DBS-C01 Monitoring and Troubleshooting Practice Test 20 — 15 Questions

Question 1

A company is using Amazon DynamoDB with global tables. The application is experiencing high write latency in one region. Which THREE factors could contribute to this issue? (Choose three.)

Accepted Answer

Replication lag from global tables causing write conflicts.. Replication lag in global tables can lead to write conflicts and increased latency. Option C is correct because high network latency between the application and the DynamoDB endpoint directly impacts write latency. Option E is correct because insufficient write capacity units provisioned can cause throttling, resulting in higher write latency. Option B is incorrect because read capacity units do not affect write throughput. Option D is incorrect because aggressive auto scaling increases capacity, which would reduce latency, not increase it.

Answer

Insufficient read capacity units provisioned for the table.

Answer

The table's auto scaling configuration is set to increase write capacity too aggressively.

Question 2

A database administrator needs to monitor the free storage space on an Amazon RDS for SQL Server instance. Which TWO CloudWatch metrics should be used? (Choose two.)

Accepted Answer

FreeStorageSpaceInPercent. (FreeStorageSpaceInPercent) and Option E (FreeStorageSpace) are correct. Option A is wrong because DatabaseConnections measures the number of connections, not storage. Option B is wrong because ReadLatency measures read latency, not storage. Option C is wrong because BinLogDiskUsage is a metric for MySQL/RDS for MySQL, not for SQL Server.

Answer

DatabaseConnections

Answer

ReadLatency

Answer

BinLogDiskUsage

Question 3

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.. Creating Aurora Replicas offloads read-only SELECT queries from the primary instance, reducing CPU utilization on the primary and improving overall read throughput. Option A is incorrect because enabling Multi-AZ with one standby replica provides high availability but does not help with read scaling; the standby is not used for reads. Option B is incorrect because Performance Insights is a monitoring tool that helps identify performance bottlenecks but does not directly reduce CPU utilization. Option C is incorrect because simply upgrading to a larger instance class may temporarily alleviate the issue but is not cost-effective and does not address the root cause of read-heavy workload imbalance; adding read replicas scales reads horizontally.

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 4

A startup is using Amazon DynamoDB for a gaming leaderboard. The table has a partition key of 'game_id' and a sort key of 'score'. The application frequently queries the top 10 scores for a given game. Recently, users have reported that the leaderboard is showing stale data. The DBA checks the CloudWatch metrics and sees no throttling. The table has auto scaling enabled. The application uses eventual consistent reads. The DBA suspects that the issue is related to write conflicts. What should the DBA do to ensure the leaderboard shows the most recent data?

Accepted Answer

Modify the application to use strongly consistent reads for leaderboard queries.. Strongly consistent reads return the most up-to-date data from DynamoDB, ensuring the leaderboard displays the latest scores. Option B is incorrect because DynamoDB Streams capture changes but do not affect read consistency. Option C is incorrect because DAX is a caching layer that may serve stale data, not improve consistency. Option D is incorrect because write capacity adjustments do not address read consistency issues.

Answer

Enable DynamoDB Streams and process updates in near-real-time.

Answer

Enable DynamoDB Accelerator (DAX) for caching.

Answer

Increase the write capacity units to reduce write throttling.

Question 5

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.. Provisioned IOPS (io2) storage provides consistent, high IOPS performance with low latency, which is needed when the workload exceeds the baseline IOPS of gp2 storage. The current gp2 volume at 500 GB provides only 1,500 baseline IOPS (3 IOPS per GB), which is insufficient for the high write IOPS during business hours, causing high write latency and disk queue depth. Option A is incorrect because Multi-AZ does not offload writes; it only provides high availability and may increase write latency due to synchronous replication. Option B is incorrect because while gp3 offers baseline performance with the ability to provision additional IOPS independently of storage size, it is still a general-purpose SSD; for consistent low latency under sustained high IOPS, io2 is more suitable. Option C is incorrect because increasing gp2 storage to 1,000 GB would increase baseline IOPS to 3,000, but this may not be enough, and it is more cost-effective to use io2 with the required IOPS rather than over-provisioning storage.

