DBS-C01 Mock Exam 92 — 25 Questions

Question 1

A company has a document database workload on Amazon DynamoDB that stores user session data. The application frequently updates session attributes (e.g., last activity timestamp). The current design stores the entire session as a single item and updates the entire item on each session activity. This is causing high write costs and throttling. Which design pattern would reduce write costs and improve performance?

Accepted Answer

Use UpdateItem with an update expression to modify only the changed attributes.. Option B is correct because using UpdateItem with an update expression allows you to modify only the specific attributes that changed (e.g., last activity timestamp) instead of rewriting the entire item. This reduces write consumption to a fraction of the original cost, since DynamoDB charges based on the size of the written data, and partial updates write only the changed attribute bytes. This directly addresses the high write costs and throttling caused by full-item overwrites.

Answer

Increase the write capacity units (WCUs) on the table.

Answer

Implement DynamoDB Accelerator (DAX) to cache the session data.

Answer

Split the session item into multiple items, one per attribute.

Question 2

A company uses Amazon DynamoDB for a time-series IoT application. Each device sends a data point every second. The application queries data by device ID and timestamp range. Which table design is most efficient?

Accepted Answer

Use device ID as the partition key and timestamp as the sort key.. Option D is correct because it models the access pattern directly: using device ID as the partition key ensures all data for a device is co-located, and timestamp as the sort key enables efficient range queries (e.g., Query with KeyConditionExpression on timestamp between start and end). This design avoids hot partitions and allows DynamoDB to retrieve the exact time-series slice without scanning.

Answer

Use a composite key of device ID and timestamp as the partition key.

Answer

Use device ID as the partition key and a random suffix as the sort key.

Answer

Use timestamp as the partition key and device ID as the sort key.

Question 3

Refer to the exhibit. A database specialist is troubleshooting an issue where an application cannot connect to an RDS for MySQL instance using IAM database authentication. The application uses the database user 'db_user1'. The IAM policy shown is attached to the IAM role used by the application. What is the most likely reason for the connection failure?

Accepted Answer

The resource ARN in the policy uses an incorrect RDS resource ID.. Option C is correct because IAM database authentication for RDS MySQL requires the resource ARN in the IAM policy to include the correct RDS resource ID (the 'db-xxxxx' identifier from the RDS console), not the DB instance name or endpoint. If the ARN uses an incorrect resource ID, the policy will not match the target RDS instance, causing the authentication to fail even if the user name and action are correct.

Answer

The action 'rds-db:connect' is not allowed for RDS MySQL.

Answer

The policy should have 'Deny' effect instead of 'Allow'.

Answer

The database user name in the ARN must be 'admin', not 'db_user1'.

Question 4

A company is migrating a large Oracle Data Warehouse (10 TB) to Amazon Redshift. The current system uses complex stored procedures, materialized views, and window functions. The company wants to minimize migration effort. Which approach is MOST suitable?

Accepted Answer

Migrate to Amazon Redshift and rewrite stored procedures as SQL scripts.. Option B is correct because Amazon Redshift is purpose-built for large-scale data warehousing and analytics, making it the natural target for a 10 TB Oracle Data Warehouse. While stored procedures would need to be rewritten as SQL scripts (since Redshift uses a different procedural language), materialized views and window functions are natively supported, minimizing migration effort compared to other options.

Answer

Migrate to Amazon Aurora MySQL and use its parallel query feature.

Answer

Migrate to Amazon DynamoDB and use DAX for caching.

Answer

Migrate to Amazon RDS for PostgreSQL and use its foreign data wrappers.

Question 5

A gaming company uses Amazon DynamoDB for player profiles. The access pattern is to retrieve a player's profile by 'player_id'. Each profile includes a list of 'achievements' that can grow up to 400 KB. Recently, the application has been encountering 'ProvisionedThroughputExceededException' errors. The table has 1000 read capacity units (RCU) and 500 write capacity units (WCU). The average item size is 200 KB. What is the MOST likely cause of the throttling?

