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AWS Certified Database Specialty DBS-C01 (DBS-C01) — Questions 1351–1425

1663 questions total · 23pages · All types, answers revealed

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1351
MCQmedium

A company is using Amazon DynamoDB with auto scaling enabled. The application is experiencing higher than expected write throttling. Which action should be taken to resolve this issue?

A.Increase the minimum provisioned capacity for the table.
B.Disable auto scaling and set a fixed provisioned capacity.
C.Decrease the maximum provisioned capacity to limit writes.
D.Switch the table to on-demand capacity mode.
AnswerA

Increasing the minimum capacity ensures that the table can handle baseline traffic and reduces the chance of throttling during spikes.

Why this answer

Auto scaling adjusts capacity based on workload, but it can lag behind sudden traffic spikes, causing write throttling. Increasing the minimum provisioned capacity ensures a baseline capacity that can accommodate predictable bursts, reducing throttling. Option A (increase min capacity) is correct.

Option B (disable auto scaling and set fixed capacity) would remove the benefit of dynamic scaling and may not handle varying loads. Option C (decrease max capacity) would limit the table's ability to scale up, potentially worsening throttling. Option D (switch to on-demand) could eliminate throttling but at higher cost, and the question asks for a resolution while keeping auto scaling enabled.

Full explanation →
1352
MCQhard

A company runs a critical e-commerce application on Amazon RDS for MySQL with Multi-AZ enabled. The database is 2 TB and uses General Purpose (gp2) storage. Recently, during peak hours, the application experienced a 5-minute outage. The CloudWatch logs show that the primary DB instance failed and an automatic failover occurred. However, the failover took 3 minutes, which is longer than the expected 1-2 minutes. The 'ReadLatency' and 'WriteLatency' metrics were elevated before the failure. The 'BurstBalance' metric was at 0% for the hour before the failure. The team suspects the issue is related to storage performance. What should the team do to prevent this issue in the future?

A.Increase the DB instance class to a larger size.
B.Change the storage type to Provisioned IOPS (io1).
C.Increase the backup retention period to 35 days.
D.Create a read replica to offload read traffic.
AnswerB

Provisioned IOPS provides consistent I/O performance and avoids burst credit exhaustion.

Why this answer

The BurstBalance at 0% indicates the gp2 volume exhausted its burst credits, causing I/O throttling and increased latency, which likely contributed to the failover delay. Switching to Provisioned IOPS (io1) provides consistent performance and avoids burst credit exhaustion. Option A is wrong because increasing the DB instance class addresses compute capacity, not storage I/O.

Option C is wrong because backup retention period does not affect storage performance. Option D is wrong because read replicas offload read traffic but do not improve write performance on the primary instance.

Full explanation →
1353
MCQeasy

A database administrator is monitoring an Amazon RDS for SQL Server DB instance and notices that the FreeableMemory metric is consistently below 200 MB. Which of the following actions is most appropriate to mitigate performance issues?

A.Modify the DB instance's maintenance window to off-peak hours
B.Disable the SQL Server Agent and error logging
C.Enable automatic backups with a shorter retention period
D.Scale up the DB instance to a larger instance class with more memory
AnswerD

Scaling up to a larger instance class with more memory directly addresses the low FreeableMemory by increasing the total memory available to the instance, which is the most appropriate action.

Why this answer

A low FreeableMemory metric consistently below 200 MB indicates memory pressure on the RDS for SQL Server DB instance. The most effective mitigation is to scale up to a larger instance class with more memory, directly addressing the resource shortage. Option D is correct because it increases available memory.

Option B is incorrect because disabling SQL Server Agent and error logging does not free significant memory and may disrupt essential operations. Option A is incorrect because changing the maintenance window does not affect memory usage. Option C is incorrect because enabling backups with a shorter retention period does not impact memory.

Exam trap

Candidates may incorrectly assume that disabling background processes like SQL Server Agent or changing maintenance windows can reduce memory pressure, but these actions have negligible impact on memory usage. The direct solution is to increase memory by scaling the instance.

Full explanation →
1354
Multi-Selecteasy

A company is designing a database for a global application that requires low-latency reads and writes across multiple AWS regions. The application data is key-value and does not require complex queries. The team needs strong consistency for critical data. Which TWO services should they consider? (Choose TWO.)

