DBS-C01 Monitoring and Troubleshooting Practice Test 10 — 15 Questions

Question 1

Refer to the exhibit. A developer runs the AWS CLI command and receives the output shown. What is this output?

Accepted Answer

The DNS endpoint of the DB instance. The output shown is the DNS endpoint of the RDS instance. This is the standard endpoint used to connect to the DB instance. Option A is correct because it matches this output. Option B is incorrect because the output is a DNS name, not a private IP address. Option C is incorrect because a reader endpoint includes a '-ro' suffix and applies only to Multi-AZ clusters. Option D is incorrect because the resource ID is a different identifier, not a DNS endpoint.

Answer

The private IP address of the DB instance

Answer

The reader endpoint of a Multi-AZ cluster

Answer

The resource ID of the DB instance

Question 2

Refer to the exhibit. A DBA sees the above log entries for an Amazon Aurora MySQL cluster. What is the most likely cause?

Accepted Answer

There is underlying storage corruption. The logs show page corruption and checksum errors, which are classic symptoms of underlying storage corruption in Aurora MySQL. Storage corruption can occur due to hardware failures or software bugs, and Aurora's distributed storage layer can sometimes experience such issues that manifest as corrupted pages. Option B is incorrect because reader lag would show replication delay metrics and possibly 'seconds behind master' warnings, not corruption-related errors. Option C is incorrect because connection exhaustion would generate errors like 'too many connections' or timeouts, not page corruption. Option D is incorrect because a failed backup restore would typically result in errors during the restore process, not ongoing page corruption after the instance is running.

Answer

The reader instance is lagging behind the writer

Answer

The DB instance has run out of connections

Answer

A recent backup restore operation failed

Question 3

A database administrator notices that an Amazon RDS for MySQL instance's CPU utilization is consistently above 80% during peak hours. The DB instance is a db.r5.large with 16 GB memory and 500 GB gp2 storage. The application is a read-intensive web application. Which action is MOST effective to reduce CPU load without significant cost increase?

Accepted Answer

Create a read replica and redirect read traffic to it. Creating a read replica offloads read traffic from the primary instance, reducing CPU utilization without requiring a larger instance or more storage. Option A increases cost significantly. Option C may not help if the issue is CPU, not I/O. Option D increases storage but does not directly reduce CPU.

Answer

Increase the instance size to db.r5.xlarge

Answer

Enable Performance Insights to identify slow queries and optimize them

Answer

Increase the allocated storage to 1000 GB to improve I/O performance

Question 4

A company uses Amazon DynamoDB with auto scaling enabled. During a sales event, the write capacity consumption increases, but the table does not scale up as expected, resulting in throttled requests. The table has read/write capacity mode set to 'Provisioned' with auto scaling configured. What should the team check first to troubleshoot the issue?

Accepted Answer

Check the target utilization percentage in the auto scaling policy. The target utilization percentage in the auto scaling policy determines when scaling triggers. If set too high (e.g., 90%), the table may not scale until near full consumption, causing throttling during spikes. Option B is incorrect: IAM permissions for the auto scaling role are typically preconfigured and less likely to cause scaling failures—permission issues would appear in CloudTrail, not as a gradual throttling problem. Option C is incorrect: table class (Standard vs. Standard-IA) affects storage costs, not write capacity scaling behavior. Option D is incorrect: while a GSI can throttle if its write capacity is insufficient, the primary table's auto scaling is independent; the question states the table itself does not scale, so the GSI is not the first check.

Answer

Verify that the auto scaling role has the necessary IAM permissions

Answer

Check whether the table class is DynamoDB Standard-IA

Answer

Check if a global secondary index (GSI) has its own write capacity that is throttling

Question 5

A company is using Amazon Redshift for data warehousing. The users report that queries are slow, and the CloudWatch metric 'CPUUtilization' shows high usage. The cluster has 4 dc2.large nodes. The administrator runs the following query: SELECT * FROM stl_query WHERE starttime > '2023-12-01' ORDER BY duration DESC LIMIT 10; This shows that the longest query runs for over 5 minutes. Which of the following is the MOST effective way to improve query performance?

