DBS-C01 Mock Exam 83 — 25 Questions

Question 1

A company is migrating an on-premises Oracle database to Amazon RDS for Oracle. During the migration, the database administrator notices that the CPU utilization on the RDS instance is consistently above 90% during peak hours, even though the on-premises server had similar specifications. The application queries are mostly SELECT statements with occasional DML. The RDS instance is db.r5.large with 500 GB of General Purpose SSD (gp2) storage. Which change would most likely reduce CPU utilization?

Accepted Answer

Upgrade to a larger instance type, such as db.r5.xlarge.. The db.r5.large instance type has 2 vCPUs and 16 GiB of memory. Sustained CPU utilization above 90% during peak hours indicates that the instance is compute-bound for the workload. Upgrading to db.r5.xlarge (4 vCPUs, 32 GiB memory) doubles the available CPU capacity, directly reducing CPU utilization for the same query load. The on-premises server had similar specifications, but RDS instances may have different CPU architectures or hypervisor overhead, making the larger instance the most direct fix.

Answer

Create a read replica and redirect all SELECT queries to the replica.

Answer

Enable Multi-AZ to offload CPU to the standby instance.

Answer

Increase the allocated storage to 1 TB to improve I/O performance.

Question 2

A gaming company uses Amazon DynamoDB as the primary database for their player sessions. The player sessions table has a partition key of 'player_id' and a sort key of 'session_start_time'. The application frequently queries for recent sessions of a specific player, using the query API with 'player_id' and a filter on 'session_start_time' for the last 24 hours. The average item size is 5 KB. The company notices high latency on these queries during peak hours. The table has 10 Read Capacity Units (RCUs) provisioned. There are no indexes. Which design change would MOST improve query performance?

Accepted Answer

Create a Local Secondary Index (LSI) with the same partition key and a sort key of 'session_start_time', and query the index instead of the table.. Option C is correct because creating a Local Secondary Index (LSI) with the same partition key (player_id) and sort key (session_start_time) allows DynamoDB to efficiently retrieve items for a specific player sorted by session_start_time without scanning and filtering. The current query uses a filter on session_start_time after retrieving all sessions for the player, which wastes read capacity and increases latency. Querying the LSI directly uses the sort key to limit the data read to only the last 24 hours, reducing the read footprint and improving performance.

Answer

Increase the RCUs to 100.

Answer

Add a random suffix to the partition key values to distribute writes across partitions.

Answer

Create a Global Secondary Index (GSI) with partition key 'event_type' and sort key 'session_start_time' and query the GSI.

Question 3

A company is using Amazon DynamoDB with autoscaling enabled. The table has a partition key of 'order_id' and a sort key of 'order_date'. The application performs both point queries and range queries. Recently, the 'ConsumedReadCapacityUnits' metric shows that the table is consistently using 100% of the provisioned capacity. Which THREE factors should the database engineer investigate to determine the cause?

Accepted Answer

Whether the application is using Scan operations instead of Query operations.. Option B is correct because Scan operations read the entire table or index before applying filters, consuming far more read capacity than Query operations, which target specific partition and sort key values. If the application is using Scans instead of Queries, it would consistently consume 100% of provisioned capacity even for small result sets, leading to throttling and high utilization.

Answer

Whether autoscaling is configured correctly to add capacity.

Answer

Whether a global secondary index is being used for queries.

Question 4

A company is planning to migrate a 2 TB Oracle database to Amazon Aurora PostgreSQL. The database contains large objects (LOBs) and uses stored procedures. Which TWO steps should the company take as part of the migration? (Choose two.)

Accepted Answer

Use AWS Schema Conversion Tool (SCT) to convert the Oracle schema and stored procedures to PostgreSQL-compatible format.. Option B is correct because the AWS Schema Conversion Tool (SCT) is specifically designed to convert Oracle schema objects, including stored procedures, to PostgreSQL-compatible format. Since the migration involves stored procedures, SCT is necessary to handle the syntax and logic differences between Oracle PL/SQL and PostgreSQL PL/pgSQL, ensuring the target database schema is ready for migration.

Answer

Set up AWS DMS ongoing replication to keep the target in sync after migration.