Select 2 answers
A.Amazon DynamoDB Global Tables
B.Amazon S3 with cross-region replication
C.Amazon ElastiCache for Redis with global datastore
D.Amazon Aurora Global Database
E.Amazon RDS for PostgreSQL with cross-region read replicas
AnswersA, D

DynamoDB Global Tables replicate data across regions and support strong consistency.

Why this answer

Amazon DynamoDB Global Tables is correct because it provides a fully managed, multi-region, multi-active database solution that replicates data across AWS Regions with low-latency reads and writes. It supports strongly consistent reads for critical data when using the `ConsistentRead` parameter, which returns the most up-to-date data from the source region. This makes it ideal for key-value workloads requiring global scalability and strong consistency.

Exam trap

The trap here is that candidates often confuse 'global datastore' (ElastiCache for Redis) with a fully managed multi-region database, not realizing it provides only eventual consistency and is not designed for durable, strongly consistent critical data.

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1355
Multi-Selectmedium

Which TWO methods can be used to reduce the read latency for an Amazon Aurora MySQL database? (Choose 2.)

Select 2 answers
A.Enable encryption at rest
B.Use Aurora Auto Scaling to add replica capacity based on load
C.Increase the write capacity of the DB instance
D.Enable Amazon ElastiCache in front of the database
E.Add Aurora Replicas to offload read traffic
AnswersB, E

Auto Scaling ensures sufficient replicas to handle read traffic.

Why this answer

Aurora Auto Scaling automatically adjusts the number of Aurora Replicas in response to changes in read workload, thereby reducing read latency by distributing read traffic across additional replicas. Option E is correct because adding Aurora Replicas offloads read queries from the primary instance, allowing parallel processing of read requests and reducing contention, which directly lowers read latency.

Exam trap

The trap here is that candidates may confuse write scaling (Option C) with read scaling, or assume that encryption (Option A) or external caching (Option D) are native Aurora methods, when the exam expects knowledge of Aurora-specific read scaling features like Aurora Replicas and Auto Scaling.

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1356
MCQeasy

A startup is building a social media application that stores user posts in Amazon DynamoDB. The access pattern is to retrieve posts by user_id (partition key) sorted by post_timestamp (sort key) in descending order. The table has a global secondary index (GSI) with the same key structure but with different projection. The application reads from the GSI. Recently, the team noticed that writes to the base table are throttled during peak hours. The write capacity is balanced across partitions. Which design change should be made to reduce write throttling?

A.Use DynamoDB Accelerator (DAX) for writes.
B.Increase the write capacity units (WCUs) on the base table.
C.Switch to on-demand capacity mode.
D.Add a write sharding pattern by appending a random suffix to the partition key.
AnswerD

Sharding distributes writes across partitions, reducing hot spots.

Why this answer

The write throttling is caused by a hot partition, where a single partition key (user_id) receives a disproportionate number of writes. By appending a random suffix to the partition key, the writes are distributed evenly across multiple partitions, eliminating the hot spot. This is a well-known sharding pattern for DynamoDB when access patterns create uneven write traffic, and it does not require changing the read logic because the GSI can be queried with a sort key condition on post_timestamp.

Exam trap

The trap here is that candidates often assume increasing capacity or switching to on-demand mode will solve all throttling issues, but they overlook the fundamental partition-level throughput limits that cause hot partition throttling.

How to eliminate wrong answers

Option A is wrong because DAX is an in-memory cache for reads, not writes; it does not increase write capacity or reduce write throttling. Option B is wrong because increasing WCUs on the base table would not resolve the underlying hot partition issue; throttling occurs at the partition level, and if one partition is overloaded, adding more capacity to the table does not help because the partition's throughput limit is fixed. Option C is wrong because switching to on-demand capacity mode would only handle unpredictable traffic patterns, but it does not solve the hot partition problem; on-demand still has per-partition throughput limits (3,000 RCU or 1,000 WCU per partition), and a single hot partition can still throttle writes.

Full explanation →
1357
MCQeasy

A database administrator notices that an Amazon RDS for SQL Server DB instance has been in the 'storage-optimization' state for several hours after modifying the storage type from gp2 to io1. What should the administrator do to resolve this?

A.Wait for the storage optimization to complete.
B.Restore from the latest snapshot and reapply the modification.
C.Cancel the modification by modifying the DB instance back to gp2.
D.Reboot the DB instance.
AnswerA

Storage optimization is automatic and takes time; no action is needed.

