DBS-C01 Mock Exam 94 — 25 Questions

Question 1

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 2

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 3

A gaming company uses Amazon DynamoDB as the primary data store for player profiles and game state. The application experiences sudden spikes in traffic during new game launches, causing throttling on write requests. The current table has on-demand capacity mode. The table's partition key is 'player_id' (high cardinality). The read/write patterns are evenly distributed. Despite on-demand mode, throttling occurs because the per-partition throughput limit is being reached. The company wants to eliminate throttling without changing the partition key. Which solution should be recommended?

Accepted Answer

Enable DynamoDB adaptive capacity and implement write sharding using a random suffix.. Option D is correct. DynamoDB adaptive capacity automatically adjusts per-partition throughput based on traffic patterns. Enabling it (it is on by default) or ensuring it is active can help. However, if throttling persists, using write sharding by appending a suffix to the partition key can further distribute writes across partitions. Option A (increase RCU/WCU in provisioned mode) will not solve per-partition limits. Option B (switch to provisioned with auto scaling) similar issue. Option C (use DynamoDB Accelerator) only caches reads, not writes.

Answer

Implement Amazon DynamoDB Accelerator (DAX) to offload read traffic.

Answer

Use DynamoDB auto scaling with provisioned capacity.

Answer

Switch to provisioned capacity mode and increase write capacity units.

Question 4

A company uses Amazon DynamoDB to store user session data for a web application. The table has a partition key of 'user_id' and no sort key. Each item is about 5 KB. The application performs frequent GetItem and UpdateItem operations. Recently, the application has been experiencing higher than expected latency and some throttling. The table's read and write capacity are set to on-demand mode. The CloudWatch metrics show that the ConsumedWriteCapacityUnits are well below the provisioned limits (if they were provisioned), but there are occasional ThrottledWriteEvents. The application team also notices that the throttling occurs for specific users. What is the most likely cause and solution?

Accepted Answer

Implement write sharding by appending a random suffix to the partition key for high-traffic users.. Option B is correct because throttling for specific users indicates a hot partition, even with on-demand capacity, because DynamoDB distributes throughput evenly across partitions. A single partition can be throttled if its activity exceeds 3000 RCU or 1000 WCU. Option A (increasing capacity) is irrelevant for on-demand. Option C (adding a sort key) doesn't help because the partition key is already the main access pattern. Option D (adding a GSI) may not solve the hot partition issue for the base table writes.

Answer

Create a global secondary index with a different partition key for the hot users.

Answer

Add a sort key to the table to improve data distribution.

Answer

Switch to provisioned capacity mode and increase the write capacity units significantly.

Question 5

A startup is building a social media analytics platform. The workload is write-heavy, with millions of events per day containing user actions (likes, shares, comments). The data model is simple: each event is a JSON document with a timestamp, user ID, and action type. Queries are primarily aggregations over time (e.g., count of likes per hour) and require low-latency responses for dashboards. The team wants to minimize operational overhead and cost. Which database service is most appropriate?

Accepted Answer

Amazon Timestream, a purpose-built time-series database.. Option B is correct. Amazon Timestream is optimized for time-series data, supports high write throughput, and provides built-in aggregation functions for time-based queries. Option A (DynamoDB) is not ideal for time-series aggregations without additional processing. Option C (RDS for PostgreSQL) can handle the workload but may require more management and scaling effort. Option D (ElastiCache) is a cache, not a durable database.

Answer

Amazon ElastiCache for Redis to store aggregated counts.

Answer

Amazon RDS for PostgreSQL with TimescaleDB extension.

Answer

Amazon DynamoDB with global secondary indexes on timestamp and action type.

Question 6

A financial services company runs a critical application on Amazon RDS for MySQL that processes transactions. The database must maintain ACID compliance and support point-in-time recovery (PITR) with a recovery point objective (RPO) of 5 seconds and recovery time objective (RTO) of 1 minute. The current setup uses a single db.r5.large instance with automated backups enabled (retention period 7 days) and Multi-AZ deployment. During a recent failover test, the failover took 2 minutes, exceeding the RTO. What should the database specialist recommend to meet the RTO requirement?

