DBS-C01 Practice Test 27 — 15 Questions

Question 1

A company runs a critical e-commerce application on Amazon RDS for PostgreSQL with a db.r5.2xlarge instance and 500 GB of gp2 storage. The application experiences periodic write spikes during flash sales. During these events, the WriteIOPS metric exceeds the provisioned baseline IOPS of 1,500, and the database becomes unresponsive for several seconds. The DBA has configured a CloudWatch alarm on WriteIOPS, but the alarm triggers after the performance issue occurs. The company needs to ensure that the database can handle these spikes without downtime. The budget allows for moderate cost increases. What should the DBA do?

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

Accepted Answer

Modify the DB instance to use gp3 storage with provisioned IOPS of 5,000.. Gp3 storage provides a baseline of 3,000 IOPS regardless of storage size, and allows provisioning up to 16,000 IOPS independently. By setting provisioned IOPS to 5,000, the database can handle write spikes without exceeding the IOPS limit, preventing unresponsiveness. This solution fits the moderate cost increase budget, as gp3 is typically 20% cheaper than gp2 for equivalent performance.

Answer

Increase the allocated storage to 1,000 GB to increase baseline IOPS to 3,000.

Answer

Enable Performance Insights to identify the problematic queries and tune them.

Answer

Add a read replica and redirect read traffic to it to reduce write contention.

Question 2

A company is migrating a large Oracle data warehouse to Amazon Redshift. The source database has many complex stored procedures, views, and joins. Which THREE actions should the company take during the migration?

Accepted Answer

Use the AWS Schema Conversion Tool (SCT) to convert stored procedures.. Option B is correct because AWS Schema Conversion Tool (SCT) is the AWS-recommended tool for converting Oracle PL/SQL stored procedures, functions, and views into Redshift-compatible SQL or Python/Java UDFs, since Redshift does not natively support Oracle's procedural syntax. Option D is correct because Redshift is a columnar, MPP (massively parallel processing) data warehouse optimized for sequential scans rather than complex multi-table joins, so denormalizing the schema reduces join overhead and improves query performance. Option E is correct because a star schema with fact and dimension tables is the recommended dimensional modeling approach for Redshift, aligning with its columnar storage and distribution/sort key design to minimize data movement during joins. Option A is incorrect because Redshift does not support triggers, so they cannot be migrated as-is and must be reimplemented in the application layer or via other mechanisms. Option C is incorrect because Redshift does not support traditional B-tree indexes like Oracle; instead it uses sort keys, distribution keys, and zone maps for query optimization, so Oracle indexes cannot be recreated.

Answer

Migrate triggers as they are in Redshift.

Answer

Recreate all indexes from Oracle in Redshift.

Question 3

A database specialist is troubleshooting a performance issue on an Amazon Aurora MySQL DB cluster. The cluster has a primary instance and two Aurora Replicas. The application is experiencing high read latency. Which THREE actions should the specialist take to identify the cause?

Accepted Answer

Check the Performance Insights for the Aurora Replicas to identify long-running queries.. Option C is correct because Performance Insights on the Aurora Replicas directly surfaces the top SQL statements and wait events causing high read latency, letting the specialist pinpoint long-running or inefficient queries on the read endpoints. Option D is correct because the Aurora ReplicaLag CloudWatch metric shows how far each replica trails the primary; high lag means reads may hit stale or delayed data and indicates replication pressure contributing to latency. Option E is correct because the DB Connection Count metric reveals whether the replicas are saturated by too many concurrent connections, which is a common cause of elevated read latency. Option A is not appropriate because Write Latency on the primary measures write-path performance, not the read latency the application is experiencing. Option B is not a diagnostic action; resizing the Aurora Replicas is a remediation step that should only follow root-cause identification, not part of troubleshooting to identify the cause.

Answer

Check the Write Latency metric on the primary instance.

Answer

Increase the instance size of the Aurora Replicas.

Question 4

Which THREE of the following are key considerations when designing a time-series database using Amazon DynamoDB? (Select THREE.)

Accepted Answer

Enable Time to Live (TTL) to automatically expire old data. Amazon DynamoDB's Time to Live (TTL) feature automatically deletes expired items without consuming write throughput, making it ideal for managing data retention in time-series workloads. This eliminates the need for custom cleanup scripts and reduces storage costs over time.

Answer

Always use strongly consistent reads for accurate time-series data

Answer

Use local secondary indexes for aggregating data across partitions

Question 5

Which THREE steps should be taken to troubleshoot high replica lag in an Amazon Aurora MySQL DB cluster? (Choose THREE.)