Why this answer

The 'storage-optimization' state is a normal part of the modification process when converting between storage types on RDS. It can take hours as the database migrates to the new storage configuration. The administrator should simply wait for the process to complete.

Options B, C, and D are incorrect: restoring from a snapshot would lose recent changes and requires reapplication of the modification, modifying back to gp2 would interrupt the process and still require time, and rebooting does not affect the storage optimization.

Exam trap

Candidates might think that rebooting or restoring from a snapshot could speed up the process, but these actions are either unnecessary or disruptive.

Full explanation →
1358
MCQhard

A company runs a production Amazon RDS for PostgreSQL database with automated backups enabled. A database administrator accidentally dropped a critical table. The administrator wants to restore the table from a point in time before the drop. The database is 1 TB in size and the recovery point objective (RPO) is 5 minutes. Which approach minimizes downtime?

A.Use the point-in-time recovery feature to restore the database to a new DB instance at a time before the drop, then use pg_dump to export the table and import it into the production database.
B.Restore the automated backup from S3 to a new EC2 instance running PostgreSQL, then export the table and import it into the production database.
C.Restore the database from the most recent manual snapshot to a new instance, then use pg_dump to extract the table and import it into the production database.
D.Create a read replica from the production database, stop replication, and use pg_dump to extract the table from the replica and import it into the production database.
AnswerA

PITR allows restore to any second within the backup retention period, minimizing data loss and downtime by restoring to a new instance.

Why this answer

Amazon RDS Point-in-Time Recovery (PITR) allows restoring to any second within the backup retention window, enabling a restore to just before the table was dropped. After restoring to a new DB instance, pg_dump can export the specific table, and then pg_restore or psql can import it into the production database. This minimizes downtime by avoiding a full database restore and only moving the single dropped table.

Exam trap

The trap here is that candidates may think a read replica or manual snapshot can recover a dropped table, but they fail to realize that the drop operation is replicated to the replica and that manual snapshots may not meet the required RPO.

How to eliminate wrong answers

Option B is wrong because automated backups are stored as system snapshots and transaction logs within RDS, not as raw files accessible directly from S3; you cannot restore an RDS automated backup to an EC2 instance running PostgreSQL. Option C is wrong because restoring from the most recent manual snapshot may not capture a point in time close enough to the drop event, potentially exceeding the 5-minute RPO and requiring more data loss. Option D is wrong because creating a read replica from the production database after the table has been dropped will replicate the drop, so the replica will also be missing the table; stopping replication does not recover the dropped data.

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1359
MCQhard

A company has an Amazon RDS for SQL Server DB instance with Multi-AZ deployment. During a recent failover test, the application experienced a longer downtime than expected. The application uses a single connection string. What change should be made to reduce failover downtime?

A.Implement connection pooling in the application.
B.Use a custom DNS CNAME record pointing to the RDS endpoint.
C.Set the DNS TTL to a higher value.
D.Configure the application to use the RDS instance ID instead of endpoint.
AnswerB

CNAME allows DNS update after failover, reducing downtime.

Why this answer

Using a custom DNS CNAME record that points to the RDS endpoint allows the application to control the DNS Time-To-Live (TTL) value independently. By setting a low TTL (e.g., 5 seconds) on the CNAME, the application's DNS resolver will refresh the IP address more quickly after a failover, reducing the time the application spends trying to connect to the old, unreachable primary instance. This minimizes downtime because the application can resolve the new primary's IP address sooner, rather than relying on the default RDS endpoint's TTL, which is typically set to 60 seconds and cannot be modified.

Exam trap

The trap here is that candidates often think connection pooling (Option A) reduces failover downtime, but it actually addresses connection overhead, not DNS resolution delays, which is the primary cause of extended downtime during a Multi-AZ failover.

How to eliminate wrong answers

Option A is wrong because connection pooling reuses existing database connections to reduce overhead, but it does not affect how quickly the application detects a DNS change or reconnects after a failover; it may even keep stale connections alive longer. Option C is wrong because setting the DNS TTL to a higher value would increase the time the application caches the old IP address, thereby extending downtime after a failover, not reducing it. Option D is wrong because the RDS instance ID is not a DNS-resolvable endpoint; the application must use the RDS endpoint (or a custom CNAME) to connect, and the instance ID alone cannot be used in a connection string.

