DBS-C01 Workload-Specific Database Design Practice Test 2 — 15 Questions

Question 1

A company is migrating an on-premises MongoDB database to Amazon DocumentDB. The database stores IoT sensor data with time-series characteristics. The application performs range queries on timestamp fields and updates recent documents frequently. Which THREE aspects of DocumentDB should the company consider to optimize performance for this workload? (Choose three.)

Accepted Answer

Implement sharding to distribute write load across multiple instances.. Option B is correct because DocumentDB does not support native sharding like MongoDB; however, for workloads with high write throughput, you can distribute writes by using multiple DocumentDB instances and routing writes based on a shard key in the application layer. This helps avoid write bottlenecks on a single instance and scales write capacity horizontally.

Answer

Use global secondary indexes to speed up queries on the timestamp field.

Answer

Use a single large instance class to avoid sharding complexity.

Question 2

A gaming company uses Amazon DynamoDB as the primary database for their player sessions. The player sessions table has a partition key of 'player_id' and a sort key of 'session_start_time'. The application frequently queries for recent sessions of a specific player, using the query API with 'player_id' and a filter on 'session_start_time' for the last 24 hours. The average item size is 5 KB. The company notices high latency on these queries during peak hours. The table has 10 Read Capacity Units (RCUs) provisioned. There are no indexes. Which design change would MOST improve query performance?

Accepted Answer

Create a Local Secondary Index (LSI) with the same partition key and a sort key of 'session_start_time', and query the index instead of the table.. Option C is correct because creating a Local Secondary Index (LSI) with the same partition key (player_id) and sort key (session_start_time) allows DynamoDB to efficiently retrieve items for a specific player sorted by session_start_time without scanning and filtering. The current query uses a filter on session_start_time after retrieving all sessions for the player, which wastes read capacity and increases latency. Querying the LSI directly uses the sort key to limit the data read to only the last 24 hours, reducing the read footprint and improving performance.

Answer

Increase the RCUs to 100.

Answer

Add a random suffix to the partition key values to distribute writes across partitions.

Answer

Create a Global Secondary Index (GSI) with partition key 'event_type' and sort key 'session_start_time' and query the GSI.

Question 3

A financial services company uses Amazon RDS for MySQL to store transaction data. The database has a single table 'transactions' with 500 million rows. The table has an auto-increment primary key and an index on 'transaction_date'. The company runs a monthly report that aggregates transactions by account_id and transaction_date. The report query uses a GROUP BY on account_id and transaction_date, and scans the entire table. The query takes over 2 hours to complete and often times out. The DBA suggests creating a materialized view. However, the company wants to minimize operational overhead. Which solution meets the requirements with the LEAST operational overhead?

Accepted Answer

Migrate the reporting workload to Amazon Redshift by loading the transactions table into Redshift and running the report query there.. Option B is correct because Amazon Redshift is purpose-built for large-scale analytical queries. By migrating the reporting workload to Redshift, the company offloads the heavy aggregation from the transactional RDS instance to a columnar storage engine that can scan and aggregate 500 million rows efficiently using massively parallel processing (MPP). This approach requires no changes to the existing RDS database and minimizes operational overhead compared to managing a materialized view or manual indexing.

Answer

Increase the RDS instance size to the largest available to improve performance.

Answer

Create a materialized view in MySQL that pre-aggregates the data and refreshes it nightly.

Answer

Add a composite index on (account_id, transaction_date) to speed up the GROUP BY.

Question 4

A company is designing a database for an e-commerce application that needs to store product catalog data. The data is highly structured with relationships between products, categories, and suppliers. The application requires ACID transactions to maintain data integrity when updating inventory. The company expects moderate read and write traffic. Which AWS database service is MOST suitable for this workload?

Accepted Answer

Amazon RDS for MySQL. Amazon RDS for MySQL is the most suitable choice because it provides a fully managed relational database that supports ACID transactions, which are essential for maintaining data integrity during inventory updates. The highly structured nature of product catalog data with relationships between products, categories, and suppliers maps naturally to MySQL's relational model with foreign keys and joins. RDS for MySQL handles moderate read/write traffic efficiently and offers features like Multi-AZ for high availability and read replicas for scaling reads.

Answer

Amazon ElastiCache for Redis

Answer

Amazon Neptune

Answer

Amazon DynamoDB

Question 5

A social media company uses Amazon DynamoDB to store user posts. The table has a partition key of 'user_id' and a sort key of 'post_timestamp'. Each item is about 10 KB. The application needs to retrieve all posts for a given user within a date range. The company recently added a new feature that allows users to 'like' posts, and they store the like count as an attribute in the post item. The like count is updated frequently. The application experiences high write throttling on the table. The table has 1000 WCUs provisioned. The write pattern is bursty. Which design change would MOST effectively reduce write throttling?