Answer

Use AWS DMS native Oracle to PostgreSQL endpoint to migrate data without schema conversion.

Answer

Enable RDS Proxy to reduce connection overhead during migration.

Question 5

A media company is storing large video files (up to 10 GB each) in Amazon S3 and needs to maintain metadata about each file, including title, duration, and upload timestamp. The workload involves frequent writes (1000+ per second) and occasional read queries by title. Which database is best suited for this metadata store?

Accepted Answer

Amazon DynamoDB. Amazon DynamoDB is the best choice because it supports single-digit millisecond latency at any scale, handles over 1000 writes per second with auto-scaling, and can efficiently serve occasional read queries by title using a global secondary index (GSI) on the title attribute. Its fully managed, serverless nature eliminates operational overhead for high-throughput metadata storage.

Answer

Amazon Neptune

Answer

Amazon RDS for MySQL

Answer

Amazon ElastiCache for Memcached

Question 6

A company is running a production Amazon RDS for PostgreSQL database. The database experiences high write latency during peak hours. The company wants to reduce latency without changing the application code. Which solution is MOST cost-effective and scalable?

Accepted Answer

Increase the instance size to a larger DB instance class. Increasing the instance size to a larger DB instance class directly addresses high write latency by providing more CPU and memory resources, which improves the database's ability to process write operations faster. This is the most cost-effective and scalable solution because it does not require application code changes and can be scaled vertically as needed, whereas other options either do not reduce write latency or introduce unnecessary complexity.

Answer

Change the storage type to Provisioned IOPS (io1)

Answer

Enable RDS Proxy to pool database connections

Answer

Add a Multi-AZ standby to offload writes

Question 7

A company runs a document management system using Amazon DocumentDB (with MongoDB compatibility). The application stores large documents (up to 5 MB each) and frequently fetches them by document ID. The team notices increased latency during peak hours. They need to reduce read latency. Which action is MOST effective?

Accepted Answer

Add read replicas to the cluster. Adding read replicas to the DocumentDB cluster is the most effective action because it offloads read traffic from the primary instance, directly reducing read latency during peak hours. DocumentDB supports up to 15 read replicas that are kept in sync via the cluster's replication mechanism, and the application's frequent fetches by document ID are read-heavy operations that benefit from distributing the load across multiple replicas.

Answer

Shard the collection across multiple DocumentDB clusters

Answer

Implement Amazon ElastiCache for Redis in front of DocumentDB

Answer

Increase the instance class of the primary instance

Question 8

Which TWO of the following are valid considerations when migrating an on-premises Oracle database to Amazon RDS for Oracle using AWS DMS? (Select TWO.)

Accepted Answer

DMS can continue to replicate changes after the full load is complete until the cutover.. AWS DMS supports ongoing replication (change data capture, CDC) after the full load completes, allowing you to keep the target database synchronized with the source until you perform the cutover. This minimizes downtime because you can replicate ongoing changes from the on-premises Oracle database to Amazon RDS for Oracle, then stop all applications and switch over with only a brief interruption.

Answer

DMS automatically converts Oracle stored procedures to RDS for Oracle compatible code.

Answer

DMS automatically converts partitioned tables to non-partitioned tables.

Answer

DMS can migrate directly to Amazon RDS Custom for Oracle without any configuration changes.

Question 9

A company needs to build a reporting dashboard that queries a large dataset stored in Amazon S3. The queries are complex and require full SQL support. Which service should be used?

Accepted Answer

Amazon Athena. Amazon Athena is the correct choice because it is a serverless, interactive query service that allows you to analyze data directly in Amazon S3 using standard SQL. It supports complex queries, including joins, window functions, and CTEs, and requires no infrastructure management, making it ideal for ad-hoc reporting on large datasets in S3.

Answer

Amazon Redshift Spectrum

Answer

Amazon RDS

Answer

Amazon DynamoDB

Question 10

A mobile gaming application uses Amazon DynamoDB to store player profiles and game state. The write throughput is high during events, but low otherwise. The company wants to minimize costs while maintaining performance. Which capacity mode should they use?