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1360
MCQeasy

A company is migrating an on-premises MongoDB database to Amazon DocumentDB (with MongoDB compatibility). They want to validate that the data is consistent after migration. Which tool should they use?

A.mongoexport and mongoimport
B.dbHash command on both databases
C.DocumentDB native consistency check tool
D.AWS DMS data validation
AnswerD

DMS can validate data between source and target.

Why this answer

AWS DMS data validation is the correct tool because it provides built-in, row-level checksum-based validation that compares source and target data after a full load or ongoing replication. For DocumentDB migrations, DMS can compute and compare checksums on the fly, ensuring consistency without requiring manual scripting or external tools. This is the recommended AWS approach for validating data integrity during and after a migration to DocumentDB.

Exam trap

The trap here is that candidates assume a native MongoDB command like dbHash or a generic export/import tool can validate consistency across different database engines, but DocumentDB does not support dbHash and AWS DMS is the only service-integrated validation method for cross-engine migrations.

How to eliminate wrong answers

Option A is wrong because mongoexport and mongoimport are data export/import utilities, not validation tools; they cannot compare existing data in both databases for consistency. Option B is wrong because the dbHash command computes a hash of all data in a MongoDB instance, but DocumentDB does not support the dbHash command natively, and it would require custom scripting to compare hashes across different database engines. Option C is wrong because there is no native 'DocumentDB native consistency check tool' — DocumentDB relies on AWS DMS or manual methods for consistency validation, not a built-in tool.

Full explanation →
1361
Multi-Selectmedium

A company is migrating a 1 TB on-premises SQL Server database to Amazon RDS for SQL Server. The migration must be completed within 24 hours and with minimal downtime. Which TWO approaches should be used? (Choose 2)

Select 2 answers
A.Use AWS DMS with SSIS packages.
B.Use native backup and restore to S3.
C.Use AWS DMS with ongoing replication (CDC).
D.Use AWS SCT to assess and convert the schema.
E.Use AWS DMS full load only.
AnswersC, D

Near-zero downtime migration.

Why this answer

AWS DMS with ongoing replication (CDC) allows you to perform a full load of the existing data and then continuously replicate changes from the source SQL Server database to Amazon RDS for SQL Server, minimizing downtime to just the final cutover window. This approach meets the 24-hour migration window and the requirement for minimal downtime by keeping the target database nearly synchronized with the source until you are ready to switch.

Exam trap

The trap here is that candidates often confuse a full-load-only DMS task (Option E) with a full-load-plus-CDC task (Option C), assuming any DMS migration automatically minimizes downtime, but only CDC provides ongoing replication to reduce the cutover window.

Full explanation →
1362
MCQeasy

A social media company stores user posts in a database. Each post has a unique ID, content, and timestamp. The application frequently queries posts by user ID and also needs to support a global feed sorted by timestamp. Which database design is most efficient?

A.Amazon DynamoDB with a single table and scan operation for the global feed
B.Amazon S3 with a metadata index in DynamoDB
C.Amazon DynamoDB with user_id as partition key and timestamp as sort key, plus a GSI on timestamp
D.Amazon RDS for PostgreSQL with indexes on user_id and timestamp
AnswerC

This design efficiently supports both query patterns.

Why this answer

It uses user_id as the partition key and timestamp as the sort key for efficient per-user queries, while the Global Secondary Index (GSI) on timestamp allows the global feed to be sorted by timestamp without a costly scan. This design leverages DynamoDB's key-value and query capabilities to support both access patterns with low latency and minimal read capacity consumption.

Exam trap

The trap here is that candidates often assume a relational database with indexes is always the best for sorted queries, but DynamoDB's GSI and sort key design can handle both access patterns more efficiently at scale, and the exam tests understanding of when to use NoSQL over SQL for high-throughput workloads.

How to eliminate wrong answers

Option A is wrong because a Scan operation on a single DynamoDB table reads every item, which is inefficient, expensive, and does not scale for a global feed sorted by timestamp; DynamoDB is designed for query-based access, not full-table scans. Option B is wrong because storing posts in S3 with a DynamoDB metadata index adds unnecessary complexity and latency for frequent queries, as each post retrieval requires two round trips (one to DynamoDB for metadata, one to S3 for content), and it does not natively support sorted global feeds without additional processing. Option D is wrong because while PostgreSQL with indexes can support both queries, it is a relational database that may introduce overhead for a social media workload with high write throughput and requires manual scaling, whereas DynamoDB provides managed, auto-scaling NoSQL performance better suited for this use case.