Accepted Answer

Add more nodes to the cluster (scale out). Adding more nodes to the cluster (scale out) distributes the workload across additional compute resources, increasing parallelism and CPU capacity. This directly addresses the high CPU utilization and reduces query execution time. Option A is incorrect because the issue is CPU, not storage; increasing storage volume does not improve CPU performance. Option C is incorrect: VACUUM reclaims disk space and sorts data but does not directly improve query performance when the bottleneck is CPU. Option D is incorrect: increasing WLM concurrency allows more queries to run simultaneously but may actually increase contention and not improve the performance of individual long-running queries.

Answer

Increase the size of the cluster volume by adding more storage

Answer

Run the VACUUM command to reclaim space and improve query performance

Answer

Modify the workload management (WLM) queue to increase concurrency

Question 6

A company is running a MongoDB-compatible Amazon DocumentDB cluster with one writer and two readers. The application writes a large amount of data during batch processing, and after a batch completes, the writer's CPU is high, and the readers have significant replica lag. The team wants to reduce replica lag without affecting the batch performance. What should they do?

Accepted Answer

Increase the instance size of the readers to improve apply throughput. Replica lag in Amazon DocumentDB occurs when readers are unable to apply writes from the writer's oplog quickly enough. Increasing the instance size of the readers provides more CPU and memory resources, improving their apply throughput and reducing lag without affecting the writer's batch performance. Option A is incorrect because changing the storage type to Provisioned IOPS may improve I/O but does not directly address the reader's apply capacity. Option C is incorrect because reducing batch size would lower the write rate and degrade batch performance, which the team wants to avoid. Option D is incorrect because increasing the writer's instance size may help the writer handle more writes, but it does not help readers apply those writes faster; it could even increase the write rate and worsen replica lag.

Answer

Change the storage type to Provisioned IOPS on all instances

Answer

Reduce the batch size to lower the write rate

Answer

Increase the instance size of the writer to handle the batch faster

Question 7

A developer is receiving timeout errors when connecting to an Amazon ElastiCache for Redis cluster from an Amazon EC2 instance. The security group for the EC2 instance allows outbound traffic to the Redis cluster's security group on port 6379. The Redis cluster's security group does not allow inbound traffic from the EC2 instance. What is the most likely cause of the timeout?

Accepted Answer

The Redis cluster security group does not have an inbound rule allowing traffic from the EC2 security group on port 6379. Security groups are stateful, meaning that if an outbound rule allows traffic, the return traffic is automatically allowed. However, inbound traffic must be explicitly permitted by the target security group. In this scenario, the EC2 instance's security group allows outbound traffic to the Redis cluster's security group on port 6379, but the Redis cluster's security group does not have an inbound rule allowing traffic from the EC2 security group on that port. Therefore, the connection is blocked, causing timeout errors. Option A is incorrect because network ACLs are stateless and would affect both inbound and outbound traffic, but the issue is specifically with security groups. Option C is incorrect because subnet route tables control network routing, not port-level access. Option D is incorrect because ElastiCache for Redis clusters are accessible within the same VPC if proper security group rules are configured.

Answer

The network ACL for the Redis subnet is blocking inbound traffic on port 6379

Answer

The subnet route table does not have a route to the Redis cluster

Answer

The Redis cluster is not accessible from within the same VPC

Question 8

A company uses Amazon RDS for PostgreSQL with Multi-AZ deployment. The primary instance fails, and a failover occurs. After the failover, the application is still unable to connect to the database endpoint. The database administrator checks the RDS console and sees that the new primary is in 'available' state. What should the administrator do next to diagnose the connectivity issue?

Accepted Answer

Check the DNS resolution of the RDS endpoint from the application server. The DNS CNAME of the RDS endpoint should have updated to point to the new primary. If the application is using the old IP or a cached DNS entry, it may not connect. Option A is incorrect because security group rules are usually unchanged. Option B is incorrect because the subnet group is not the issue. Option D is incorrect because the primary is already in available state.

