DBS-C01 Workload-Specific Database Design — Timed 15 Questions

Question 1

A company is designing a database for a global e-commerce application with millions of users. The workload requires single-digit millisecond read latency, high availability across multiple AWS Regions, and strong consistency. Which database service should the company use?

15 minute time limit — choose an answer to begin.

Accepted Answer

Amazon Aurora Global Database. Amazon Aurora Global Database is designed for global applications requiring high availability and low-latency reads across multiple AWS Regions. While it uses eventual consistency for reads from secondary regions, it provides strong consistency within the primary region, which is the only region where writes are accepted. For a global e-commerce application that demands strong consistency, the best practice is to route all writes to the primary region and use the primary endpoint for strongly consistent reads. Although cross-region reads are eventually consistent, Aurora Global Database offers the best balance of low latency, high availability, and strong consistency for the primary region, making it the most suitable option among the choices. In contrast, Amazon DynamoDB global tables provide only eventual consistency across regions, Amazon ElastiCache is a cache (not a primary database), and Amazon Neptune is a graph database not optimized for e-commerce workloads.

Answer

Amazon ElastiCache for Redis

Answer

Amazon Neptune

Answer

Amazon DynamoDB with global tables

Question 2

Which THREE of the following are best practices for designing a DynamoDB table for a gaming leaderboard that updates scores frequently and supports queries for top players? (Choose 3.)

Accepted Answer

Use strongly consistent reads for the leaderboard queries. Strongly consistent reads guarantee that the leaderboard query always returns the most up-to-date scores, which is critical for a competitive gaming leaderboard where accuracy is paramount. Option C is correct because a Global Secondary Index (GSI) with score as the sort key allows efficient range queries to retrieve top players without scanning the entire table. Option D is correct because DynamoDB Streams can be used to update a materialized leaderboard table, enabling efficient queries on pre-computed rankings. Options A and E are incorrect: scanning the entire table is inefficient and a single partition key creates a hot partition and prevents efficient sorted queries.

Answer

Scan the entire table and sort to get top scores

Answer

Use a single partition key for all players to easily query top scores

Question 3

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.. Amazon DocumentDB fully supports secondary indexes, similar to MongoDB. However, for reporting workloads that frequently query the same fields, covered queries (which retrieve all required data from the index without accessing the underlying documents) can provide significant performance benefits by reducing I/O and eliminating document lookups. This can lower index maintenance overhead and storage costs, making it a valuable design consideration when heavy reliance on secondary indexes is expected. The key is to design indexes that support covered queries for the most common reporting queries, rather than assuming secondary indexes alone will suffice.

Answer

Enable Multi-AZ deployment to improve read performance.

Answer

Shard the collection across multiple DocumentDB clusters.

Answer

Use the MongoDB aggregation pipeline to bypass indexing.

Question 4

A social media startup stores user posts in Amazon DynamoDB with a partition key of user_id and sort key of post_timestamp. The application frequently queries the five most recent posts for a given user. Which design pattern improves query performance and reduces cost?

Accepted Answer

Create a local secondary index on user_id and post_timestamp. Creating a local secondary index (LSI) on user_id (same partition key) and post_timestamp as the sort key allows you to run a Query operation on the index with a limit of 5 items, sorted in descending order, to efficiently retrieve the most recent posts for a user. This targeted read reduces consumed read capacity units compared to scanning the base table, lowering cost. LSI also supports strongly consistent reads if needed. Option A (DAX) adds cost without reducing RCU for infrequent queries. Option B (increasing RCU) increases cost without improving efficiency. Option C (GSI on post_timestamp) cannot efficiently scope to a single user, leading to full scans and higher cost.

Answer

Enable DynamoDB Accelerator (DAX) for the table

Answer

Increase the read capacity units on the table

Answer

Use a global secondary index on post_timestamp

Question 5

An IoT company ingests sensor data into Amazon DynamoDB. The data has a partition key of device_id and sort key of timestamp. Queries often filter by device_id and a date range. Which design pattern improves query performance and reduces cost?

Accepted Answer

Use DynamoDB Time to Live (TTL) to expire old data. DynamoDB Time to Live (TTL) automatically expires and deletes old sensor data. By removing obsolete items, the table size is reduced, leading to faster queries (less data to scan) and lower storage costs. Additionally, fewer read capacity units are consumed because queries process fewer items. This pattern both improves query performance and reduces cost for time-series data where queries focus on recent date ranges.

Answer

Create a local secondary index on device_id and timestamp

Answer

Enable DynamoDB Accelerator (DAX)

Answer

Create a global secondary index on timestamp

Question 6

Which TWO of the following are valid design considerations when using Amazon DynamoDB for a high-traffic e-commerce application? (Select TWO.)