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 6

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.. Increasing provisioned read capacity to 2000 RCU would directly address the read throttling, and implementing DAX caching would offload read traffic, reducing the load on the DynamoDB table. Option A is incorrect because decreasing write capacity does not free up read capacity; DynamoDB provisioned capacity for reads and writes is independent. Option B is incorrect because increasing the auto scaling target utilization to 90% would actually cause scaling to trigger at a higher utilization, potentially leading to more throttling, not less. Option C is incorrect because DynamoDB does not use storage types like GP2; that is a concept for Amazon EBS volumes, not applicable to 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 7

A company has an Amazon RDS for PostgreSQL database that is experiencing intermittent connection timeouts. The application logs show 'FATAL: remaining connection slots are reserved for non-replication superuser connections'. The database has a max_connections parameter set to 200. The application uses a connection pool. The DBA checks the CloudWatch metric 'DatabaseConnections' and sees it at 195 during peak hours. The application is deployed on AWS Lambda with a provisioned concurrency of 100. The Lambda function creates a new connection for each invocation. What should the DBA do to resolve the issue?

Accepted Answer

Set up Amazon RDS Proxy to manage the database connections from Lambda.. Set up Amazon RDS Proxy. The issue is that Lambda functions create a new connection per invocation, quickly exhausting the connection pool. While the DatabaseConnections metric shows 195 out of 200 max_connections, the Lambda functions are opening connections and not closing them properly, leading to connection exhaustion. RDS Proxy manages connection pooling, reusing connections across invocations, reducing the number of connections needed. Option A is wrong because reducing provisioned concurrency does not address the underlying connection management issue and may impact application performance. Option B is wrong because increasing max_connections could lead to increased memory usage and potential performance degradation. Option D is wrong because decreasing max_connections would make the problem worse by reserving fewer slots.

Answer

Reduce the Lambda provisioned concurrency to 50.

Answer

Increase max_connections to 500 to accommodate more connections.

Answer

Decrease max_connections to 100 to reserve more slots.

Question 8

A company runs a production Amazon RDS for PostgreSQL Multi-AZ DB instance (db.r5.large) with 500 GB of General Purpose SSD (gp2) storage. The application experiences intermittent latency spikes every 15 minutes. Monitoring shows that during these spikes, the ReadIOPS metric on the primary instance spikes to 5,000 IOPS (the baseline is 1,500 IOPS), and the BurstBalance drops from 100% to 20% then recovers. There is no increase in CPU or connections. The application uses connection pooling with pgBouncer on an EC2 instance. The team has verified that no long-running queries or index scans are causing the spikes. Which action is MOST likely to resolve the intermittent latency?

Accepted Answer

Migrate the storage to gp3 with a baseline of 3,000 IOPS and 125 MB/s throughput.. The latency spikes are caused by gp2 storage burst credit exhaustion. The 500 GB gp2 volume has a baseline of 1,500 IOPS, but the workload spikes to 5,000 IOPS every 15 minutes, rapidly consuming burst credits. Migrating to gp3 provides a baseline of 3,000 IOPS and 125 MB/s throughput without relying on burst credits, thus eliminating the credit exhaustion issue. Option A (read replica) does not resolve the primary instance's write IOPS spikes. Option B (larger instance) does not address storage IOPS limitations; CPU and connections are already normal. Option D (scale storage to 1,000 GB) would increase the gp2 baseline to 3,000 IOPS and provide more burst credits, but gp3 offers a simpler, more cost-effective solution with consistent performance and no credit-based throttling.

Answer

Create a read replica and redirect read traffic to it.

Answer

Increase the DB instance to db.r5.xlarge to improve CPU and network performance.

Answer

Scale the storage to 1,000 GB to increase baseline IOPS and burst credits.

Question 9

A company uses Amazon DynamoDB to store sensor data from IoT devices. The table has a partition key of DeviceID (String) and a sort key of Timestamp (Number). The table is configured with provisioned capacity of 1000 read capacity units (RCUs) and 500 write capacity units (WCUs). Auto Scaling is enabled with target utilization of 70% and is working correctly. Recently, a new batch job was introduced that performs Scan operations on the entire table every hour. The Scan consumes many read capacity units and causes throttling of the sensor write requests. The team wants to minimize the impact on the write-heavy sensor ingestion. Which approach is BEST?