Accepted Answer

Add a random suffix to the user_id partition key to distribute writes across multiple partitions.. Option C is correct because the write throttling is caused by a 'hot partition' — all writes for a given user_id go to the same partition, and the bursty write pattern (e.g., many likes on a single post) exceeds the partition's 1,000 WCU limit (1,000 write capacity units per partition). Adding a random suffix to the user_id partition key distributes writes across multiple partitions, effectively increasing the write throughput for that logical user's data. This is a common design pattern for DynamoDB to handle high-traffic items without increasing provisioned capacity.

Answer

Increase the WCUs to 5000.

Answer

Enable DynamoDB Accelerator (DAX) to cache writes.

Answer

Create a Global Secondary Index (GSI) on the like count attribute.

Question 6

A company is designing a database for an IoT application that ingests sensor data from thousands of devices. Each device sends a reading every minute. The data includes device_id, timestamp, temperature, humidity, and pressure. The application needs to store this data and support queries that retrieve all readings for a specific device within a time range. The company expects high write throughput and moderate read frequency. The data must be stored with high durability. Which TWO database designs are appropriate for this workload? (Choose TWO.)

Accepted Answer

Use Amazon DynamoDB with device_id as partition key and timestamp as sort key.. Option C is correct because DynamoDB's partition key (device_id) and sort key (timestamp) design allows efficient retrieval of all readings for a specific device within a time range using a Query operation with a KeyConditionExpression on the sort key. This schema supports high write throughput by distributing writes across partitions based on device_id, and DynamoDB's multi-AZ replication provides high durability.

Answer

Use Amazon DynamoDB with device_id as partition key and store all readings for a device as a list attribute in a single item, updating the list every minute.

Answer

Use Amazon S3 to store compressed JSON files per device per hour, and query using Amazon Athena.

Answer

Use Amazon RDS for MySQL with a single table and index on device_id and timestamp.

Question 7

A company runs a customer relationship management (CRM) application on Amazon RDS for PostgreSQL. The application has a table 'customers' with columns: id (primary key), name, email, signup_date, and status. The table has 10 million rows. The application frequently queries by email address. The company also needs to run analytical queries that aggregate customers by signup_date and status. The DBA notices that the index on email is heavily used and causes high write latency. The company wants to improve write performance without sacrificing read performance for email queries. Which THREE actions should the database specialist take? (Choose THREE.)

Accepted Answer

Create a BRIN index on signup_date.. Option A is correct because a BRIN (Block Range INdex) index on signup_date is ideal for large tables where data is naturally ordered by insertion time. BRIN indexes are much smaller and have lower write overhead than B-tree indexes, making them suitable for analytical queries on signup_date without significantly impacting write performance.

Answer

Remove the index on email to reduce write latency.

Answer

Create a GIN index on email.

Question 8

A company runs a multi-tenant SaaS application on Amazon DynamoDB. Each tenant's data is stored in a separate table named with a tenant-specific prefix (e.g., tenant1_orders, tenant2_orders). The application uses DynamoDB Streams to replicate data to a central analytics table. Recently, the company added a new large tenant that generates 10x more write traffic than any other tenant. The DynamoDB Streams for the large tenant's table is falling behind by several hours, causing stale data in the analytics table. The company has already increased the write capacity of the large tenant's table to 50,000 WCUs, but the streams lag persists. The analytics table is also in DynamoDB and uses a Global Secondary Index (GSI) for querying. The streams processing Lambda function performs simple transformations and writes to the analytics table. The Lambda function is not throttled. Which action would resolve the streams lag?

Accepted Answer

Enable DynamoDB on-demand mode for the large tenant's table to allow automatic scaling of stream shards.. Option A is correct because DynamoDB Streams shards are directly tied to the physical partitions of the table. When a table is in provisioned mode, the number of stream shards is fixed and determined by the table's partitions, which cannot scale independently. Enabling on-demand mode allows DynamoDB to automatically split partitions and thus increase the number of stream shards, enabling higher stream throughput to keep up with the large tenant's write volume. This directly addresses the root cause of the streams lag without requiring manual partition management.

Answer

Remove the GSI from the analytics table to reduce write amplification.

Answer

Increase the Lambda function's reserved concurrency to the maximum.

Answer

Increase the write capacity of the large tenant's table to 100,000 WCUs.

Question 9

A logistics company uses Amazon RDS for MySQL to track package shipments. The 'shipments' table contains 200 million rows and has a primary key on 'shipment_id' (UUID). The application frequently queries for shipments by 'tracking_number', which is a unique string of 20 characters. The DBA created a B-tree index on tracking_number. The queries by tracking_number are fast, but inserts are becoming slower over time. The table has 50 GB of data. The company plans to double the insert rate next month. The database is a db.r5.large instance with 500 GB of Provisioned IOPS SSD storage. The instance's CPU utilization is below 30%, and there is no lock contention. What should the database specialist do to improve insert performance?

