10 data points for a specific device. Which query design is most efficient?

Accepted Answer

Query the table with ScanIndexForward=false and Limit=10.. Option C is correct because Query with ScanIndexForward=false retrieves items in descending order by the sort key (timestamp), and Limit=10 stops after the first 10 items, which are the most recent 10 data points for the given device_id. This is the most efficient design as it reads only the 10 items needed, leveraging the DynamoDB local secondary index or table's sort key order without any post-processing.

Answer

Use GetItem on the device_id partition key with the maximum timestamp.

Answer

Query the table with ScanIndexForward=true and Limit=10, then reverse the result set.

Answer

Scan the entire table and filter by device_id, then sort by timestamp.

Question 5

A company runs a PostgreSQL database on Amazon RDS for a CRM application. The database is 500 GB and experiences high read traffic. The company wants to improve read scalability and reduce latency. Which TWO actions should the company take? (Choose two.)

Accepted Answer

Implement Amazon ElastiCache for Redis to cache common queries.. Option A is correct because Amazon ElastiCache for Redis can cache the results of frequently executed read queries, offloading read traffic from the RDS PostgreSQL instance and reducing latency for repeated queries. This is especially effective for read-heavy workloads where the same data is requested many times, as Redis provides sub-millisecond response times and reduces the load on the database.

Answer

Store large objects in Amazon S3 and reference them.

Answer

Enable Multi-AZ for automatic failover.

Answer

Migrate data to Amazon DynamoDB with Global Tables.

Question 6

A financial services company uses Amazon DynamoDB to store transaction records. Each transaction has a partition key of customer_id and a sort key of transaction_timestamp. The application queries transactions for a specific customer within a date range. Recently, the query latency increased significantly for customers with a large number of transactions. The company needs to improve query performance without changing the application code. The table is provisioned with 5000 RCUs and 2000 WCUs. Which design change should be made to optimize for this workload?

Accepted Answer

Create a global secondary index with customer_id as partition key and transaction_timestamp as sort key.. Option A is correct because creating a global secondary index (GSI) with customer_id as the partition key and transaction_timestamp as the sort key allows efficient querying of transactions for a specific customer within a date range. The existing table's sort key is transaction_timestamp, but the GSI provides a separate index optimized for this access pattern, avoiding full table scans on large customer partitions. This improves query performance without requiring application code changes, as the application can query the GSI directly.

Answer

Enable DynamoDB Accelerator (DAX) on the table.

Answer

Increase the provisioned RCUs to 10000.

Answer

Change the sort key to a composite key including a tenant identifier.

Question 7

A database administrator is troubleshooting a slow-performing query on an Amazon RDS for MySQL instance. The slow query log shows the above entry. Based on the exhibit, which index would most improve the query performance?

Accepted Answer

Composite index on (`status`, `created_at`).. The query filters on `status` and then sorts or filters on `created_at`. A composite index on (`status`, `created_at`) allows MySQL to use the index for both the equality condition on `status` and the range or sort on `created_at`, avoiding a filesort and reducing row scans. This is the most efficient index for this query pattern.

Answer

Index on `created_at` only.

Answer

Index on `status` only.

Answer

Full-text index on `status` and `created_at`.

Question 8

A company needs to store application logs for 90 days and run periodic analytical queries. The logs are generated at 1 TB per day. Which storage solution is most cost-effective?

Accepted Answer

Store logs in Amazon S3 and use S3 Select for queries.. Amazon S3 is the most cost-effective storage solution for 90-day retention of 1 TB/day of application logs, as it offers low-cost object storage with lifecycle policies to automatically expire data after 90 days. S3 Select allows you to run analytical queries (e.g., filtering, aggregations) directly on the data stored in S3 using SQL-like statements, without needing to load data into a separate analytics engine, thus minimizing compute costs and operational overhead.

Answer

Store logs in Amazon RDS for MySQL with partitioning.

Answer

Store logs in Amazon Redshift with automatic compression.

Answer

Store logs in Amazon DynamoDB with TTL for expiration.

Question 9

A company is designing a database for an e-commerce platform that needs to store product catalog data. The data is highly relational with many-to-many relationships between products, categories, and suppliers. The platform requires ACID transactions and complex joins. Which TWO AWS database solutions are suitable for this workload? (Choose TWO.)

Accepted Answer

Amazon Aurora MySQL. Amazon Aurora MySQL is a fully ACID-compliant relational database that supports complex joins and many-to-many relationships through foreign keys and junction tables. It is optimized for high-throughput e-commerce workloads with features like auto-scaling storage and up to 15 low-latency read replicas, making it suitable for product catalog data that requires transactional consistency.

Answer

Amazon ElastiCache for Redis

Answer

Amazon Neptune

Answer

Amazon DynamoDB

Question 10

A company is designing a database solution for a global user base that requires single-digit millisecond read latency for user profile data. The data is eventually consistent and can tolerate a few seconds of staleness. Which TWO AWS services or features should be combined to achieve this latency?

Accepted Answer

Amazon DynamoDB Global Tables.. Amazon DynamoDB Global Tables provides a fully managed, multi-Region, multi-active database that replicates data across AWS Regions with sub-second latency, enabling single-digit millisecond reads for user profile data. Combined with Amazon CloudFront as a CDN, you can cache DynamoDB responses at edge locations, further reducing read latency for a global user base while tolerating eventual consistency and a few seconds of staleness.