Accepted Answer

Review network latency between the writer and replica. Options A, C, and E are correct. High replica lag in Amazon Aurora MySQL can be caused by network latency (A), resource contention on the replica (C), or issues with the replication process itself (E). Option B (increasing instance size) is a mitigation, not a troubleshooting step—you diagnose first. Option D (disabling binary logging) would break replication entirely.

Answer

Increase the instance size of the replica

Answer

Disable binary logging on the writer

Question 6

A company runs a time-series application on Amazon DynamoDB. The data has a pattern of frequent writes for recent data and rare reads for older data. They want to optimize storage costs and query performance for the time-series data. Which THREE strategies should they implement? (Choose THREE.)

Accepted Answer

Use DynamoDB Time to Live (TTL) to automatically delete old data after a certain period. Option A is correct because DynamoDB TTL lets you define an attribute holding an expiration timestamp, and DynamoDB automatically deletes expired items at no write cost, which is ideal for time-series data whose recent items are hot but older items are rarely read. Option B is correct because moving historical time-series data to Amazon S3 in a columnar format (e.g., Parquet) and querying it with Amazon Athena provides cheap, serverless SQL analytics over large cold datasets without consuming DynamoDB capacity. Option D is correct because DynamoDB Streams can trigger an AWS Lambda function that archives aged items to Amazon S3 Glacier, giving low-cost long-term retention while keeping the DynamoDB table small and fast. Option C is not appropriate because blindly increasing read capacity units raises cost without addressing the underlying pattern of rare reads for old data, and the goal is cost optimization plus performance, not raw throughput. Option E is not appropriate because decreasing write capacity units would throttle the frequent writes for recent data, harming the application's primary workload rather than optimizing storage or query performance.

Answer

Increase DynamoDB read capacity units to improve query performance

Answer

Decrease DynamoDB write capacity units to reduce cost

Question 7

A company is running a production Amazon DynamoDB table with on-demand capacity. The application is experiencing increased latency and throttled requests during peak hours. Which monitoring tool should the database specialist use to identify the specific partition keys causing the throttling?

Accepted Answer

Amazon CloudWatch Contributor Insights for DynamoDB. Amazon CloudWatch Contributor Insights for DynamoDB is the correct tool because it analyzes DynamoDB request logs to identify the most frequently accessed partition keys, including those causing throttling. It provides top-N keys by request count or throttled events, enabling the database specialist to pinpoint hot partitions responsible for increased latency and throttled requests during peak hours.

Answer

Amazon Inspector

Answer

AWS Config

Answer

AWS CloudTrail logs

Question 8

A company's security policy requires that all database passwords be rotated every 90 days. The company uses AWS Secrets Manager to store database credentials for Amazon RDS. Which feature can be used to automate password rotation?

Accepted Answer

Configure automatic rotation in Secrets Manager with a rotation interval of 90 days.. Secrets Manager has built-in rotation support for RDS databases. Option B is wrong because Lambda can be used but is not a feature of Secrets Manager itself; the managed rotation is the simplest. Option C is wrong because IAM Access Analyzer is for analyzing resource policies, not password rotation. Option D is wrong because Systems Manager Parameter Store does not have built-in rotation for RDS.

Answer

Use an AWS Lambda function triggered by Amazon CloudWatch Events every 90 days to rotate the password.

Answer

Use IAM Access Analyzer to detect unused passwords and rotate them.

Answer

Store the password in AWS Systems Manager Parameter Store and use automatic rotation.

Question 9

A company is using Amazon ElastiCache for Redis as a caching layer. The application performance degrades when cache misses increase. Which metric should be monitored to track the cache hit rate?

Accepted Answer

CacheHits and CacheMisses. The cache hit rate is calculated directly from the CacheHits and CacheMisses CloudWatch metrics — hit rate = CacheHits / (CacheHits + CacheMisses). Monitoring both is the only way to quantify how often the application is served from Redis versus forced to fall back to the origin database. This is the standard ElastiCache metric pair for diagnosing cache effectiveness.

Answer

CurrConnections

Answer

CPUUtilization

Answer

Evictions

Question 10

A company is deploying a new application with a PostgreSQL database on Amazon RDS. The database must be highly available across two Availability Zones. Which deployment option meets this requirement?

Accepted Answer

Multi-AZ RDS instance.. A Multi-AZ RDS instance automatically provisions and maintains a synchronous standby replica in a different Availability Zone, providing automatic failover for high availability. This ensures the database remains accessible even if the primary AZ fails, meeting the requirement for cross-AZ high availability.

Answer

RDS with a read replica in another AZ.

Answer

Single-AZ RDS instance.

