DBS-C01 Monitoring and Troubleshooting Practice Test 11 — 15 Questions

Question 1

A company is running an Amazon RDS for SQL Server instance with Multi-AZ deployment. The database is used by a critical application. During a recent failover test, the application experienced a 2-minute downtime. The application's connection string uses the DB instance endpoint, not the cluster endpoint. Which change would minimize downtime during future failovers?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Modify the application to use the cluster endpoint instead of the instance endpoint. Using the cluster endpoint (the DNS name that automatically points to the current primary after failover) allows the application to reconnect to the new primary without waiting for DNS propagation, thus minimizing downtime. Option B is incorrect because increasing the DB instance class size does not reduce failover time. Option C is incorrect because read replicas are used for read scaling, not for reducing failover downtime. Option D is incorrect because Multi-AZ is already enabled, so enabling it again would have no effect.

Answer

Increase the DB instance class size

Answer

Create a read replica in a different Availability Zone

Answer

Enable Multi-AZ on the DB instance

Question 2

A company is using Amazon DynamoDB with auto scaling enabled. Despite auto scaling, the application is still experiencing throttling during traffic spikes. Which THREE actions should the company take to resolve this issue? (Choose THREE.)

Accepted Answer

Implement exponential backoff in the application code. Option A is correct because implementing exponential backoff with jitter in the application code allows retries of throttled requests to be spread out over time, reducing sustained pressure on the table and improving success rates during spikes. Option B is correct because enabling DynamoDB Accelerator (DAX) provides an in-memory write-through cache for read-heavy workloads, offloading read traffic from the underlying table and reducing the read capacity consumed, which lowers the chance of throttling. Option C is correct because DynamoDB global tables replicate data across multiple AWS Regions and allow writes to be served in the local Region, distributing write traffic and reducing the load on any single regional table during spikes. Option D is not correct because switching to on-demand capacity mode is a valid alternative to auto scaling, but the scenario already has auto scaling enabled and the question asks for actions to resolve throttling rather than replace the capacity model. Option E is not correct because disabling auto scaling and setting fixed capacity removes the ability to adapt to traffic spikes and would typically worsen throttling, not resolve it.

Answer

Switch to on-demand capacity mode

Answer

Disable auto scaling and set fixed capacity

Question 3

A company is using Amazon Redshift and notices that queries are running slowly. Which TWO system views should be used to identify the cause of the slow queries? (Choose TWO.)

Accepted Answer

STL_QUERY. The correct answers are C and E. STL_QUERY contains detailed logs of all queries, including execution times, enabling identification of slow queries after execution. SVV_QUERY_STATE shows currently running queries, helping identify those that are running slowly. Option A (STV_TBL_PERM) is for table permissions, not performance. Option B (PG_TABLE_DEF) shows table definitions. Option D (STL_LOAD_COMMITS) shows load commit information.

Answer

STV_TBL_PERM

Answer

PG_TABLE_DEF

Answer

STL_LOAD_COMMITS

Question 4

A company runs an e-commerce platform on AWS using a multi-tier architecture. The application tier consists of Auto Scaling groups of EC2 instances behind an Application Load Balancer. The database tier uses Amazon RDS for MySQL with Multi-AZ deployment. Recently, the operations team noticed that during flash sales, the application becomes unresponsive and users receive 503 errors. The team checks CloudWatch metrics and sees that the RDS instance's CPU utilization spikes to 100%, and the `DatabaseConnections` metric also spikes to the maximum allowed value of 500. The application uses connection pooling with a maximum of 200 connections, but the metric shows 500 connections. The team suspects that the connection pooling configuration is not being honored. The application code is written in Python and uses SQLAlchemy with a connection pool size of 10 per application instance. There are 20 application instances in the Auto Scaling group during peak times. The team wants to resolve the issue without increasing the database instance size. What should the team do?

Accepted Answer

Set the `max_connections` parameter in the RDS parameter group to 200 and configure the application to handle connection errors with retry logic. Setting the `max_connections` parameter in the RDS parameter group enforces a hard limit at the database level, preventing the database from accepting more than 200 connections. With 20 application instances each using a connection pool of 10, the total intended connections are 200. By setting `max_connections` to 200, any excess connection attempts will be rejected, and the application should handle connection errors with retry logic, preventing the database from being overwhelmed. Option A is wrong: reducing the Auto Scaling group's desired capacity to 10 instances would reduce the total connection pool to 100, which might temporarily reduce load but is not a scalable solution and does not address the root cause that the application's connection pooling is not being honored. Option C is wrong: migrating to Amazon Aurora MySQL with Auto Scaling does not directly solve the connection pooling issue. Aurora may allow more connections, but the root cause is that the application is opening more connections than configured, and a migration is a costly change that doesn't fix the configuration problem. Option D is wrong: increasing the SQLAlchemy pool size per instance to 25 would increase the total potential connections to 500 (20 instances × 25), which would worsen the connection spike and exceed the database's maximum, increasing contention and load.

