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

Question 1

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.. The query uses the index's partition key 'status' but does not specify a sort key condition. The index's sort key is 'created_at'. The query should return items with status 'PENDING' sorted by 'created_at'. However, the response shows 0 items but consumed 0.5 capacity units, indicating the index exists but no items matched. This could happen if the index is not yet backfilled (if recently created) or if the index's partition key attribute is missing in the items. In DynamoDB, GSI items are only written if the attribute exists. If items have a 'status' attribute set to 'PENDING', they should appear. The most likely cause is that the index is not fully populated yet (if recently created) or the 'status' attribute is missing in the items. The query syntax looks correct. Option A (incorrect expression) is unlikely. Option B (missing sort key) is not required for query on partition key only. Option C (index not yet backfilled) is plausible. Option D (using table name instead of index name) is incorrect because the query specifies --index-name. So C is correct.

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 2

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 is to use Amazon RDS Proxy with a connection pool per tenant, but that does not provide isolation. The most effective approach is to migrate to Amazon Aurora, which can handle many connections and provides better performance. However, the key is to use a separate database per tenant (database-per-tenant model) with a pool of RDS instances. The most AWS-native approach is to use Amazon RDS for PostgreSQL with pg_partman or use a separate RDS instance per tenant, but that is costly. The best solution is to use Aurora Serverless v2, which can scale to zero and provides isolation through separate Aurora clusters? But that may require many clusters. The optimal solution is to use Amazon RDS with a separate database per tenant and use a connection pooling service like RDS Proxy to manage connections. The application changes are minimal because the ORM can be configured to use a different connection string per tenant. However, the question asks for the BEST solution. Option A: Use RDS Proxy with a single database but with connection pooling; does not isolate. Option B: Migrate to Aurora and use Aurora Auto Scaling; still shared. Option C: Implement a database-per-tenant model with separate RDS instances and use RDS Proxy for each; provides isolation but complex. Option D: Use Amazon DynamoDB with tenant ID as partition key; provides isolation and scaling but requires application changes to use DynamoDB instead of PostgreSQL. The stem says 'minimize application changes', so moving from PostgreSQL to DynamoDB would require significant changes. Therefore, the best is to use a database-per-tenant approach with RDS and RDS Proxy. But among the options, likely one suggests using Aurora with separate databases per tenant. I'll craft the options accordingly.

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 3

A company runs an online gaming platform that uses Amazon DynamoDB to store player profiles and game state. The platform experiences sudden spikes in write traffic when popular events occur. During these spikes, some write requests fail with ProvisionedThroughputExceededException. The operations team has configured DynamoDB Auto Scaling with a maximum write capacity of 10000 WCU, but during spikes, the write rate exceeds 10000 WCU for short bursts. The team needs to handle these bursts without losing data or sacrificing performance. What is the MOST effective solution?

Accepted Answer

Implement an Amazon SQS queue to buffer write requests and process them in batches, with a Lambda function writing to DynamoDB.. DynamoDB Auto Scaling can increase capacity, but it reacts to CloudWatch alarms and takes time (minutes). For sudden spikes, the best solution is to use DynamoDB Accelerator (DAX) for reads, not writes. For writes, you can implement exponential backoff and retries in the application, but that may increase latency. Another option is to use a queue (Amazon SQS) to buffer writes and process them at a controlled rate. This decouples the application from the database, allowing spikes to be absorbed. Option A (increase max WCU) helps but may not be enough if spikes are very high and unpredictable. Option B (DAX) does not help with writes. Option C (SQS buffer) is a common pattern. Option D (sharding) requires application changes and may not be necessary. So the best is to use SQS to buffer writes.

Answer

Enable DynamoDB Accelerator (DAX) to cache write requests and reduce the load on the table.

Answer

Shard the DynamoDB table manually by adding a random suffix to the partition key to distribute writes more evenly.

Answer

Increase the maximum write capacity for Auto Scaling to a higher value, such as 20000 WCU.

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. Option C is correct because Amazon Timestream is a time-series database optimized for IoT data with fast ingestion and low-latency aggregations. Option A (Aurora) is relational and not optimized for high-velocity time-series. Option B (DynamoDB) is key-value, not designed for time-series aggregations efficiently. Option D (ElastiCache) is an in-memory cache, not designed for persistent time-series storage.

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.. Option B is correct because using a secondary index with a different partition key can avoid hot partitions caused by frequent access to the same account. Option A (increasing RCUs) may not help if the partition is hot. Option C (changing sort key) won't help if the partition is overloaded. Option D (DAX) caches results but doesn't fix the underlying hot partition issue.

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. Option C is correct because Amazon DynamoDB offers flexible schema, high throughput, and optional strong consistency. Option A (RDS MySQL) is relational with fixed schema. Option B (Neptune) is graph-dedicated. Option D (DocumentDB) is MongoDB-compatible but not as optimized for consistent low-latency reads across a wide range of access patterns.

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. Option A is correct because Amazon Aurora provides ACID transactions, complex join support, and up to 15 cross-region read replicas. Option B (DynamoDB) is not ACID across multiple items. Option C (Redshift) is a data warehouse, not transactional. Option D (RDS SQL Server) supports ACID but cross-region replicas are limited and more complex.

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.. Option D is correct because the partition key 'game_id' causes a hot partition for game 'g123'. Using a write shard pattern distributes writes across multiple partitions. Option A (increase WCUs) may not help if the partition is hot. Option B (DAX) is for reads, not writes. Option C (auto-scaling) won't solve hot partition issue.

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. Option B is correct because Amazon CloudWatch Logs is a managed service for log storage and analysis. Option A (RDS) is not optimized for log data. Option C (DynamoDB) is not designed for log aggregation queries. Option D (ElastiCache) is an in-memory cache, not persistent log storage.

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. Option B (Neptune) is correct for graph queries. Option D (Global Database for Aurora) provides multi-region replication for relational data, but for graph queries Neptune is primary. Since Neptune does not natively support multi-region replication, the platform might use Aurora for metadata and Neptune for graph, but the question expects two services. Alternatively, DynamoDB Global Tables for key-value and Neptune for graph. However, given options, B and D are the best combined for graph and replication. Option A (DynamoDB) is not graph. Option C (Redshift) is analytics. Option E (ElastiCache) is caching.

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.. Option A (DynamoDB Streams + Lambda) enables real-time updates. Option D (DAX) provides low-latency read caching for the leaderboard. Option B (GSI) could help but not for real-time top-N. Option C (ElastiCache) is an alternative but not DynamoDB-native. Option E (Sort key on score) is a good design but not a separate pattern; it's part of table design.

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.. Option A (AWS DMS with ongoing replication) minimizes downtime. Option B (Schema Conversion Tool) helps convert Oracle schema to PostgreSQL. Option D (data validation) ensures consistency. Option C (homogeneous migration) is not needed; DMS works. Option E (Aurora Replicas) is for read scaling, not 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.. Option D is correct because partitioning the table by 'status' and 'order_date' can significantly reduce the amount of data scanned, as queries often filter on these columns. In addition, using a covering index can avoid lookups. Option A (vertical scaling) provides temporary relief but does not address the root cause. Option B (read replicas) offloads read traffic but does not fix the slow queries themselves. Option C (cache layer) can help with repeated queries but not with ad-hoc analytical queries that still scan large datasets.

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.