Accepted Answer

Use atomic counters to maintain inventory counts. Atomic counters in DynamoDB allow you to increment or decrement a numeric attribute atomically using the UpdateItem operation with an UpdateExpression like 'SET inventory = inventory - :qty'. This is ideal for maintaining inventory counts in a high-traffic e-commerce application, as it avoids the need for read-modify-write cycles and reduces the risk of race conditions without consuming additional read capacity. Option E is also correct because sparse global secondary indexes (GSI) can reduce write capacity costs. If you create a GSI where the sort key or index key is not present on every item, only items that have the index key attribute are written to the index. This is especially useful when you need to query only a subset of items (e.g., only active items) and can reduce the write capacity consumed compared to a dense index where every item is written to the index.

Answer

Use scan operations for frequent queries to reduce provisioned capacity

Answer

Use DynamoDB transactions to reduce read capacity consumption

Answer

Always use strongly consistent reads for all queries

Question 7

A company runs a MySQL database on Amazon RDS for a financial application that requires point-in-time recovery (PITR) with a recovery window of at least 35 days. The database is 500 GB in size. Which TWO actions should be taken to meet these requirements?

Accepted Answer

Set the backup retention period to 35 days.. Amazon RDS for MySQL supports point-in-time recovery (PITR) only through automated backups, which capture transaction logs. To enable PITR, automated backups must be turned on (option E). The backup retention period controls how far back PITR can recover; setting it to 35 days (option B) ensures the required 35-day recovery window. Both actions are necessary: enabling automated backups and setting the retention period to 35 days. Option A (manual snapshots) does not support PITR. Option C (deletion protection) and option D (Multi-AZ) are unrelated to PITR or retention.

Answer

Create manual snapshots every day.

Answer

Enable deletion protection on the RDS instance.

Answer

Enable Multi-AZ deployment for high availability.

Question 8

A company is migrating a 2 TB Oracle database to Amazon Aurora MySQL. The database has custom PL/SQL packages and triggers. The migration must be completed with minimal application changes. Which THREE steps should the company take? (Choose three.)

Accepted Answer

Use AWS DMS to perform live migration with ongoing replication.. To migrate a 2 TB Oracle database to Amazon Aurora MySQL with minimal application changes, the company should use AWS DMS for live migration with ongoing replication to minimize downtime and handle continuous changes. AWS SCT automates the conversion of the schema and PL/SQL code to MySQL-compatible syntax, reducing manual effort. Thorough application testing against the target database ensures that the migration does not introduce issues. Option C (S3 dump transfer) is not suitable because it requires downtime and does not support ongoing replication. Option D (manual rewriting) is unnecessary as SCT handles most conversions automatically.

Answer

Use Amazon S3 to store and transfer database dump files.

Answer

Manually rewrite all PL/SQL packages to MySQL syntax.

Question 9

A company is moving a legacy on-premises Oracle database to AWS. The database has large tables with complex stored procedures and triggers. The company wants to minimize application changes. Which THREE AWS database services should the database specialist consider? (Choose THREE.)

Accepted Answer

Amazon RDS for Oracle. Amazon RDS for Oracle (Option C) provides native Oracle compatibility, enabling migration of legacy Oracle databases with minimal application changes by directly supporting Oracle stored procedures, triggers, and large tables. Amazon RDS Custom for Oracle (Option E) offers OS-level access and flexibility for legacy Oracle features that require custom patches or configuration. Amazon Aurora PostgreSQL with Babelfish (Option D) is designed for SQL Server migrations, not Oracle, and would require significant application changes to convert Oracle PL/SQL to PostgreSQL, making it unsuitable for minimizing changes. Therefore, the correct options are C and E.

Answer

Amazon Redshift

Answer

Amazon DynamoDB

Answer

Amazon Aurora PostgreSQL with Babelfish

Question 10

A company uses Amazon DynamoDB to store IoT sensor data. Each sensor sends data every minute. The table has a partition key of sensor_id and a sort key of timestamp. The application queries data for a sensor over the last hour. The table uses on-demand capacity. Recently, the query latency increased for sensors that generate a high volume of data. The application retrieves all attributes for the sensor data. Which design change should be made to reduce latency?

Accepted Answer

Use DynamoDB Accelerator (DAX) for the table.. The latency increase is due to high data volume per sensor, causing large reads from the base table. DynamoDB Accelerator (DAX) is an in-memory cache that reduces read latency by serving frequently accessed data directly from memory, avoiding expensive DynamoDB read operations. Option B (GSI with projection) would not help because the application retrieves all attributes, so any missing attributes in the GSI would require additional fetches from the base table, increasing latency. Option C adds complexity and Option D is irrelevant since on-demand capacity already scales.

Answer

Create a global secondary index (GSI) with the same key structure but projecting only the required attributes.

Answer

Enable DynamoDB Streams to replicate data to Amazon ElastiCache.

Answer

Increase the read capacity units (RCUs) on the table.