Accepted Answer

On-Demand capacity mode. On-Demand capacity mode is ideal for this workload because it automatically scales to handle high write throughput during events and scales down to zero when idle, eliminating the need for capacity planning. This minimizes costs by charging only for actual reads and writes, without requiring any provisioning or management of throughput limits.

Answer

Reserved capacity

Answer

Provisioned capacity without auto scaling

Answer

Provisioned capacity with auto scaling

Question 11

Which TWO AWS services can be used to monitor database security events in real time? (Choose 2.)

Accepted Answer

Amazon GuardDuty. Amazon GuardDuty (Option B) is a threat detection service that continuously monitors for malicious activity and unauthorized behavior, including database security events such as suspicious API calls or potential credential compromise. It uses machine learning, anomaly detection, and integrated threat intelligence to analyze AWS CloudTrail logs, VPC Flow Logs, and DNS logs in real time, making it suitable for monitoring database security events.

Answer

AWS WAF

Answer

Amazon RDS Performance Insights

Answer

AWS CloudTrail

Question 12

Which TWO of the following are best practices for designing a DynamoDB table for high traffic? (Choose 2)

Accepted Answer

Use global secondary indexes for alternate access patterns. Global secondary indexes (GSIs) allow you to define alternate partition and sort keys to support different query patterns without duplicating data. This is a best practice for high-traffic workloads because it enables efficient access to data using multiple access patterns while maintaining a single base table, reducing the need for expensive scans or application-level joins.

Answer

Store large items to reduce the number of items

Answer

Use normalized tables and perform joins in application

Answer

Always use strongly consistent reads for best performance

Question 13

Which TWO design patterns are commonly used to handle hot partitions in Amazon DynamoDB? (Choose 2.)

Accepted Answer

Write sharding. Write sharding distributes writes across multiple partition keys to prevent a single partition from exceeding the 1,000 WCU limit. Adding random suffixes to partition keys is a specific write sharding technique that spreads writes across many partitions, avoiding hot spots.

Answer

Decreasing write capacity units

Answer

Using a single partition key

Answer

Increasing read capacity units

Question 14

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. Option B is correct because 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 15

A company is deploying an Amazon Aurora MySQL database with read replicas. They need to ensure high availability and disaster recovery across AWS Regions. Which THREE components should be included?

Accepted Answer

Aurora Replicas in the same Region. Aurora Replicas in the same Region provide read scaling and automatic failover within an AWS Region. They share the same underlying storage volume as the primary instance, so failover is fast (typically under 30 seconds) and does not require data rehydration. This is a core component for high availability within a single Region.

Answer

Auto Scaling groups

Answer

Amazon CloudFront

Question 16

A media streaming company uses Amazon DynamoDB as the primary data store for user session information. The table has a partition key of 'user_id' (String) and a sort key of 'session_start_time' (Number). The table is provisioned with 5000 read capacity units (RCU) and 5000 write capacity units (WCU). Over the past week, the company noticed occasional throttling on writes during peak hours, even though the average consumed WCU is only 3500. The DynamoDB table also experiences hot partitions. The development team is considering using DynamoDB Accelerator (DAX) to cache read-heavy workloads, but the issue is with writes. The database administrator needs to resolve the write throttling and hot partitions. Which action should the administrator take?

Accepted Answer

Enable DynamoDB adaptive capacity to automatically handle uneven access patterns.. Option A is correct because adaptive capacity automatically adjusts throughput to handle uneven access patterns, helping with hot partitions. Option B is wrong because reducing WCU would worsen throttling. Option C is wrong because enabling auto scaling adjusts capacity but doesn't address hot partitions directly. Option D is wrong because DAX caches reads, not writes.

Answer

Deploy DAX to cache frequent writes and reduce write load.

Answer

Reduce the provisioned WCU to match the average consumption to save costs.

Answer

Enable DynamoDB auto scaling for write capacity to scale based on demand.

Question 17

An e-learning platform uses Amazon Aurora MySQL for its database. The application runs reporting queries that scan large portions of the database, causing high CPU utilization on the primary instance. The primary instance is a db.r5.2xlarge with 64 GB memory. The reporting queries are not time-sensitive but need to return results within 5 minutes. The operations team wants to reduce the impact on the primary instance without increasing costs significantly. Which action should be taken?