Full explanation →
1363
Multi-Selecthard

Which TWO steps are required when migrating an Oracle database to Amazon RDS for Oracle using AWS DMS with ongoing replication? (Choose TWO.)

Select 2 answers
A.Run AWS Schema Conversion Tool (SCT) to convert the schema.
B.Enable minimal supplemental logging.
C.Enable archive logging on the source Oracle database.
D.Enable supplemental logging for all columns.
E.Create a VPC endpoint for the DMS replication instance.
AnswersC, D

DMS needs archive logs to capture changes.

Why this answer

AWS DMS requires archive logging on the source Oracle database to capture ongoing changes for continuous replication. Archive logging ensures that redo logs are retained and available for DMS to read transaction changes after a log switch, enabling Change Data Capture (CDC) without data loss.

Exam trap

The trap here is that candidates often confuse minimal supplemental logging (which is sufficient for Oracle GoldenGate) with the full supplemental logging required by AWS DMS, leading them to incorrectly select option B instead of D.

Full explanation →
1364
MCQeasy

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?

A.Change the storage type to Provisioned IOPS for better performance
B.Increase the allocated storage for the RDS instance
C.Grant the rds_backup user additional permissions to write to S3
D.Switch to automated backups instead of native backups
AnswerB

More storage space allows the backup file to be written.

Why this answer

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.

Full explanation →
1365
MCQhard

A company is migrating an on-premises MongoDB database to Amazon DocumentDB. The migration must be online with minimal downtime. The source MongoDB is version 4.0 and uses replica sets. Which tool should the company use?

A.Use MongoDB Compass to export data and import into DocumentDB.
B.Use mongodump and mongorestore.
C.Create a read replica of the MongoDB replica set and promote to DocumentDB.
D.Use AWS DMS with MongoDB as source and DocumentDB as target.
AnswerD

DMS supports ongoing replication from MongoDB to DocumentDB.

Why this answer

AWS DMS (Database Migration Service) supports continuous replication from MongoDB (including replica sets) to Amazon DocumentDB, enabling an online migration with minimal downtime. DMS uses the MongoDB oplog to capture ongoing changes, ensuring data consistency during the cutover. This is the only option that meets the requirement for an online migration with minimal downtime.

Exam trap

The trap here is that candidates often confuse a MongoDB read replica promotion (which works within native MongoDB clusters) with cross-service migration to DocumentDB, which requires a purpose-built tool like DMS for online replication.

How to eliminate wrong answers

Option A is wrong because MongoDB Compass is a GUI tool for ad-hoc data export/import, not designed for continuous replication or minimal downtime migrations. Option B is wrong because mongodump and mongorestore perform a snapshot-based, offline migration that requires stopping writes, causing significant downtime. Option C is wrong because you cannot promote a MongoDB read replica to become an Amazon DocumentDB cluster; DocumentDB is a separate service with its own replication mechanism, not a MongoDB replica set member.

Full explanation →
1366
MCQhard

A gaming company uses Amazon DynamoDB as the primary data store for player profiles and game state. The application experiences sudden spikes in traffic during new game launches, causing throttling on write requests. The current table has on-demand capacity mode. The table's partition key is 'player_id' (high cardinality). The read/write patterns are evenly distributed. Despite on-demand mode, throttling occurs because the per-partition throughput limit is being reached. The company wants to eliminate throttling without changing the partition key. Which solution should be recommended?

A.Implement Amazon DynamoDB Accelerator (DAX) to offload read traffic.
B.Use DynamoDB auto scaling with provisioned capacity.
C.Enable DynamoDB adaptive capacity and implement write sharding using a random suffix.
D.Switch to provisioned capacity mode and increase write capacity units.
AnswerC

Adaptive capacity helps distribute load; write sharding further spreads writes across partitions.

Why this answer

On-demand capacity mode already scales automatically, but per-partition throughput limits can still be reached if a single partition receives too many writes. Adaptive capacity (enabled by default) helps by dynamically adjusting per-partition throughput based on traffic patterns. However, if a specific partition key value experiences hot-spotting, write sharding—adding a random suffix to the partition key—further distributes writes across multiple partitions, increasing overall write capacity.