Accepted Answer

Switch the table to on-demand capacity mode to eliminate throttling.. Switching to on-demand capacity mode eliminates throttling by automatically scaling read/write capacity to meet traffic demands. Since the scan operation runs hourly and consumes significant RCUs, on-demand mode will handle the burst without throttling write requests. Option C is incorrect because DAX caches individual items, not entire scan results; hourly scans would cause cache misses, providing no benefit. Option A is incorrect because increasing write capacity does not address read throttling. Option B is incorrect because off-peak scheduling may not be feasible and does not guarantee elimination of throttling.

Answer

Increase the provisioned write capacity to 2000 WCUs to handle bursts.

Answer

Schedule the Scan to run during off-peak hours when sensor writes are lower.

Answer

Enable DAX (DynamoDB Accelerator) to cache Scan results and reduce read consumption on the table.

Question 10

A company runs a self-managed Redis cluster on Amazon EC2 for caching. The cluster has one primary and two replicas, each on c5.large instances. The application experiences high latency during peak hours. CloudWatch metrics show that the primary node's CPU utilization is consistently above 80% and the network bandwidth is near the instance limit. The replicas show moderate CPU usage. The team wants to reduce latency without increasing cost significantly. Which combination of actions should the team take? (Choose two.)

Accepted Answer

Enable Redis Cluster Mode and distribute data across multiple shards.. Enables Redis Cluster Mode to shard data across multiple nodes, reducing CPU and network load on the primary. Option E migrates to Amazon ElastiCache for Redis with Cluster Mode, which manages scaling and reduces operational overhead. Together, they address the bottleneck without significant cost increase. Option C (read from replicas) does not reduce primary write/CPU load. Option D (upgrade instance) increases cost and may still hit network limits. Option B (add replicas) increases cost and does not reduce primary load.

Answer

Add more EC2 instances as additional replicas to offload reads.

Answer

Configure the application to read from replica nodes.

Answer

Upgrade the primary to a c5.2xlarge instance type.

Question 11

A company uses Amazon Aurora MySQL for its e-commerce platform. The DB cluster has one writer and two readers. Recently, the application started showing occasional deadlock errors during order processing. The error logs show: 'Transaction (Process ID 123) was deadlocked on lock resources with another process and has been chosen as the deadlock victim. Rerun the transaction.' The application retries three times before failing. The development team wants to reduce the likelihood of deadlocks. Which three actions should the team take? (Choose three.)

Accepted Answer

Shorten the duration of transactions by committing frequently.. Reduces the time locks are held, decreasing contention. Option C uses a weaker isolation level (READ COMMITTED) that reduces locking overhead compared to REPEATABLE READ. Option E ensures transactions request locks in a consistent order, preventing cycle dependencies. Option B is incorrect because READ UNCOMMITTED can cause dirty reads and is not suitable for e-commerce order processing. Option D is incorrect because increasing retry attempts does not reduce the likelihood of deadlocks; it only allows more retries after a deadlock occurs.

Answer

Switch to READ UNCOMMITTED isolation level to reduce locking.

Answer

Increase the number of retry attempts to 10.

Question 12

A company uses Amazon DocumentDB (with MongoDB compatibility) for its content management system. The application runs on EC2 instances and connects to a DocumentDB cluster with one instance (db.r5.large). Recently, users reported that retrieving documents takes longer than usual. CloudWatch metrics show that the CPU utilization of the DocumentDB instance is at 90% and the freeable memory is below 100 MB. The team has verified that no query optimization is possible. Which action should the team take FIRST to improve performance?

Accepted Answer

Scale up the instance to db.r5.xlarge.. High CPU utilization (90%) and low freeable memory (<100 MB) indicate that the current instance size (db.r5.large) is insufficient for the workload. Scaling up to db.r5.xlarge provides additional CPU and memory resources, directly addressing the performance bottleneck. Option A (adding a read replica) offloads read traffic but does not reduce CPU/memory pressure on the primary writer. Option B (creating additional indexes) could improve query performance, but the team has verified that no query optimization is possible. Option C (increasing storage volume) improves I/O performance but does not directly affect CPU or memory usage.

