DBS-C01 Practice Test 19 — 15 Questions

Question 1

A social media startup is designing a database for user activity feeds. Each user follows up to 5,000 other users. The feed must show the latest 100 posts from followed users with latency under 200ms. Reads are 10x writes. Which database design best meets these requirements?

Accepted Answer

Use Amazon DynamoDB with a fan-out on write pattern, storing each user's feed in a separate partition. Option B is correct because DynamoDB with a fan-out on write pattern pre-joins posts into per-user feed tables, enabling fast reads with low latency. Option A (RDS with read replicas) would struggle with the high write volume and join complexity. Option C (ElastiCache as primary store) loses durability. Option D (DynamoDB single table with GSI) would require expensive scans to filter followed users' posts.

Answer

Use Amazon RDS for PostgreSQL with read replicas and materialized views refreshed every minute

Answer

Use Amazon ElastiCache for Redis as a primary data store with sorted sets per user

Answer

Use a single DynamoDB table with a global secondary index on user_id and timestamp

Question 2

A company runs a MySQL-compatible database on Amazon RDS for a mission-critical application. The database experiences high write latency due to frequent index updates. The team wants to redesign the database to reduce write amplification and improve insert performance. Which TWO design changes could help?

Accepted Answer

Use batch INSERT statements instead of single-row inserts. Option A (removing unused indexes) directly reduces write amplification. Option C (batch inserts) reduces per-row overhead. Option B (increasing instance size) may not address root cause. Option D (changing to InnoDB) is already MySQL's default engine; switching to MyISAM is not recommended. Option E (normalizing) might increase joins but not necessarily reduce write amplification.

Answer

Switch the storage engine from InnoDB to MyISAM

Answer

Upgrade to a larger RDS instance class

Answer

Normalize the database schema to reduce data redundancy

Question 3

A company needs to run complex analytical queries on structured data in Amazon S3 without loading data into a database. The queries must execute quickly and support standard SQL. Which service should they use?

Accepted Answer

Amazon Athena. Amazon Athena is an interactive query service that analyzes data in S3 using standard SQL. Option B (Redshift Spectrum) also works but requires a Redshift cluster. Option C (Glue ETL) is for ETL jobs. Option D (QuickSight) is a BI tool.

Answer

Amazon QuickSight

Answer

AWS Glue ETL jobs

Answer

Amazon Redshift Spectrum

Question 4

A company runs a time-series application on Amazon DynamoDB. The data has a pattern of frequent writes for recent data and rare reads for older data. They want to optimize storage costs and query performance for the time-series data. Which THREE strategies should they implement? (Choose THREE.)

Accepted Answer

Use DynamoDB Time to Live (TTL) to automatically delete old data after a certain period. DynamoDB Time to Live (TTL) automatically deletes expired items, reducing storage. Archiving old data to S3 Glacier provides cheap storage. Using S3 and Athena for historical queries avoids scanning old data in DynamoDB. Option D is wrong because increasing RCU is expensive and not a storage optimization. Option E is wrong because decreasing WCU may cause throttling.

Answer

Increase DynamoDB read capacity units to improve query performance

Answer

Decrease DynamoDB write capacity units to reduce cost

Question 5

A data analyst reports that a nightly ETL job to Amazon Redshift is failing with timeout errors shown in the exhibit. The cluster is a dc2.large with 2 nodes. The ETL job inserts large volumes of data. What is the most likely cause?

Accepted Answer

The cluster has insufficient disk space for the data load.. Option C is correct because dc2 nodes have limited local SSD storage; if the cluster runs out of disk space, writes time out. Option A is wrong because Redshift does not have a max_connections parameter like RDS. Option B is wrong because network ACLs would cause connection failures, not timeouts after connection. Option D is wrong because WLM queue timeouts cause different errors.

Answer

The cluster has reached the maximum number of connections.

Answer

The workload manager (WLM) queue timeout is too low.

Answer

The security group is blocking inbound traffic from the ETL server.

Question 6

A company needs to store and query JSON documents that have varying attributes. The workload is read-heavy, and the team wants to use SQL-like queries. Which service is most appropriate?

Accepted Answer

Amazon DocumentDB (with MongoDB compatibility). Option B is correct because Amazon DocumentDB (with MongoDB compatibility) stores JSON documents natively and supports MongoDB-like queries. Option A is wrong because DynamoDB is key-value, not optimized for rich JSON queries. Option C is wrong because RDS requires a fixed schema. Option D is wrong because Neptune is a graph database.

Answer

Amazon Neptune

Answer

Amazon DynamoDB with PartiQL

Answer

Amazon RDS for MySQL with JSON data type

Question 7

A company uses Amazon RDS for Oracle with a Multi-AZ deployment for a critical OLTP application. During a recent failover test, they noticed that the application experienced a two-minute downtime. The team wants to reduce downtime to under 30 seconds during automatic failovers. What should they do?

Accepted Answer

Migrate to Amazon Aurora with Multi-AZ and use the Aurora auto-failover feature. Amazon RDS Multi-AZ failovers typically take 1-2 minutes. To reduce failover times, use Multi-AZ with two readable standbys in RDS for Oracle (if supported) or migrate to Amazon Aurora, which has faster failover (typically under 30 seconds) and also provides read replicas. Option A does not help. Option B reduces failover detection time but not the failover process itself. Option D is about read replicas, not failover.

Answer

Add a read replica to offload reads

Answer

Reduce the DNS TTL value to 5 seconds

Answer

Enable Automatic Failover in the RDS console

Question 8

A company runs an Oracle database on Amazon RDS. The database is used by multiple applications, and the company needs to capture all data modification language (DML) changes for auditing. Which solution should be used?