Accepted Answer

Migrate to a Multi-AZ DB Cluster deployment for RDS for MySQL.. Option C is correct. Amazon RDS Multi-AZ with a Single-AZ deployment failover can be slow; using a Multi-AZ DB Cluster provides faster failover (typically under 1 minute) because it uses synchronous replication to two standby instances. Option A (increase instance size) does not affect failover time. Option B (use Read Replica for failover) adds complexity and is not automatic. Option D (disable Multi-AZ) would increase failover risk.

Answer

Remove Multi-AZ and rely on automated backups for recovery.

Answer

Configure a cross-Region read replica and promote it during a failure.

Answer

Increase the instance size to db.r5.xlarge to improve failover speed.

Question 7

A company is migrating a 500 GB Oracle database to Amazon RDS for Oracle. The migration must be completed within 24 hours with minimal downtime. The on-premises network bandwidth is 1 Gbps. The migration team plans to use AWS DMS with ongoing replication. During the initial load, the DMS task fails with an error indicating that the source database is experiencing high CPU utilization. The source database is a production system serving live traffic. The team needs to reduce the impact on the source database while still meeting the migration deadline. Which approach should the team take?

Accepted Answer

Reduce the number of tables loaded in parallel in the DMS task and schedule the migration during off-peak hours. The issue is that DMS is consuming too many resources on the source. Reducing the number of tables loaded in parallel and scheduling the migration during off-peak hours will reduce CPU impact. Option B is the most balanced. Option A (increase DMS instance size) would increase source CPU usage. Option C (change to full load only) would require downtime. Option D (use Snowball) may not meet the 24-hour deadline due to shipping time.

Answer

Change the DMS task to perform only full load and skip ongoing replication

Answer

Increase the size of the DMS replication instance to speed up the migration

Answer

Use AWS Snowball to transfer the data offline and then use DMS for ongoing replication

Question 8

A company is migrating a 500 GB PostgreSQL database from on-premises to Amazon Aurora PostgreSQL. The migration must be completed within 6 hours with minimal downtime. The on-premises network bandwidth is 500 Mbps. The team decides to use AWS DMS. During initial full load, DMS reports a 'Target metadata error: cannot write to Aurora' for a specific table. Investigation shows that the table has a trigger that tries to insert into an external web service. What should the team do to resolve this issue?

Accepted Answer

Disable the trigger on the target Aurora database during migration.. Option C is correct: disabling triggers on the target prevents external calls during migration. Option A is wrong because the issue is not about DMS version. Option B is wrong because disabling the trigger on the source does not help. Option D is wrong because increasing instance size does not fix the trigger issue.

Answer

Increase the DMS replication instance size.

Answer

Upgrade the DMS replication instance engine version.

Answer

Disable the trigger on the source database.

Question 9

A company is deploying a new web application on AWS. The application uses Amazon RDS for MySQL as its database. The database must be highly available and automatically failover in the event of an AZ outage. The company also needs to offload read traffic from the primary database to improve performance. The application read-to-write ratio is 80:20. The database workload is variable, with occasional spikes. The company wants a cost-effective solution that scales read capacity automatically. The operations team has limited experience with AWS. Which solution should the company implement?

Accepted Answer

Deploy an Amazon Aurora MySQL cluster with Multi-AZ and enable Aurora Auto Scaling for read replicas. Multi-AZ provides automatic failover. Read replicas offload read traffic. However, read replicas do not auto-scale automatically; you need to add replicas manually or use auto-scaling with a custom solution. But given limited experience, the simplest solution is to use Multi-AZ and one or more read replicas. Option C (Multi-AZ with read replicas and Application Auto Scaling) is the most complete, but Application Auto Scaling for read replicas requires custom metrics. Option D (Aurora with Auto Scaling) is simpler because Aurora Auto Scaling automatically adds/removes read replicas based on CPU or connections. Aurora is MySQL-compatible and provides better performance and scalability. For limited experience, Aurora is a better choice. Option A (Multi-AZ only) does not offload reads. Option B (Read replicas only) lacks automatic failover. So the best is D.