Answer

Verify that the security group for the RDS instance allows inbound traffic from the application

Answer

Check if the subnet group for the RDS instance is correctly configured

Answer

Restart the RDS instance to force a new connection

Question 9

A team is using Amazon DynamoDB Accelerator (DAX) to improve read performance for a table. They notice that DAX is returning stale data even though the TTL is set to 5 minutes. The table is updated frequently by multiple writers. What is the most likely cause of the stale reads?

Accepted Answer

The TTL is too long, causing cached items to remain after updates. DAX uses a write-through cache, meaning that items are cached only when they are read. When an item is updated in DynamoDB, the cached copy is not automatically invalidated; instead, it remains in the cache until the TTL expires. If the TTL is set too long (e.g., 5 minutes) and the item is updated frequently, stale data will be served from the cache until the TTL forces its removal. Option A is incorrect because cache misses would cause slower reads, not stale reads. Option B is incorrect because DAX always uses eventual consistency for reads, but stale data occurs here due to TTL, not consistency level. Option C is incorrect because DAX clusters are deployed in a single VPC and can be accessed from any AZ with proper routing; AZ placement does not cause staleness.

Answer

The DAX cluster is not large enough to cache all items, causing cache misses

Answer

DAX is configured with eventual consistency, which returns stale data by design

Answer

The DAX cluster is deployed in a different Availability Zone than the application

Question 10

A company is using Amazon RDS for SQL Server with native backup and restore. The backup process is failing with an error indicating insufficient disk space for the backup file. The DB instance has 200 GB of allocated storage, and the backup file is 50 GB. What should the database administrator do to resolve this issue?

Accepted Answer

Increase the allocated storage for the RDS instance. Native backups in RDS for SQL Server are stored in the instance's attached storage. When the allocated storage is full, the backup fails due to insufficient disk space. Increasing the allocated storage provides additional space for the backup file. Option A is incorrect because changing to Provisioned IOPS affects performance, not storage capacity. Option C is incorrect because the error is about disk space, not permissions. Option D is incorrect because switching to automated backups would not resolve the immediate failure of the native backup process, and the question specifically addresses native backup failure.

Answer

Change the storage type to Provisioned IOPS for better performance

Answer

Grant the rds_backup user additional permissions to write to S3

Answer

Switch to automated backups instead of native backups

Question 11

A database administrator is troubleshooting a performance issue on an Amazon Aurora MySQL cluster. The application is experiencing high latency on write operations. Which TWO CloudWatch metrics should the administrator analyze to identify the root cause?

Accepted Answer

DMLThroughput. The correct metrics to analyze for high write latency are DMLThroughput and CommitLatency. DMLThroughput measures the rate of Data Manipulation Language operations, including INSERT, UPDATE, and DELETE, which are write-intensive. High DMLThroughput may indicate excessive write operations. CommitLatency measures the time taken to commit transactions; high commit latency directly contributes to write latency. The other options are not directly related to write performance.

Answer

ReadLatency

Answer

SelectLatency

Answer

FreeableMemory

Question 12

A company is using Amazon DynamoDB with auto scaling for read and write capacity. During a traffic spike, write requests are being throttled even though the table's write capacity is below the maximum limit. Which TWO actions should the team take to resolve the throttling?

Accepted Answer

Review the table's partition key design to ensure even distribution of write traffic. A suboptimal partition key can cause a hot partition, leading to throttling even when overall table capacity is not exhausted. Option E is correct because DynamoDB auto scaling has a lag in provisioning capacity; pre-warming by manually increasing write capacity before a spike can prevent throttling. Option A is incorrect because DynamoDB Streams offloads data but does not increase write capacity. Option B is incorrect because global tables replicate data across regions but do not resolve local hot partitions or capacity lag. Option D is incorrect because reducing read capacity does not increase write capacity.