Answer

Reduce the Auto Scaling group's desired capacity to 10 instances during flash sales

Answer

Migrate the database to Amazon Aurora MySQL with Auto Scaling enabled

Answer

Increase the SQLAlchemy pool size to 25 per instance to reduce connection contention

Question 5

A database administrator notices that the CPU utilization on an Amazon RDS for PostgreSQL instance is consistently above 90% during peak hours. Which CloudWatch metric should be checked first to identify the cause of the high CPU usage?

Accepted Answer

DatabaseConnections. A high number of database connections can lead to increased CPU usage as each connection requires processing. Option B is wrong because NetworkThroughput measures network traffic, which is not a primary cause of high CPU. Option C is wrong because SwapUsage indicates memory pressure, not CPU. Option D is wrong because ReadIOPS measures disk I/O and may not directly cause high CPU.

Answer

NetworkThroughput

Answer

SwapUsage

Answer

ReadIOPS

Question 6

A company is running a production Amazon Aurora MySQL database. The database performance has degraded over the past week. The DBA suspects an increase in lock waits. Which tool should be used to identify queries experiencing lock waits?

Accepted Answer

Amazon RDS Performance Insights. Amazon RDS Performance Insights is the correct tool because it provides detailed database load metrics, including wait events such as lock waits, broken down by SQL statement, user, host, and database. It visualizes the database load and allows you to identify which queries are experiencing lock waits by examining the 'db.load.avg' metric filtered by wait events like 'wait/io/table/sql/handler' or 'wait/lock/table/sql/handler'. This granular, query-level insight is specifically designed for diagnosing performance issues like lock contention.

Answer

Amazon CloudWatch Logs

Answer

Amazon RDS Enhanced Monitoring

Answer

AWS Trusted Advisor

Question 7

A company is using Amazon DynamoDB with auto scaling. The application is experiencing increased read latency. The monitoring shows that the ReadThrottleEvents metric is high. Which action should the company take to reduce read throttling?

Accepted Answer

Implement DynamoDB Accelerator (DAX) for caching.. High ReadThrottleEvents indicate the table is exceeding its provisioned read capacity. DAX (DynamoDB Accelerator) is an in-memory cache that serves eventually consistent reads with microsecond latency, offloading read traffic from the table and directly reducing read throttling. It is the AWS-recommended solution for read-heavy workloads with high latency.

Answer

Switch the table to on-demand capacity mode.

Answer

Enable DynamoDB Streams on the table.

Answer

Increase the write capacity units.

Question 8

A developer reports that an application is unable to connect to an Amazon RDS for Oracle database. The security group for the database allows inbound traffic on port 1521 from the application's security group. The database is publicly accessible. What should be checked next?

Accepted Answer

Verify that the DB subnet group includes a public subnet with an internet gateway.. The database is publicly accessible, meaning it must be in a public subnet that has an internet gateway attached. Even if the security group allows traffic, the subnet must be configured correctly for public access. Option A is incorrect because the 'remote_listener' parameter affects Oracle listener registration, not basic network connectivity. Option B is incorrect because backup retention is unrelated to connection issues. Option D is incorrect because CloudWatch Logs may not capture failed connection attempts at the network level.

Answer

Check the DB parameter group for the 'remote_listener' parameter.

Answer

Check the automated backup retention period.

Answer

Review the CloudWatch Logs for error logs.

Question 9

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 10

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 11

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 12

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 13

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.. ReadThrottleEvents being high while write throttling is low indicates the read side is the bottleneck, not the write side. The table is provisioned with 1000 RCU and read throttling spikes at peak, so the fix is to add read capacity (e.g., 2000 RCU) and/or offload repeated reads with DAX caching. DAX provides an in-memory read cache that reduces consumed RCU for hot items like user profiles, directly lowering ReadThrottleEvents.

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 14

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.. The error 'remaining connection slots are reserved for non-replication superuser connections' occurs because PostgreSQL reserves superuser_reserved_connections (default 3) slots, and with max_connections=200 and 195 active connections, the application's non-superuser role cannot obtain a slot. Lambda with provisioned concurrency of 100 creates a new connection per invocation, rapidly exhausting the pool. Amazon RDS Proxy pools and multiplexes connections from Lambda, so the database sees far fewer actual connections while Lambda scales, resolving the exhaustion without changing max_connections.

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 15

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 the need to pre-provision read and write capacity, automatically scaling to accommodate the Scan's read load and the sensor write load without throttling. DynamoDB on-demand instantly adjusts to workload changes, so the Scan's RCU consumption no longer competes with writes for fixed provisioned capacity. This directly addresses the root cause: the fixed provisioned RCUs are being exhausted by the Scan, causing write throttling. On-demand is the best fit for unpredictable, spiky workloads like this batch job.

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.