Accepted Answer

The application is using strongly consistent reads, which consume double the read capacity.. Strongly consistent reads in DynamoDB consume twice the read capacity units (RCUs) as eventually consistent reads. With an average item size of 200 KB, each strongly consistent read consumes 200 KB / 4 KB = 50 RCUs (rounded up). If the application is using strongly consistent reads, a single read of a 200 KB item uses 50 RCUs, which can quickly exhaust the 1000 RCU table capacity, especially under concurrent access, leading to ProvisionedThroughputExceededException.

Answer

The item size exceeds the DynamoDB item size limit of 400 KB.

Answer

The read capacity units are set too low for the number of partitions.

Answer

The partition key 'player_id' is causing hot partitions.

Question 6

Which TWO of the following are valid methods to migrate an on-premises MySQL database to Amazon RDS for MySQL? (Select TWO.)

Accepted Answer

Use AWS DMS to migrate from on-premises MySQL to RDS.. AWS DMS (Database Migration Service) is a fully managed service designed to migrate databases to AWS with minimal downtime. It supports homogeneous migrations like MySQL to Amazon RDS for MySQL, handling schema conversion, data replication, and ongoing changes via change data capture (CDC). This makes option B a valid and recommended method for migrating an on-premises MySQL database to RDS.

Answer

Use pg_dump to export the data and import into RDS.

Answer

Use SQL Server Integration Services (SSIS) to migrate the data.

Answer

Use Oracle Data Pump to export the data and import into RDS.

Question 7

A company is building a real-time leaderboard for a mobile game using Amazon DynamoDB. The leaderboard displays the top 100 players by score. The table has 'game_id' as partition key and 'player_id' as sort key. The score is updated frequently. Which THREE design patterns should the company implement to ensure low-latency reads for the leaderboard? (Choose three.)

Accepted Answer

Enable DynamoDB Accelerator (DAX) to cache the leaderboard query.. Option B is correct because DynamoDB Accelerator (DAX) provides an in-memory cache that can serve repeated leaderboard queries with microsecond latency, reducing the read load on the base table and avoiding throttling. For a real-time leaderboard that is read frequently, DAX ensures low-latency responses without needing to query DynamoDB directly each time.

Answer

Use 'ScanIndexForward: false' on the base table to get top players.

Answer

Use 'begins_with' operator on the sort key to filter by score range.

Question 8

A company is designing a document storage system using Amazon DynamoDB. Each document is up to 400 KB and is identified by a unique 'document_id'. The access pattern is to retrieve a document by its ID. Which DynamoDB table design is MOST efficient?

Accepted Answer

Use 'document_id' as the primary partition key (only).. Option B is correct because DynamoDB can store items up to 400 KB in a single table, and using 'document_id' as the sole partition key directly supports the access pattern of retrieving a document by its ID with a single GetItem call, which is the most efficient operation. No secondary index or composite key is needed, as the primary key alone provides O(1) lookup performance for this use case.

Answer

Use 'document_id' as the partition key and create a GSI on 'document_id'.

Answer

Store documents in Amazon S3 and use DynamoDB to store metadata with a reference to S3.

Answer

Use a composite key: partition key 'document_id' and sort key 'version'.

Question 9

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 10

A gaming company runs a global leaderboard on Amazon DynamoDB. The leaderboard is updated frequently and must return the top 100 scores in milliseconds. The current design uses a single table with a Global Secondary Index (GSI) on score. However, the query to retrieve top scores often throttles under load. Which design change would best improve performance?

Accepted Answer

Implement a write shard pattern using a random suffix on the partition key and a GSI on score.. Option B is correct because the write shard pattern distributes high-frequency writes across multiple partition keys by appending a random suffix, preventing hot partitions. The GSI on score still allows efficient top-N queries by scanning the index in descending order. This avoids throttling by spreading write capacity evenly, while the GSI remains a sparse index that can be queried without impacting the base table's write throughput.

Answer

Use a scan operation with a filter to retrieve top scores.

Answer

Add DynamoDB Accelerator (DAX) in front of the table.

Answer

Switch to strongly consistent reads for the leaderboard query.

Question 11

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 12

A retail company runs its inventory management system on Amazon RDS for PostgreSQL. The application performs frequent updates to inventory counts. The operations team notices that write latency increases significantly during peak sales hours. The database is a single db.r5.large instance with General Purpose SSD (gp2) storage. The CPU utilization is around 40% during peaks, but the write latency spikes. The team suspects a storage bottleneck. Which change would most effectively reduce write latency?