Answer

Enable DynamoDB Streams on the table to offload writes to a Lambda function

Answer

Create a DynamoDB global table to distribute writes across regions

Answer

Decrease the read capacity to free up resources for writes

Question 13

A company is using Amazon Redshift for analytics. The database administrator notices that some queries are slow and the system is running out of memory. Which THREE steps should the administrator take to improve performance?

Accepted Answer

Optimize the table design by choosing appropriate distkeys and sortkeys. Scaling up increases memory per node but may not be sufficient; scaling out (adding nodes) is often more effective. Option B is correct because proper distribution and sort keys minimize data shuffling and improve query performance. Option C is correct because adding more nodes increases the cluster's total memory and compute capacity. Option D is incorrect because VACUUM reclaims disk space, not memory. Option E is correct because configuring WLM limits concurrent queries, reducing memory contention.

Answer

Increase the node size (scale up) to get more memory per node

Answer

Run the VACUUM command to reclaim space from deleted rows

Question 14

A company runs a critical application on Amazon RDS for PostgreSQL with Multi-AZ deployment. The database is a db.r5.xlarge instance with 500 GB of Provisioned IOPS (io1) storage. The application writes a large volume of time-series data every 5 minutes. Recently, the team noticed that the replica lag between the primary and standby instance has increased to over 10 seconds during peak write periods. The application requires that the standby be within 5 seconds of the primary for disaster recovery purposes. The team has already verified that network latency between AZs is minimal, and the standby instance is the same size as the primary. The CloudWatch metric 'TransactionLogsDiskUsage' shows that the primary is generating logs at a high rate. Which combination of actions should the database administrator take to reduce the replica lag? (Choose the best option.)

Accepted Answer

Increase the Provisioned IOPS on both the primary and standby instances to improve log write throughput. Increasing Provisioned IOPS on both the primary and standby instances improves I/O throughput for writing transaction logs, which is the bottleneck causing replica lag. Higher IOPS reduces the time needed to generate logs on the primary and apply them on the standby. Option A is incorrect because disabling synchronous commit would reduce durability and may still not achieve the required lag reduction. Option B is incorrect since the standby instance size is already equal to the primary; the issue is I/O performance, not compute capacity. Option C is incorrect because reducing batch frequency impacts application responsiveness and does not address the I/O bottleneck.

Answer

Disable synchronous commit on the primary to reduce log generation

Answer

Change the standby instance to a larger instance type (e.g., db.r5.2xlarge) to improve apply throughput

Answer

Reduce the batch write frequency to every 10 minutes to lower the log generation rate

Question 15

A company is using Amazon DynamoDB for a gaming leaderboard. The table has a partition key of 'game_id' and a sort key of 'score'. The table is configured with on-demand capacity. During a major tournament, the application experiences high latency and some requests return 'ProvisionedThroughputExceededException' errors. The CloudWatch metric 'ThrottledRequests' spikes. The application uses a single partition key for all writes during the tournament (game_id = 'tournament_final'). What is the most likely cause of the throttling, and what is the best solution?

Accepted Answer

The application is using a single partition key, causing all writes to go to one partition. The team should redesign the partition key to distribute writes across multiple partitions. Even with on-demand capacity, each partition has a maximum throughput limit (read/write capacity per partition). When all writes use the same partition key ('tournament_final'), they all go to a single partition, exceeding its limit and causing throttling (ThrottledRequests and ProvisionedThroughputExceededException). The best solution is to redesign the partition key to distribute writes across multiple partitions (e.g., add a suffix like user_id or timestamp). Option B is incorrect because DAX is a caching layer for reads, not writes; it does not alleviate write throttling. Option C is incorrect because the question does not mention a GSI, and a GSI would not cause this issue on the base table. Option D is incorrect because switching to provisioned capacity does not solve the hot partition problem; the same partition key would still overload a single partition under provisioned capacity.

Answer

The application is using a single partition key, causing all writes to go to one partition. The team should implement DAX to cache writes

Answer

The table has a global secondary index that is throttling writes; the team should remove the GSI

Answer

The table is using on-demand capacity, which has a maximum throughput limit per partition; the team should switch to provisioned capacity with auto scaling