Accepted Answer

Use AWS DMS with change data capture (CDC) to stream changes to Amazon S3.. Option D is correct because AWS DMS with CDC and stream to S3 allows capturing DML changes and storing them for auditing. Option A is wrong because RDS does not support Oracle Flashback. Option B is wrong because RDS does not support Oracle Audit Vault. Option C is wrong because CloudTrail does not capture database-level DML.

Answer

Use AWS CloudTrail to capture database events.

Answer

Enable Oracle Flashback and store the flashback logs.

Answer

Install Oracle Audit Vault on the RDS instance.

Question 9

A gaming company uses Amazon DynamoDB to store player profiles. The table has partition key 'player_id' and sort key 'game_id'. During a new game launch, write traffic to a subset of players (influencers) spikes, causing throttling. The table uses on-demand capacity. Which solution resolves the hot key issue?

Accepted Answer

Add a random suffix to the partition key for the hot players to distribute writes. Option C is correct because adding a suffix to the partition key for hot keys distributes writes across multiple partitions. Option A (increase read capacity) doesn't help writes. Option B (DAX) is for reads. Option D (change to provisioned) doesn't solve hot key; adaptive capacity works in both modes.

Answer

Increase the maximum read capacity units in the on-demand settings

Answer

Switch to provisioned capacity mode and increase write capacity units

Answer

Enable DynamoDB Accelerator (DAX) to cache writes

Question 10

Refer to the exhibit. The exhibit shows CloudWatch metrics from an Amazon RDS for PostgreSQL instance. The application is experiencing slow query performance. Which is the most likely cause?

Accepted Answer

High storage latency due to provisioned IOPS being fully utilized or EBS volume contention. Option A is correct because ReadLatency of 2.5 ms is high for a database with 5000 provisioned IOPS; typically sub-millisecond is expected. WriteLatency of 5 ms is also high. Memory usage is 450 MB out of 16 GB, so memory is fine. Connections (100) are moderate. IOPS (5000 read, 2000 write) are within limits but latency is high, indicating potential throttling or contention. Option B: connections are not high. Option C: memory is not pressured. Option D: CPU is not mentioned; high latency is often storage-related.

Answer

High CPU utilization due to complex queries

Answer

Too many database connections causing context switching

Answer

Insufficient memory causing disk swaps

Question 11

Which TWO database services are suitable for storing time-series data with high ingestion rates? (Select TWO.)

Accepted Answer

Amazon Timestream. Amazon Timestream is purpose-built for time-series. DynamoDB with TTL can be used for time-series with efficient expiration. Options C, D, E are not optimal for high ingestion time-series.

Answer

Amazon RDS for MySQL

Answer

Amazon ElastiCache for Memcached

Answer

Amazon Redshift

Question 12

A gaming company wants to store player profiles and game state. The data is accessed via a REST API and must be highly available with single-digit millisecond latency. The schema is simple and may evolve over time. Which database should they use?

Accepted Answer

Amazon DynamoDB. Option A is correct because DynamoDB is a fully managed NoSQL database with consistent single-digit millisecond latency and flexible schema. Option B is wrong because RDS is relational and adds latency for simple lookups. Option C is wrong because Redshift is for analytics. Option D is wrong because S3 is object storage, not designed for low-latency updates.

Answer

Amazon S3

Answer

Amazon RDS for PostgreSQL

Answer

Amazon Redshift

Question 13

A company is running a MongoDB-compatible workload on Amazon DocumentDB. They are experiencing high write latency during peak hours. The current cluster has one instance (db.r5.large) with 100 GB storage. Which change is most likely to improve write performance?

Accepted Answer

Increase the instance size to db.r5.xlarge. DocumentDB writes are handled by the primary instance. Since the instance is r5.large, increasing instance size provides more CPU and memory, improving write throughput. Adding read replicas does not help writes. Scaling storage does not directly improve write latency. Enabling Multi-AZ adds a standby but does not improve write performance.

Answer

Increase storage to 500 GB

Answer

Enable Multi-AZ deployment

Answer

Add a read replica in a different Availability Zone

Question 14

A company runs a critical Oracle database on Amazon RDS. The database has a large table that is frequently accessed by multiple applications. The team wants to implement caching to reduce the load on the database. The cached data must be strongly consistent with the database. Which caching strategy should they use?

Accepted Answer

Write-through cache using Amazon ElastiCache. Write-through cache ensures that data is written to the cache and the database simultaneously, providing strong consistency. Lazy loading can lead to stale data. Read-only cache does not help with writes. Eventual consistency is not acceptable.

Answer

Eventual consistency with DynamoDB Accelerator (DAX)

Answer

Read-only cache with Amazon ElastiCache

Answer

Lazy loading with cache-aside pattern

Question 15

A company uses Amazon RDS for MySQL with Multi-AZ. During a recent failover test, the database experienced a 5-minute write outage. The application can tolerate up to 1 minute of downtime. Which solution should be used to reduce the failover time?

Accepted Answer

Migrate to Amazon Aurora MySQL.. Option D is correct because Amazon Aurora MySQL provides faster failover (typically under 30 seconds) due to its shared storage architecture. Option A is wrong because modifying the DNS TTL may help but does not reduce the failover time significantly. Option B is wrong because RDS Proxy helps with connection pooling, not failover speed. Option C is wrong because Multi-AZ already provides automatic failover, but failover time is typically 1-2 minutes for RDS.

Answer

Use Amazon RDS Proxy between the application and the database.

Answer

Enable automatic failover on the Multi-AZ deployment.

Answer

Decrease the DNS TTL on the RDS endpoint.