Accepted Answer

Change the primary key from UUID to an auto-increment integer, and keep the tracking_number index.. Option C is correct because UUID primary keys cause random writes and index fragmentation, degrading insert performance as the table grows. Switching to an auto-increment integer primary key allows sequential writes to the clustered index, reducing page splits and improving insert throughput. The B-tree index on tracking_number remains to support fast queries, while the new primary key eliminates the UUID write overhead.

Answer

Add a read replica and route insert queries to the replica.

Answer

Drop the index on tracking_number to reduce write overhead.

Answer

Increase the provisioned IOPS to 20,000.

Question 10

A social media analytics company uses Amazon DynamoDB as the primary data store for user session data. Each session record has a partition key of user_id (String) and a sort key of session_start_time (Number, epoch). The application often queries the most recent 10 sessions for a given user. The traffic pattern shows that 90% of reads are for the last 10 sessions, while 10% are for historical sessions. The table has a provisioned read capacity of 5000 RCU and consistently experiences throttled read requests during peak hours. The company wants to optimize read performance without changing the provisioned capacity. Which design change will MOST improve read performance for this workload?

Accepted Answer

Create a Global Secondary Index (GSI) with the same partition key and a sort key of session_start_time, but query with ScanIndexForward=false and Limit=10.. Option A is correct because creating a GSI with the same partition key (user_id) and sort key (session_start_time) allows you to query with ScanIndexForward=false and Limit=10 to efficiently retrieve only the most recent 10 sessions per user. This avoids scanning all sessions for a user, reducing consumed read capacity and eliminating throttling without increasing provisioned RCU. The GSI also supports the 90% workload pattern by providing a targeted index that minimizes read unit consumption.

Answer

Increase the provisioned read capacity to 10000 RCU to handle the peak load.

Answer

Enable DynamoDB Accelerator (DAX) with default settings to cache the most recent sessions.

Answer

Configure Amazon ElastiCache for Redis as a read-through cache for session data.

Question 11

A gaming company uses Amazon RDS for PostgreSQL to store player profiles and game state data. The database is currently 500 GB and grows by 10 GB per day. The company runs weekly reports that scan the entire database, causing high I/O and CPU usage. The application experiences read latency spikes during report generation. The team wants to minimize performance impact on the application while maintaining the ability to run reports. Which solution should the team implement?

Accepted Answer

Create a read replica and direct all report queries to the read replica.. Creating a read replica for Amazon RDS for PostgreSQL allows the team to offload all report queries to a separate read-only endpoint, eliminating the I/O and CPU contention on the primary database. This directly addresses the read latency spikes during report generation without requiring any application changes beyond redirecting the reporting queries. The read replica asynchronously replicates data from the primary instance, ensuring the reports see a near-real-time snapshot of the data while the primary remains dedicated to the application workload.

Answer

Enable Multi-AZ deployment to provide a standby instance for failover and use it for reporting.

Answer

Scale up the RDS instance to a larger instance type to handle the additional load from reports.

Answer

Archive historical game state data to Amazon S3 and delete it from the database to reduce size.

Question 12

A financial services company uses Amazon DynamoDB to store transaction records. Each transaction has a unique transaction_id as the partition key and a timestamp as the sort key. The application frequently queries all transactions for a given customer within a date range. However, customer_id is not an attribute indexed for querying. The company wants to optimize these queries without redesigning the entire table schema. Which action should the company take?

Accepted Answer

Create a Global Secondary Index (GSI) with customer_id as the partition key and timestamp as the sort key.. Option C is correct because creating a Global Secondary Index (GSI) with customer_id as the partition key and timestamp as the sort key allows efficient querying of all transactions for a given customer within a date range without redesigning the base table. The GSI provides a new access pattern with its own partition and sort keys, enabling the Query operation on customer_id and timestamp, which is far more efficient than a Scan. This approach preserves the existing table schema and supports the required query pattern with minimal overhead.

Answer

Change the table's partition key to customer_id and use a composite sort key.

Answer

Create a Local Secondary Index (LSI) on customer_id.

Answer

Use the Scan operation with a filter expression for customer_id and timestamp.

Question 13

Arrange the steps to migrate an on-premises Oracle database to Amazon RDS for Oracle using AWS DMS (Database Migration Service) in the correct order.

Question 14

Arrange the steps to enable encryption at rest for an existing unencrypted Amazon RDS for MariaDB DB instance in the correct order.

Question 15

Match each AWS service to its primary purpose.