Accepted Answer

Change storage type to Provisioned IOPS (io2) with sufficient IOPS.. Option B is correct. Provisioned IOPS (io1/io2) storage provides consistent low-latency I/O performance compared to gp2, which can have variable performance due to burst credits. Option A (increase instance size) may not address I/O bottleneck. Option C (Multi-AZ) improves availability, not latency. Option D (read replica) offloads reads, not writes.

Answer

Enable Multi-AZ deployment.

Answer

Add a read replica to offload read traffic.

Answer

Scale up to a db.r5.xlarge instance.

Question 13

An organization wants to migrate a 200 GB MySQL database from an on-premises server to Amazon Aurora MySQL. They have a 50 Mbps network connection. The database is 8 hours of downtime. The migration must be completed within 24 hours and costs must be minimized. The team decides to use AWS Database Migration Service (DMS). Which approach best meets the requirements?

Accepted Answer

Use AWS DMS with a full load and ongoing replication to minimize downtime.. Option B is correct: using a DMS full load with ongoing replication minimizes downtime and works within the network constraints. Option A is wrong because taking a dump over the network would take too long. Option C is wrong because using a larger instance is unnecessary and costly. Option D is wrong because using Snowball is overkill for 200 GB.

Answer

Create a mysqldump and transfer it over the network to S3, then restore to Aurora.

Answer

Use AWS Snowball Edge to transfer the data physically.

Answer

Provision a large EC2 instance to run a parallel export and import.

Question 14

A company is migrating a 1 TB SQL Server database to Amazon RDS for SQL Server. The migration window is 4 hours. The on-premises network bandwidth is 1 Gbps. The database is not actively used during the migration window. The team wants to minimize migration time and cost. Which migration strategy is most appropriate?

Accepted Answer

Use AWS DMS with full load only.. Option C is correct: using AWS DMS with full load is the fastest and most cost-effective. Option A is wrong because BCP/DTS may be slower. Option B is wrong because native backup/restore requires S3 transfer and is slower. Option D is wrong because Snowball is unnecessary for 1 TB over 1 Gbps.

Answer

Use SQL Server BCP utility to export data and then import using DTS.

Answer

Use AWS Snowball to transfer the database files.

Answer

Create a native backup, upload to S3, restore to RDS.

Question 15

A company has a 100 GB MySQL database on an EC2 instance. They want to migrate to Amazon RDS for MySQL with minimal downtime. They have set up replication from the source to the target using MySQL native replication. After enabling replication, the 'Seconds_Behind_Master' value is increasing. The source database is write-heavy. What should the team do to reduce replication lag?

Accepted Answer

Increase the RDS instance size to a larger instance class.. Option B is correct: using a larger RDS instance class provides more resources to apply changes. Option A is wrong because enabling Multi-AZ does not improve replication performance. Option C is wrong because DMS is not needed. Option D is wrong because tuning the source may help but the target is the bottleneck.

Answer

Tune the source database to reduce write load.

Answer

Enable Multi-AZ on the RDS instance.

Answer

Switch to AWS DMS for migration.

Question 16

A company runs an e-commerce platform using Amazon Aurora MySQL with Multi-AZ deployment. The application has a read-heavy workload and uses a mix of SELECT and UPDATE queries. Recently, the company migrated from a db.r5.large to a db.r5.2xlarge instance class to handle increased traffic. However, after the migration, the CPU utilization remains high during peak hours, and the application's page load times have increased. The DBA notices that the 'Read IOPS' metric is high, but the 'Read Latency' metric is low. There is also a high number of 'Select' queries in the database. The application uses a single database endpoint. What should the DBA do to reduce CPU utilization and improve read performance?

Accepted Answer

Create one or more Aurora Replicas and modify the application to use read-only endpoints for SELECT queries.. Option C is correct because creating read replicas offloads SELECT queries from the primary, reducing CPU. Option A is wrong because increasing instance size again may not be cost-effective and the issue is read-heavy. Option B is wrong because enabling Performance Insights only helps monitoring, not performance. Option D is wrong because Multi-AZ is for failover, not read scaling.