Question 11

A financial services company uses Amazon RDS for MySQL with Multi-AZ and read replicas. The application suddenly reports increased write latency. The DB instance is a db.r5.large with 300 GB of General Purpose SSD (gp2) storage. CloudWatch metrics show Write IOPS is at 2,500 and Write Latency averages 50 ms. What is the most likely cause?

Accepted Answer

The gp2 volume has exhausted its I/O credit balance.. The gp2 volume's I/O credit balance is exhausted, causing the volume to fall back to its baseline IOPS of 900 for a 300 GB volume (300 GB * 3 IOPS/GB = 900 baseline IOPS). Since the Write IOPS is 2,500, which exceeds the baseline, the volume must use burst credits; once credits are depleted, latency spikes as seen here. This is the classic symptom of a gp2 volume running out of burst credits, leading to increased write latency. Note that the maximum burst IOPS for gp2 volumes up to 1 TB is 3,000, but the baseline is lower.

Answer

The read replica is under-provisioned causing replication lag.

Answer

The Write IOPS has exceeded the gp2 volume's maximum burst IOPS of 3,000.

Answer

Multi-AZ synchronous replication is causing high latency.

Question 12

A company is migrating an on-premises Oracle database with complex stored procedures and triggers to AWS. They want to minimize code changes. Which two AWS database services should they consider? (Choose two.)

Accepted Answer

Amazon RDS for Oracle. Amazon RDS for Oracle is the most direct migration target because it uses the same Oracle Database engine, allowing existing stored procedures, triggers, and PL/SQL code to run with minimal or no modification. Amazon Aurora PostgreSQL with Babelfish is not suitable for Oracle migrations because Babelfish provides compatibility with SQL Server T-SQL, not Oracle PL/SQL. Therefore, only RDS for Oracle minimizes code changes for Oracle stored procedures.

Answer

Amazon DynamoDB

Answer

Amazon S3

Answer

Amazon Redshift

Answer

Amazon Aurora PostgreSQL with Babelfish

Question 13

A company runs an e-commerce platform on Amazon Aurora MySQL. The platform experiences high traffic during flash sales. To handle the load, they want to offload read traffic to read replicas. However, they are concerned about replication lag causing stale data for inventory checks. Which three strategies can reduce replication lag? (Choose three.)

Accepted Answer

Enable binary log replication with parallel apply on replicas.. Options A, B, and E are effective strategies to reduce replication lag in Amazon Aurora MySQL. Enabling binary log parallel apply on replicas (A) allows concurrent application of transactions, reducing lag. Using a lower isolation level like READ COMMITTED (B) reduces locking contention on replicas, improving apply performance. Increasing the writer instance size (E) helps if the writer is resource-constrained, allowing faster binlog generation and reducing replication delay. Option D (adding read replicas) does not reduce replication lag; in fact, adding more replicas can increase the replication load and potentially increase lag. Option C (synchronous replication) is not available in Aurora; replication is asynchronous.

Answer

Implement synchronous replication between writer and replicas.

Answer

Use Aurora Auto Scaling to add read replicas during peak traffic.

Question 14

A company uses Amazon Aurora MySQL for an OLTP application. The database has a buffer pool cache hit ratio of 99%. However, the application still experiences high latency for some queries. Which of the following is the most likely cause? (Choose two.)

Accepted Answer

The queries are performing full table scans. Even with a high cache hit ratio, queries that scan large amounts of data (e.g., full table scans) can cause high latency due to I/O and CPU. Option D: Row lock contention can cause queries to wait, increasing latency. Option B: A low cache hit ratio would be an issue, but it's high. Option C: Insufficient write capacity is not a problem with Aurora. Option E: Replica lag affects reads from replicas, not primary.

Answer

The buffer pool cache hit ratio is actually low

Answer

The database instance type is too small for the write workload

Answer

The read replicas are lagging behind the primary

Question 15

A company is designing a database for an IoT application that ingests millions of sensor readings per second. The data is time-series and is queried to generate reports on average temperature over the last hour. Which TWO database solutions are most suitable for this workload?

Accepted Answer

Amazon DynamoDB with time-series data model and TTL. Amazon DynamoDB with a time-series data model and TTL is suitable because it can handle high-throughput writes from millions of sensors per second using partition key design (e.g., sensor ID + timestamp) and automatically expire old data via TTL, reducing storage costs. The query pattern for average temperature over the last hour can be efficiently served using DynamoDB's built-in time-series optimizations, such as adaptive capacity and on-demand scaling, without the overhead of relational joins. Amazon Timestream is also suitable because it is a purpose-built time-series database that can ingest millions of sensor readings per second and provides built-in functions for time-series aggregation, such as average temperature over the last hour, with automatic data retention policies and tiered storage for cost optimization.

Answer

Amazon Neptune

Answer

Amazon RDS for PostgreSQL

Answer

Amazon ElastiCache for Redis