Accepted Answer

Create an Aurora Replica and configure the reporting application to connect to the replica endpoint. Option D is correct because creating an Aurora Replica and directing reporting traffic to it offloads the primary instance. Option A is wrong because increasing the instance class is costly and may not be needed. Option B is wrong because ElastiCache is for caching, not for heavy reporting queries. Option C is wrong because the parameter group change would affect all queries, not just reporting.

Answer

Modify the DB cluster parameter group to enable result set caching

Answer

Use Amazon ElastiCache to cache the reporting results

Answer

Increase the primary instance to db.r5.4xlarge

Question 18

A financial services company runs a production Amazon Aurora MySQL database cluster (1 writer, 2 readers) in us-east-1. The database stores critical trading data. The company's disaster recovery policy requires an RPO of 5 seconds and an RTO of 1 minute for a regional failure. The current setup does not include any cross-region replication. The database is 5 TB in size. The operations team needs to implement a solution that meets the DR requirements with minimal cost and operational overhead. Which solution should the team implement?

Accepted Answer

Deploy an Aurora Global Database with a secondary cluster in us-west-2. Configure the secondary cluster as a failover target.. Aurora Global Database provides replication across regions with typical RPO of seconds and RTO of minutes. It is the most appropriate solution for low RPO/RTO with minimal overhead. Option B is correct. Option A (cross-region read replicas) can have replication lag exceeding 5 seconds. Option C (automated backups) have RPO of 5 minutes. Option D (DMS) adds cost and complexity.

Answer

Enable automated backups and configure cross-Region snapshot copy. Use point-in-time recovery in the secondary region.

Answer

Use AWS Database Migration Service (DMS) to continuously replicate changes to an Aurora cluster in us-west-2.

Answer

Create cross-Region read replicas in us-west-2 and set up a replication channel. In disaster, promote a replica.

Question 19

A social media company runs a large Amazon DynamoDB table (Users) with 10 TB of data. The table uses on-demand capacity. Recently, the application started experiencing occasional ProvisionedThroughputExceeded exceptions during read-heavy periods. The table's read metrics show that consumed read capacity is consistently below 50% of provisioned capacity. The application uses eventually consistent reads. The table has a global secondary index (GSI) on the 'status' attribute. The GSI's read capacity consumption is often at 100% of its provisioned capacity. What is the most likely cause of the throttling?

Accepted Answer

The GSI is throttling because its read capacity is not set to on-demand, or the GSI's partition key is causing hot partitions.. The GSI has its own read capacity settings. If the GSI is throttled, reads on the base table that require the GSI may also be throttled. Option B is correct. Option A is unlikely because base table RCU is not maxed. Option C is incorrect because on-demand auto scaling does not apply to GSIs separately in the same way. Option D is incorrect because eventual consistency does not cause throttling.

Answer

The on-demand capacity mode is not suitable for this workload; switch to provisioned with auto scaling.

Answer

The base table's read capacity is insufficient despite on-demand mode.

Answer

The application should use strongly consistent reads to reduce throttling.

Question 20

A gaming company uses Amazon Aurora PostgreSQL for its leaderboard data. The database has a writer instance and two reader instances. The application frequently queries the leaderboard to display top players. The queries involve sorting and aggregation on a large table with millions of rows. Recently, the queries started timing out during peak hours. The database administrator analyzed the workload and found that the reader instances are underutilized, while the writer instance has high CPU due to write operations. The administrator wants to offload the read-heavy leaderboard queries to the readers without modifying the application code. What should the administrator do?

Accepted Answer

Configure the application to use the Aurora reader endpoint for read-only queries.. Option C is correct because Aurora auto-scaling reader endpoints distribute connections across readers; the application can use the reader endpoint for read-only queries. Option A is wrong because read replicas are already present. Option B is wrong because upgrading the writer does not offload reads. Option D is wrong because creating a new cluster is unnecessary.

Answer

Upgrade the writer instance to a larger instance class to handle both read and write operations.

Answer

Create additional reader instances to distribute the read load.

Answer

Implement a read-only Aurora cluster and point read queries to it.