DBS-C01 Workload-Specific Database Design Practice Test 20 — 15 Questions

Question 1

A company has a multi-player game that uses DynamoDB to store game state. The access pattern is write-heavy, and the game state for each active game session is updated frequently. The team notices throttling on the table during peak hours. The table has a partition key of game_id and no sort key. What design change would best reduce throttling?

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

Accepted Answer

Use a composite key with a random suffix on the partition key.. The write-heavy access pattern with frequent updates to the same game sessions causes throttling because all writes for a given game_id hit the same partition, creating a hot partition. Using a composite key with a random suffix on the partition key distributes the writes across multiple partitions, reducing the per-partition write throughput and alleviating throttling.

Answer

Enable DynamoDB global tables.

Answer

Enable DynamoDB Accelerator (DAX) for the table.

Answer

Increase the provisioned read capacity units (RCU).

Question 2

A company is migrating an on-premises Oracle data warehouse to AWS. The warehouse contains 50 TB of data and runs complex queries that involve joins and aggregations. The team wants to minimize migration effort and cost while maintaining query performance. Which AWS service should they use?

Accepted Answer

Amazon Redshift. Amazon Redshift is purpose-built for large-scale data warehousing, supporting up to petabytes of data with massively parallel processing (MPP) architecture that efficiently handles complex joins and aggregations. It minimizes migration effort by supporting automated schema conversion from Oracle via the AWS Schema Conversion Tool (SCT) and cost-effective columnar storage with compression, making it the optimal choice for a 50 TB Oracle data warehouse migration.

Answer

Amazon RDS for Oracle

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Amazon ElastiCache for Redis

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Amazon DynamoDB

Question 3

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). Amazon DocumentDB (with MongoDB compatibility) is the most appropriate service because it is purpose-built for storing and querying JSON documents with varying attributes, and it supports SQL-like queries via MongoDB's query language, which is familiar to developers. As a read-heavy workload, DocumentDB's architecture with distributed storage and read replicas provides high throughput and low latency for such patterns. The flexible schema of DocumentDB naturally handles documents with differing attributes without requiring schema migrations.

Answer

Amazon Neptune

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Amazon DynamoDB with PartiQL

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Amazon RDS for MySQL with JSON data type

Question 4

Which TWO factors should you consider when choosing between Amazon RDS and Amazon DynamoDB for a new application?

Accepted Answer

RDS requires a predefined schema, while DynamoDB is schema-less.. Amazon RDS (relational database service) requires a predefined schema with tables, columns, and data types before data can be inserted, enforcing ACID compliance and referential integrity. In contrast, Amazon DynamoDB is a NoSQL key-value and document database that is schema-less, allowing you to store items with varying attributes without upfront schema definition, which is ideal for agile development and unstructured data.

Answer

DynamoDB can only be accessed from within a VPC, while RDS can be public.

Answer

Only RDS supports Multi-AZ deployments for high availability.

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Both services support encryption at rest and in transit.

Question 5

Which THREE design patterns are commonly used to optimize DynamoDB performance for write-heavy workloads?

Accepted Answer

Using DynamoDB adaptive capacity to handle unbalanced access patterns.. DynamoDB adaptive capacity automatically isolates heavily accessed partitions, allowing them to consume more throughput without throttling other partitions. This is critical for write-heavy workloads with uneven access patterns, as it prevents hot partitions from degrading overall performance.

Answer

Using sparse indexes on rarely accessed attributes.

Answer

Using global tables to distribute writes across regions.

Question 6

Which TWO of the following are advantages of using Amazon Aurora over standard RDS for MySQL?

Accepted Answer

Aurora can deliver up to 5x the throughput of standard MySQL on the same hardware.. Amazon Aurora uses a distributed, SSD-backed storage subsystem that separates compute from storage, enabling it to deliver up to 5x the throughput of standard MySQL running on the same hardware. This performance gain comes from the Aurora storage engine's ability to reduce I/O operations and parallelize writes across multiple storage nodes.

Answer

Aurora automatically fails over to a read replica in case of primary failure.

Answer

Aurora is compatible with PostgreSQL, so you can migrate from SQL Server easily.

Answer

Aurora supports up to 15 read replicas, while RDS for MySQL only supports 5.

Question 7

A company is designing a database for an e-commerce platform that stores product catalog data. The catalog has frequent reads and occasional writes. The data is highly relational but the team wants the lowest possible latency for read queries. Which database service should they choose?

Accepted Answer

Amazon DynamoDB with DAX. Amazon DynamoDB with DAX is the best choice because the workload requires the lowest possible latency for read queries on a highly relational product catalog with frequent reads and occasional writes. DynamoDB provides single-digit millisecond latency at scale, and DAX (DynamoDB Accelerator) is an in-memory cache that reduces read latency to microseconds for eventually consistent reads, directly addressing the low-latency requirement without sacrificing the ability to handle occasional writes.

Answer

Amazon DocumentDB

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Amazon RDS for PostgreSQL with read replicas

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Amazon ElastiCache for Redis

Question 8

A financial services company runs a core banking application on Amazon RDS for MySQL. They need to maintain a full audit trail of all changes to customer account balances for regulatory compliance. The audit trail must be immutable and queryable for up to 7 years. Which solution meets these requirements with minimal impact on the source database?

Accepted Answer

Migrate to Amazon Aurora MySQL and enable database activity streams, streaming to Amazon Kinesis Data Streams and then to Amazon S3. Amazon Aurora MySQL Database Activity Streams provide a near-real-time, immutable audit trail of database activities at the transaction level, which can be streamed to Amazon Kinesis Data Streams and then stored in Amazon S3 for long-term queryable retention. This solution meets the 7-year compliance requirement with minimal impact on the source database because it offloads the audit processing to external services and does not require changes to the application or additional logging overhead on the database instance.