Answer

Amazon Aurora Global Database.

Question 11

A company uses Amazon DynamoDB with auto scaling enabled. During a flash sale, write traffic spikes and some requests receive ThrottlingException errors. The table's write capacity auto scaling reaches the maximum configured capacity but still throttles. Which TWO actions should resolve the throttling? (Choose TWO.)

Accepted Answer

Increase the maximum write capacity setting for the auto scaling policy.. Option A is correct because the throttling occurs precisely because the table's write capacity auto scaling has hit its configured maximum, so raising that upper limit allows the scaling policy to provision more WCUs and absorb the flash-sale spike. Option B is correct because even with sufficient capacity, a poorly chosen partition key can create hot partitions that throttle individual partitions regardless of total table capacity, so reviewing the key design for even access distribution addresses this root cause. Option C is not correct because DAX is a read-through/write-through cache that accelerates read-heavy workloads and does not increase write throughput or eliminate write throttling. Option D is not correct because DynamoDB Streams captures change records for downstream processing and does not offload or reduce the write traffic hitting the table. Option E is not correct because disabling auto scaling and switching to on-demand is not the recommended fix here; the scenario already has auto scaling that simply needs a higher ceiling, and the phrasing of the option is technically off since on-demand mode replaces provisioned capacity rather than being used alongside disabled auto scaling.

Answer

Add a DynamoDB Accelerator (DAX) cluster to cache writes.

Answer

Enable DynamoDB Streams to offload write traffic.

Answer

Disable auto scaling and use On-Demand capacity mode.

Question 12

A SysOps administrator is tasked with monitoring the free storage space on all Amazon RDS DB instances. Which AWS service should be used to set up an alarm that sends an email notification when free storage space falls below a threshold?

Accepted Answer

Amazon CloudWatch with an alarm on the FreeStorageSpace metric and an SNS topic.. Amazon CloudWatch provides the `FreeStorageSpace` metric for RDS DB instances, which reports the amount of available storage in bytes. You can create a CloudWatch alarm on this metric and configure it to send a notification via an Amazon SNS topic when the value falls below a specified threshold, enabling proactive monitoring of storage capacity.

Answer

AWS CloudTrail to monitor storage events.

Answer

AWS Config to track storage configuration changes.

Answer

Amazon Inspector to check for storage vulnerabilities.

Question 13

A company has an Amazon Aurora MySQL DB cluster with a single writer and two readers. The writer instance fails, and the failover mechanism promotes one of the readers to writer. The application, which uses a custom connection pool, continues to experience errors for several minutes. What should the database administrator do to minimize downtime during future failovers?

Accepted Answer

Modify the application to use the cluster endpoint instead of the instance endpoint.. Aurora's cluster endpoint always points to the current writer instance and is automatically updated during failover. If the application connects to the instance endpoint of the old writer, it will keep trying to reach a now-reader (or failed) instance, causing errors until DNS or the pool refreshes. Switching to the cluster endpoint lets the connection pool reconnect to the new writer with minimal disruption.

Answer

Increase the connection pool size to handle more connections.

Answer

Configure the application to use the reader endpoint for all traffic.

Answer

Enable Multi-AZ on the Aurora cluster.

Question 14

A company uses Amazon RDS for PostgreSQL to store sensor data. Each sensor sends a row every second. The table has grown to 500 GB and queries filtering on a timestamp column are slow even with an index. The team wants to improve query performance while keeping the data online. Which approach should they take?

Accepted Answer

Partition the table by time using PostgreSQL table partitioning. PostgreSQL table partitioning by time (e.g., by day or month) allows the query planner to prune partitions that do not match the timestamp filter, drastically reducing the amount of data scanned. This is the most effective approach for time-series data because it maintains online access and improves query performance without requiring a migration or additional replicas.

Answer

Migrate to Amazon Aurora PostgreSQL and enable parallel query

Answer

Add more indexes on the timestamp column

Answer

Create a read replica and direct queries to the replica

Question 15

Which TWO factors should be considered when designing a database for an IoT workload that ingests millions of sensor readings per second? (Choose 2.)

Accepted Answer

Implement data retention and aggregation to reduce storage costs. IoT workloads generate massive volumes of data, and implementing data retention policies (e.g., automatically deleting raw data after a set period) combined with aggregation (e.g., downsampling sensor readings into hourly or daily averages) directly reduces storage costs. This is a core design pattern for time-series databases like Amazon Timestream, which supports automatic retention and aggregation via scheduled queries or rollups.

Answer

Ensure strong consistency for all reads

Answer

Enforce ACID transactions for all writes

Answer

Use a graph database to model relationships between sensors