Answer

Amazon ElastiCache for Redis with global datastore.

Answer

Amazon RDS for MySQL with cross-Region read replicas.

Answer

Amazon Aurora Global Database.

Question 11

A company is designing a highly available e-commerce application using Amazon DynamoDB. The application requires strongly consistent reads for inventory data and eventual consistency for user session data. Which TWO design decisions should the company make?

Accepted Answer

Use strongly consistent reads for the inventory table by setting ConsistentRead=true in the query.. Option D is correct because DynamoDB supports strongly consistent reads by setting the `ConsistentRead=true` parameter in the GetItem, Query, or Scan API calls. This ensures that the application always reads the most recent write, which is critical for inventory data where accuracy is paramount. Strongly consistent reads come at the cost of higher latency and lower throughput compared to eventually consistent reads, but they meet the requirement for inventory consistency.

Answer

Enable DynamoDB Streams on the inventory table to replicate data for disaster recovery.

Answer

Use DynamoDB Accelerator (DAX) for the inventory table to provide strongly consistent reads.

Answer

Use a single DynamoDB table for both inventory and session data with different partition keys.

Question 12

A company runs a large-scale e-commerce platform using Amazon RDS for MySQL with a Multi-AZ deployment. The database has a table 'orders' with 200 million rows. Recently, they added a new index on the 'order_date' column to improve reporting queries. After adding the index, they noticed increased write latency and occasional replication lag. The application writes new orders continuously. The table experiences about 10,000 writes per second. The DB instance is db.r5.4xlarge. The index creation was done using the ALTER TABLE statement with a default algorithm. What is the most likely cause of the increased write latency and replication lag?

Accepted Answer

The index was created using the default algorithm (COPY), which locks the table and blocks writes, causing replication lag.. Option C is correct because the default algorithm for ALTER TABLE in MySQL is COPY, which creates a new table, copies all rows, and rebuilds indexes. During this process, the table is locked with a write lock, blocking DML operations and causing increased write latency. In a Multi-AZ deployment, the DDL is replicated to the standby, but the lock on the primary delays writes, which can manifest as replication lag when the standby applies the same blocking DDL.

Answer

The index creation DDL statement is not replicated to the standby instance, causing inconsistency.

Answer

The instance size is insufficient for the write workload.

Answer

The new index is causing excessive overhead on write operations due to index maintenance.

Question 13

A company uses Amazon DynamoDB for a gaming application that stores player data. The application frequently accesses items by the player's user ID. However, the company also needs to query players by their subscription tier (Gold, Silver, Bronze) and registration date. Which design should the database specialist recommend for this access pattern?

Accepted Answer

Create a Global Secondary Index (GSI) on subscription tier and registration date.. A Global Secondary Index (GSI) on subscription tier and registration date is the correct choice because it allows efficient querying on non-primary key attributes without affecting the base table's primary key structure. DynamoDB GSIs support eventually consistent reads and can be created on any table, enabling the required access pattern of querying players by subscription tier and registration date while maintaining the primary access pattern by user ID.

Answer

Export the data to Amazon Elasticsearch Service for querying.

Answer

Create a Local Secondary Index (LSI) on subscription tier and registration date.

Answer

Enable DynamoDB Streams and process the stream to populate a separate table.

Question 14

A company runs a global application using Amazon Aurora Global Database. The primary region is us-east-1, and secondary regions are eu-west-1 and ap-southeast-1. The application reports that writes to the primary are taking longer than expected. What is the most likely cause?

Accepted Answer

The Global Database replication to secondary regions is causing synchronous commit latencies.. Aurora Global Database uses asynchronous replication from the primary to secondary regions, not synchronous. However, the question states writes to the primary are taking longer than expected, which is a symptom of synchronous replication overhead. In Aurora Global Database, the primary commits locally and then asynchronously replicates to secondaries, so writes should not be directly impacted by secondary region latency. The most likely cause is that the primary DB instance is under-provisioned (Option C), leading to resource contention and slower write operations.

Answer

Multi-AZ failover occurred in the primary region.

Answer

The primary DB instance is under-provisioned.

Answer

Read replicas in secondary regions are overloaded.

Question 15

A company runs an online transaction processing (OLTP) workload on Amazon RDS for PostgreSQL. The database has grown to 2 TB and the company needs to run complex analytical queries that join multiple large tables. These analytical queries are slowing down the OLTP operations. What is the MOST cost-effective solution to separate the workloads?

Accepted Answer

Create an RDS for PostgreSQL read replica and route analytical queries to it.. Option A is correct because creating a read replica offloads analytical queries from the primary instance, and it's cost-effective. Option B is wrong because Amazon Redshift is a data warehouse, more expensive and overkill for this use case. Option C is wrong because DynamoDB is NoSQL and not suitable for complex joins. Option D is wrong because ElastiCache is an in-memory cache, not for analytical queries.

Answer

Use Amazon ElastiCache for caching analytical query results.

Answer

Migrate the analytical queries to Amazon Redshift.

Answer

Migrate the OLTP workload to Amazon DynamoDB.