Answer

Enable Multi-AZ with one standby replica.

Answer

Enable Performance Insights and analyze the top SQL.

Answer

Upgrade the instance class to db.r5.4xlarge.

Question 17

A company has an Amazon RDS for SQL Server database that stores customer orders. The database is running on a db.m5.large instance. Recently, the DBA noticed that the 'Write IOPS' metric is consistently high during business hours, and the 'Write Latency' metric is also elevated. The application is experiencing slow order placement. The DBA checks the disk queue depth and finds it to be high. The database uses General Purpose SSD (gp2) storage with 500 GB. What should the DBA do to improve write performance?

Accepted Answer

Migrate to Provisioned IOPS (io2) storage with appropriate IOPS.. Option C is correct because Provisioned IOPS (io2) provides consistent low latency. Option A is wrong because gp3 may not provide enough IOPS for the workload. Option B is wrong because increasing storage size increases baseline IOPS for gp2, but 500 GB already gives 1500 IOPS; more IOPS may be needed. Option D is wrong because Multi-AZ does not improve write performance; it may increase latency.

Answer

Enable Multi-AZ to offload writes to standby.

Answer

Change the storage type to gp3 and increase the IOPS.

Answer

Increase the storage size to 1000 GB to get more baseline IOPS.

Question 18

A company is using Amazon DynamoDB with auto scaling for a social media application. The table has a partition key of 'user_id'. The application performs many small writes (update user profile) and reads (fetch user profile). Recently, the application's response time has increased. The DBA checks CloudWatch and sees that 'ConsumedWriteCapacityUnits' is close to 'ProvisionedWriteCapacityUnits', and 'WriteThrottleEvents' is low. However, 'ReadThrottleEvents' is high. The table has 1000 WCU and 1000 RCU provisioned. The auto scaling is configured to add capacity when utilization exceeds 70%. The DBA also notices that 'ReadThrottleEvents' spikes during peak hours. What should the DBA do to reduce read throttling?

Accepted Answer

Increase the provisioned read capacity to 2000 RCU or implement DAX caching.. Option D is correct because increasing RCU or using DAX can reduce read throttling. Option A is wrong because auto scaling target should be 70%, not 90%. Option B is wrong because decreasing WCU doesn't help reads. Option C is wrong because GP2 is for RDS, not DynamoDB.

Answer

Decrease the provisioned write capacity to 500 WCU to free up resources.

Answer

Increase the auto scaling target utilization to 90% to allow more headroom.

Answer

Change the storage type to General Purpose SSD (gp2) to improve I/O.

Question 19

A security team needs to audit all SQL statements executed against an Amazon Aurora MySQL DB cluster. Which combination of actions should be taken to achieve this? (Choose TWO.)

Accepted Answer

Set the server_audit_logging parameter to 1 in the DB cluster parameter group.. Options B and C are correct. To audit SQL statements in Aurora MySQL, you need to enable advanced audit (database activity streams) or use the Aurora MySQL audit plugin. Enabling the audit plugin is done by setting the server_audit_logging parameter to 1 in the DB cluster parameter group. Then you can publish the audit logs to Amazon CloudWatch Logs for analysis. Option A is incorrect because RDS event subscription does not capture SQL statements. Option D is incorrect because CloudTrail captures API calls, not database-level SQL. Option E is incorrect because Enhanced Monitoring captures OS metrics, not SQL.

Answer

Enable AWS CloudTrail for the Aurora DB cluster.

Answer

Enable Enhanced Monitoring for the DB cluster.

Answer

Enable RDS event subscription for the DB cluster.

Question 20

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

Create a global secondary index (GSI) with the same key structure but projecting only the required attributes.. Option B is correct because creating a GSI with the same key structure but projecting only the required attributes reduces the amount of data read from the base table. Since the application retrieves all attributes, but the GSI can be designed to project only the necessary columns, this minimizes read I/O and reduces latency. This is especially effective for sensors with high data volume, as it avoids fetching large item payloads from the base table.

Answer

Use DynamoDB Accelerator (DAX) for the table.

Answer

Enable DynamoDB Streams to replicate data to Amazon ElastiCache.

Answer

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