Answer

Deploy an RDS for MySQL instance with Multi-AZ and one or more read replicas, and use Application Auto Scaling to add replicas based on CPU utilization

Answer

Deploy an RDS for MySQL instance with Multi-AZ and enable automatic scaling of the instance size

Answer

Deploy an RDS for MySQL instance with multiple read replicas and use a custom script to promote a read replica in case of failure

Question 10

A company is migrating a 3 TB Oracle database from on-premises to Amazon RDS for Oracle. The migration must have zero downtime. The on-premises server uses Oracle Data Guard for disaster recovery. The network bandwidth is 200 Mbps. The team plans to use AWS DMS with CDC from Oracle redo logs. During the initial full load, DMS reports that the source table 'ORDERS' has a table-level supplemental log missing. The migration fails. What should the team do first to resolve this issue?

Accepted Answer

Enable supplemental logging on the Oracle source database for the 'ORDERS' table.. Option B is correct: the missing supplemental log prevents DMS from capturing changes. Option A is wrong because re-creating the task without fixing the log will fail again. Option C is wrong because using a different tool may not address the root cause. Option D is wrong because disabling CDC would cause downtime.

Answer

Switch to Oracle GoldenGate for migration.

Answer

Use AWS SCT to convert the schema and then use a native export/import.

Answer

Recreate the DMS task with 'full load only' to bypass CDC.

Question 11

A company is migrating a 2 TB Oracle database to Amazon RDS for Oracle. The migration requires minimal downtime. They use AWS SCT to convert the schema and AWS DMS for data migration. After the full load, DMS ongoing replication is unable to capture changes because the archived redo logs are being deleted on the source before DMS can read them. The source database has a log retention setting of 2 hours. The team cannot increase the retention due to storage constraints. What should they do?

Accepted Answer

Configure DMS to use an S3 bucket to store archived redo logs.. Option B is correct: using an S3 staging area to store archived logs temporarily gives DMS more time to read them. Option A is wrong because increasing instance size does not affect log retention. Option C is wrong because it would cause data loss. Option D is wrong because changing to full load only would cause downtime.

Answer

Switch to a full load only migration strategy.

Answer

Disable archiving on the source database.

Answer

Increase the DMS replication instance size to improve log reading speed.

Question 12

A company is migrating a 3 TB on-premises Oracle database to Amazon Aurora PostgreSQL. The source database runs Oracle 12c on a Linux server with a 1 Gbps network connection to AWS via Direct Connect. The migration must have minimal downtime and be completed within a 2-day window. The database is heavily used during business hours (9 AM - 5 PM) and has low activity overnight. The company has a test environment on AWS already. The migration team plans to use AWS DMS with CDC for ongoing replication. They also plan to use AWS SCT for schema conversion. They start the migration on a Friday evening. On Saturday morning, the CDC replication lag is increasing, and the target Aurora instance is struggling to keep up. The team notices that the source database has a high number of write transactions even during the weekend, and the DMS replication instance is a small instance type. Which action should the team take to complete the migration on time?

Accepted Answer

Upgrade the DMS replication instance to a larger size to increase throughput.. Option B is correct because upgrading the DMS replication instance increases its capacity to handle high write volume. Option A is wrong because it doesn't address the bottleneck. Option C is wrong because it adds complexity and may not help. Option D is wrong because it doesn't solve the replication lag.

Answer

Reduce the number of tables being migrated to lower the load.

Answer

Pause the migration and restart it on Monday during low activity.

Answer

Switch to use AWS Snowball to transfer data and bypass DMS.

Question 13

A startup is deploying a new web application on AWS. The application uses Amazon RDS for PostgreSQL as its database. The developers need to create the initial schema and load test data. They have a SQL script file that creates tables and inserts 10,000 rows. The DB instance is a db.t3.micro with 20 GB gp2 storage. The developers connect to the RDS instance using pgAdmin from their local machine and run the script. The script fails with a 'connection reset' error after inserting 5,000 rows. What is the MOST likely cause?

Accepted Answer

The DB instance ran out of memory or CPU credits due to the intensive script. Option A is correct because the db.t3.micro instance has limited memory and CPU credits; a large script can exhaust resources and cause a connection reset. Option B is wrong because the storage is 20 GB and 10,000 rows of test data are small. Option C is wrong because security group timeout is not typical for a connection reset. Option D is wrong because the default max_connections for db.t3.micro is typically sufficient for a single connection.