Answer

Add a read replica instance to offload read traffic.

Answer

Create additional indexes on frequently queried fields.

Answer

Increase the storage volume size to improve I/O performance.

Question 13

A company runs a critical MySQL database on Amazon RDS Single-AZ (db.m5.large) with 200 GB of Provisioned IOPS (io1) storage set to 3000 IOPS. The application team reports that write operations are occasionally slow. CloudWatch metrics show that the Write IOPS metric peaks at 3500 IOPS during the slowdowns, but the average is 2000 IOPS. The Read IOPS average is 500 IOPS. The queue depth metric occasionally spikes to 20. The storage configuration includes a 50 GB General Purpose SSD (gp2) log volume attached to the same RDS instance. Which change will MOST effectively resolve the write latency?

Accepted Answer

Increase the provisioned IOPS on the io1 volume to 4000.. The io1 volume is provisioned at 3000 IOPS, but the workload bursts to 3500 IOPS, causing queue depth to spike. Increasing the provisioned IOPS to 4000 ensures that the volume can handle the peak without queuing. Option A is incorrect because changing to gp3 with 3000 baseline IOPS does not address the peak IOPS requirement; gp3 can burst but the consistent peak above baseline may still cause throttling. Option B is incorrect because moving the log volume to the same io1 volume would mix I/O streams and could increase contention, not reduce it. Option D is incorrect because enabling Multi-AZ provides high availability and fails over to a standby, but does not increase IOPS capacity or resolve write latency.

Answer

Change the storage type to gp3 with 3000 baseline IOPS.

Answer

Move the log volume to the same io1 volume to reduce I/O overhead.

Answer

Enable Multi-AZ for failover protection.

Question 14

A company uses Amazon DynamoDB Global Tables for a multi-region application. The table is configured with on-demand capacity. The application writes data in the us-east-1 region and reads from us-west-2. Users in us-west-2 report that data written in us-east-1 is not appearing in us-west-2 within the expected replication latency of under 5 seconds. Instead, replication sometimes takes up to 30 seconds. Which two factors could be causing this increased replication latency? (Choose two.)

Accepted Answer

High network latency or packet loss between us-east-1 and us-west-2.. Options B and E are correct. Option B: High network latency or packet loss between the two regions directly increases replication time. Option E: A large volume of writes creates a backlog in the Global Tables replication stream, causing delays. Option A is incorrect because reading before replication completes is a symptom, not a cause of increased latency. Option C is incorrect because read capacity in a replica region does not affect replication latency; replication uses write capacity. Option D is incorrect because on-demand capacity automatically scales and does not cause write throttling.

Answer

The application is reading from the us-west-2 table before replication completes.

Answer

The read capacity in us-west-2 is insufficient, causing read throttling.

Answer

The on-demand capacity mode is causing write throttling in us-east-1.

Question 15

A company runs a PostgreSQL database on an Amazon RDS DB instance (db.t3.medium) with 100 GB of General Purpose SSD (gp2) storage. The database is used by a web application that experiences occasional slowdowns. CloudWatch metrics show that the BurstBalance metric for the storage volume drops to 0% during peak usage and then recovers. The average IOPS during peak is 600, and the baseline IOPS for the volume is 300. The team needs a cost-effective solution to eliminate the performance issues. What should the team do?

Accepted Answer

Migrate the storage to gp3 with 3000 baseline IOPS.. To migrate to gp3 storage. gp3 provides a consistent baseline of 3000 IOPS regardless of volume size, eliminating the need for burst credits. This directly addresses the BurstBalance dropping to 0% during peak usage. Option A (upgrade instance) does not affect storage performance. Option B (increase gp2 to 200 GB) would raise the baseline IOPS to 600 and add more burst credits, but the workload still depends on credits and may not be as cost-effective as gp3. Option D (Performance Insights) is a monitoring tool and does not improve performance.

Answer

Upgrade the DB instance to db.t3.large.

Answer

Increase the gp2 volume size to 200 GB.

Answer

Enable Performance Insights to monitor database load.