Answer

Use Amazon RDS Performance Insights to capture SQL queries and store them in Amazon S3

Answer

Use Amazon DynamoDB Streams with a Lambda function to capture changes from the MySQL database

Answer

Enable binary logging on the RDS instance and stream the binlog to Amazon S3 using AWS Database Migration Service (DMS)

Question 9

A startup is building a social media application. User profiles, posts, and comments have relationships but the team expects rapid growth and wants to scale horizontally with no single points of failure. They need a database that supports flexible schemas for different content types. Which database service is most appropriate?

Accepted Answer

Amazon DynamoDB. Amazon DynamoDB is the most appropriate choice because it is a fully managed NoSQL key-value and document database that supports flexible schemas, enabling the application to handle user profiles, posts, and comments with varying attributes. It scales horizontally by automatically partitioning data across multiple nodes, and its multi-AZ replication eliminates single points of failure, meeting the startup's requirements for rapid growth and high availability.

Answer

Amazon Neptune

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Amazon Redshift

Answer

Amazon RDS for MySQL

Question 10

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. Increasing the instance size to db.r5.xlarge provides more CPU and memory resources, which directly improves the cluster's ability to handle write operations under load. In Amazon DocumentDB, write performance is primarily bound by the instance's compute capacity (vCPUs and memory) for processing write requests and managing the storage engine's buffer cache. A larger instance reduces contention and allows more concurrent writes to be processed efficiently.

Answer

Increase storage to 500 GB

Answer

Enable Multi-AZ deployment

Answer

Add a read replica in a different Availability Zone

Question 11

A company uses Amazon RDS for PostgreSQL to store sensor data. Each sensor sends a row every second. The table has grown to 500 GB and queries filtering on a timestamp column are slow even with an index. The team wants to improve query performance while keeping the data online. Which approach should they take?

Accepted Answer

Partition the table by time using PostgreSQL table partitioning. PostgreSQL table partitioning by time (e.g., by day or month) allows the query planner to prune partitions that do not match the timestamp filter, drastically reducing the amount of data scanned. This is the most effective approach for time-series data because it maintains online access and improves query performance without requiring a migration or additional replicas.

Answer

Migrate to Amazon Aurora PostgreSQL and enable parallel query

Answer

Add more indexes on the timestamp column

Answer

Create a read replica and direct queries to the replica

Question 12

A mobile gaming company needs a database to store player scores and leaderboards. The data must be updated in real time as players finish games. The database must support high write throughput and provide sub-millisecond read latency for leaderboard queries. Which database is best suited?

Accepted Answer

Amazon DynamoDB. Amazon DynamoDB is the best choice because it is a fully managed NoSQL key-value and document database designed for single-digit millisecond read and write performance at any scale. Its DAX (DynamoDB Accelerator) caching layer can further reduce read latency to sub-millisecond for leaderboard queries, while its auto-scaling write capacity handles the high write throughput required for real-time player score updates.

Answer

Amazon RDS for MySQL with read replicas

Answer

Amazon Redshift

Answer

Amazon ElastiCache for Redis

Question 13

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 Aurora with Multi-AZ provides faster failover than RDS for Oracle because Aurora uses a shared storage architecture and a cluster endpoint that automatically redirects traffic to the replica within 30 seconds, often in as little as 15 seconds. In contrast, RDS for Oracle Multi-AZ relies on DNS record updates and a standby instance that must be promoted, which typically takes 60–120 seconds. Migrating to Aurora eliminates the DNS propagation delay and the need for storage failover, meeting the sub-30-second requirement.

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 14

A company runs a web application on Amazon EC2 instances behind an Application Load Balancer. The application uses Amazon RDS for MySQL. Recently, the database CPU utilization spikes to 100% during peak hours. The team observes that the spike is caused by a large number of slow queries. They need to identify and resolve the issue with minimal disruption. Which combination of steps should they take? (Choose two.)

Accepted Answer

Enable RDS Performance Insights to identify the slow queries. The correct combination is A and D. Option A: Enabling RDS Performance Insights quickly identifies slow queries and their resource consumption, pinpointing the cause of CPU spikes. Option D: Creating a read replica and directing reporting queries to it offloads read traffic from the primary instance, reducing CPU load during peak hours. Option B is not an identification step and may not address the root cause, while also incurring unnecessary cost. Option C addresses I/O bottlenecks, not CPU spikes from slow queries. Option E helps with connection management but does not identify or directly resolve the slow query issue.

Answer

Upgrade to a larger instance type

Answer

Increase the DB instance storage to improve I/O

Answer

Implement connection pooling using Amazon RDS Proxy

Question 15

A company is migrating an on-premises MySQL database to Amazon Aurora MySQL. The database is 2 TB and the migration must have minimal downtime. The network bandwidth between the on-premises data center and AWS is 1 Gbps. Which migration approach is most appropriate?

Accepted Answer

Use AWS Database Migration Service (DMS) with ongoing replication to keep the target in sync. AWS DMS with ongoing replication is the most appropriate approach because it allows a full load of the 2 TB database followed by continuous change data capture (CDC) to keep the target Aurora MySQL cluster in sync with minimal downtime. At 1 Gbps, the initial load would take approximately 5–6 hours, but the CDC phase reduces the final cutover downtime to seconds or minutes, meeting the minimal downtime requirement.

Answer

Use mysqldump to export the data, upload to Amazon S3, and import into Aurora

Answer

Take a snapshot of the on-premises database and restore it to Aurora

Answer

Use AWS Snowball Edge to transfer the data physically to AWS