Answer

The max_connections parameter was set too low

Answer

The storage volume reached its maximum capacity

Answer

The security group blocked the connection due to a timeout

Question 14

A company is migrating a 3 TB PostgreSQL database from on-premises to Amazon RDS for PostgreSQL. The migration must have minimal downtime. The on-premises network bandwidth to AWS is 1 Gbps. The database supports a critical application that cannot be stopped for more than 30 minutes. The migration team has set up AWS DMS with ongoing replication using CDC. The initial full load is expected to take 8 hours. During the migration, the DMS task fails with an error indicating that the source database's transaction log is being cleaned before DMS can capture changes. The source database has a transaction log retention period of 24 hours. The migration team notices that the CDC phase falls behind during peak hours. What should the team do to complete the migration successfully within the downtime window?

Accepted Answer

Increase the transaction log retention period on the source database and use a larger DMS replication instance. The issue is that DMS cannot keep up with changes during peak hours, causing the transaction logs to be cleaned before DMS reads them. Increasing the source database's transaction log retention period gives DMS more time to catch up. Using a larger DMS replication instance will increase processing capacity. Option C is the best combination. Option A (Snowball) would take too long. Option B (Direct Connect) doesn't solve the log retention issue. Option D (decrease retention) makes the problem worse.

Answer

Use AWS Snowball to transfer the full database and then use DMS for CDC

Answer

Increase the network bandwidth using AWS Direct Connect

Answer

Decrease the transaction log retention period to free up space

Question 15

A company is migrating a 2 TB Oracle database to Amazon Aurora PostgreSQL. The on-premises database is 2 TB and the network link is 1 Gbps. The migration must complete within 48 hours with minimal data loss. The DBA has extracted the schema using AWS SCT and created an Aurora cluster. The DBA now needs to transfer the data. The DBA tried using AWS DMS over the network but estimates it will take 72 hours due to network overhead. The DBA also considered using an AWS Snowball Edge device but is concerned about the time to order and ship the device. Which approach should the DBA take to meet the deadline?

Accepted Answer

Use AWS Snowball Edge to transfer the database dump and then use DMS for change data capture. Option C is correct. The DBA should use AWS DMS with CDC to start the migration, then use a Snowball Edge for the initial load to reduce time. However, given the 48-hour window, using DMS with CDC and compressing data may still be too slow. Option B is actually the best: use Snowball Edge for initial load (which can be ordered and shipped quickly if available in region) and then DMS for CDC. But the correct answer according to best practices is to use DMS with CDC and optimize network (e.g., using compression, increasing instance size). However, the scenario suggests DMS alone is too slow. The best answer is to use Snowball Edge for the full load and DMS for CDC, as Snowball can transfer 2 TB quickly once received. Option A is wrong because it abandons CDC. Option B is correct because it combines Snowball for bulk and DMS for ongoing changes. Option C is wrong because it only uses DMS. Option D is wrong because it uses S3 only.

Answer

Use AWS DMS with change data capture and optimize the network by enabling compression

Answer

Use AWS S3 to upload the database dump and then load into Aurora

Answer

Use AWS DMS to perform a full load and then stop replication, accepting some data loss

Question 16

A company has a 50 GB MariaDB database on an on-premises server. They want to migrate to Amazon RDS for MariaDB. They have a 100 Mbps network connection. The migration window is 2 hours. The database can be offline for up to 30 minutes. Which migration approach is most appropriate?

Accepted Answer

Create a MariaDB dump, transfer to an EC2 instance in the same region, then import to RDS.. Option A is correct: creating a dump and importing is straightforward and can be done within the window. Option B is wrong because DMS may require more setup. Option C is wrong because Snowball is overkill. Option D is wrong because SCT is for schema conversion, not needed here.

Answer

Use AWS DMS with ongoing replication.

Answer

Use AWS Snowball to transfer the database files.

Answer

Use AWS SCT to convert schema and then use DMS.

Question 17

A company is migrating a 5 TB PostgreSQL database from on-premises to Amazon RDS for PostgreSQL. The database has a 24/7 uptime requirement and must be migrated with minimal downtime. The on-premises network bandwidth is 100 Mbps. The migration must be completed within 48 hours. The team has chosen to use AWS Database Migration Service (DMS) with ongoing replication from a change data capture (CDC) source. After setting up the source endpoint, target endpoint, and replication instance, the initial full load takes 30 hours. However, during the CDC phase, the target falls behind by over 2 hours and continues to lag. The replication instance is a dms.c5.large (2 vCPUs, 4 GB memory). The source database is heavily utilized with frequent updates. What should the team do to reduce the CDC lag and meet the migration deadline?

Accepted Answer

Increase the replication instance size to dms.c5.2xlarge.. Option A is correct: increasing the replication instance size to dms.c5.2xlarge provides more CPU and memory, which can handle higher transaction throughput. Option B is wrong because splitting into multiple tasks adds overhead and may not help with a single source. Option C is wrong because DMS does not use Multi-AZ. Option D is wrong because disabling ongoing replication would cause data loss.

Answer

Enable Multi-AZ on the replication instance for better performance.

Answer

Split the migration into multiple DMS tasks for parallel processing.

Answer

Disable ongoing replication and use a one-time full load migration.

Question 18

A company is deploying a new Amazon RDS for SQL Server DB instance with Multi-AZ. The database will be used by a critical application that requires minimal downtime during failover. The application uses a single connection string with the CNAME of the RDS endpoint. During a recent failover test, the application experienced a 3-minute timeout. The DBA wants to reduce the failover time. The current RDS instance is db.r5.large with 100 GB gp2 storage. The application is hosted on EC2 in the same VPC. Which change would MOST effectively reduce the failover time?

Accepted Answer

Configure the application to use a low TTL for DNS lookups and implement connection retries. Option A is correct because Multi-AZ failover typically completes within 1-2 minutes; the 3-minute timeout may be due to DNS caching. Enabling TTL=5 on the application side for DNS lookups can reduce failover time. Option B is wrong because increasing instance size does not directly reduce failover time. Option C is wrong because Provisioned IOPS does not reduce failover time. Option D is wrong because read replicas do not provide automatic failover.

Answer

Change the storage to Provisioned IOPS (io1)

Answer

Change the instance type to db.r5.xlarge

Answer

Use a read replica with automatic failover

Question 19

A company is migrating a 2 TB Oracle database running on an on-premises Linux server to Amazon RDS for Oracle. The migration must have minimal downtime and must be fully reversible if any issues arise. The DBA has configured AWS DMS with a full load and ongoing replication task. The full load completes successfully, and CDC is replicating changes. During the cutover window, the DBA stops the source database and promotes the target RDS instance. However, after cutover, the application team reports that some recent transactions are missing from the target database. The DBA confirms that the DMS task showed a 'healthy' status before stopping. Which action should the DBA take to resolve the issue and prevent recurrence?

Accepted Answer

Before stopping the source database, run the 'Stop task' command with the '--apply-immediately' option to ensure all cached changes are written to the target.. Option B is correct. In a DMS migration, the final step should be to capture remaining changes after stopping the source database. This is done by running the 'Stop task' command with the '--apply-immediately' flag or by using the 'Last Stop' command in the console. Option A is wrong because increasing the replication instance size does not address missing transactions. Option C is wrong because restarting the task does not capture the missed transactions; they were already lost. Option D is wrong because rebuilding the migration from scratch is time-consuming and does not address the root cause.

Answer

Rebuild the entire migration using a native Oracle export/import tool.

Answer

Increase the replication instance size to improve throughput.

Answer

Restart the DMS task from the beginning to recapture the missing data.

Question 20

A company is migrating a 500 GB MongoDB database to Amazon DocumentDB. The migration is done using AWS DMS with full load and ongoing replication. The full load completes successfully, but during CDC, the DMS task logs show 'Memory limit exceeded' errors and the task fails repeatedly. The DMS replication instance is a dms.r5.large. The source MongoDB has a write-heavy workload with many small updates. The DBA needs to fix the migration without restarting from scratch. Which action should the DBA take?

Accepted Answer

Increase the DMS replication instance size to a larger instance class with more memory.. Option C is correct. The 'Memory limit exceeded' error often occurs when DMS cannot keep up with the volume of changes, especially for write-heavy workloads. Increasing the replication instance size provides more memory for buffering. Option A is wrong because stopping and restarting the task does not address the root cause. Option B is wrong because reducing the batch size may not help; the issue is memory capacity. Option D is wrong because enabling multi-AZ does not increase memory; it provides high availability.

Answer

Reduce the DMS task's 'BatchApplyEnabled' setting to apply transactions individually.

Answer

Stop and restart the DMS task to clear the cache.

Answer

Enable Multi-AZ on the DMS replication instance to distribute the load.