Option A (DAX) caches reads, not writes. Option B (auto scaling with provisioned) does not solve per-partition limits; it adjusts table-level capacity. Option D (provisioned with increased WCU) also addresses table-level capacity, not per-partition limits.

Full explanation →
1367
MCQmedium

A company is migrating a 500 GB Oracle database to Amazon RDS for Oracle. The migration must complete within a 4-hour downtime window. The network bandwidth is 1 Gbps. Which migration approach minimizes the migration time?

A.Use AWS Schema Conversion Tool (SCT) to convert the schema, then use AWS DMS for data migration.
B.Use Oracle Data Pump to export and import the database.
C.Use AWS DMS to perform a full load of the database to RDS.
D.Use AWS DataSync to copy the database files to Amazon S3, then restore to RDS.
AnswerC

DMS can efficiently migrate data directly to RDS, minimizing migration time.

Why this answer

AWS DMS performs a full load directly from the source Oracle database to the target RDS for Oracle instance over the network, leveraging the 1 Gbps bandwidth efficiently. For a 500 GB database, a full load at 1 Gbps (theoretical max ~450 Gbps per hour) can complete in under 2 hours, well within the 4-hour window, without requiring intermediate storage or schema conversion.

Exam trap

The trap here is that candidates often overcomplicate the migration by choosing tools like SCT or DataSync, not realizing that for homogeneous Oracle-to-Oracle migrations, a direct DMS full load is the simplest and fastest approach, and that Data Pump or file-based methods introduce unnecessary overhead.

How to eliminate wrong answers

Option A is wrong because the AWS Schema Conversion Tool (SCT) is used for heterogeneous migrations (e.g., Oracle to Aurora PostgreSQL) and is unnecessary for Oracle-to-Oracle migrations; adding SCT would introduce extra steps and time, not minimize migration time. Option B is wrong because Oracle Data Pump export/import requires writing to a file system (e.g., EBS or S3) and then transferring those files, which adds I/O overhead and network transfer time for the entire 500 GB, often exceeding the 4-hour window due to export/import processing and file copy latency. Option D is wrong because AWS DataSync is designed for file-based transfers to S3, not for direct database migration; restoring from S3 to RDS would require additional steps (e.g., using Oracle RMAN or Data Pump), adding complexity and time, and the full 500 GB must be uploaded and then restored, which is slower than a direct DMS full load.

Full explanation →
1368
MCQmedium

A company is migrating its on-premises Oracle database to Amazon RDS for Oracle. The database is 2 TB in size and has a 24-hour maintenance window. The company needs to minimize downtime during the migration. Which AWS service should be used to perform the migration with minimal downtime?

A.AWS Database Migration Service
B.Oracle Data Guard
C.AWS Schema Conversion Tool (AWS SCT)
D.AWS Data Pipeline
AnswerA

This is the same as Option D and refers to AWS Database Migration Service, which is designed for minimal downtime migrations.

Why this answer

AWS Database Migration Service (AWS DMS) can migrate the on-premises Oracle database to Amazon RDS for Oracle with minimal downtime by using ongoing replication. Option B (Oracle Data Guard) is not supported for cross-environment replication to Amazon RDS. Option C (AWS Schema Conversion Tool) is used for schema conversion, not data migration.

Option D (AWS Data Pipeline) is a data orchestration service unrelated to database migration.

Full explanation →
1369
MCQmedium

A company is running a critical application on Amazon RDS for Oracle. They need to ensure high availability with automatic failover in case of a database failure. The database size is 500 GB. Which solution should they implement?

A.Create a cross-Region read replica
B.Migrate to Amazon DynamoDB Global Tables
C.Take regular snapshots and restore in a different Availability Zone
D.Enable Multi-AZ deployment
AnswerD

Multi-AZ automatically fails over to a standby instance.

Why this answer

Multi-AZ deployment for Amazon RDS for Oracle provides synchronous replication to a standby instance in a different Availability Zone, with automatic failover in the event of a database failure. This ensures high availability without manual intervention, meeting the requirement for automatic failover for a 500 GB Oracle database.

Exam trap

The trap here is that candidates may confuse cross-Region read replicas or snapshot-based recovery with automatic failover, but only Multi-AZ provides synchronous replication and automatic failover without manual intervention for RDS databases.

How to eliminate wrong answers

Option A is wrong because cross-Region read replicas are designed for disaster recovery and read scaling, not automatic failover within the same region; they require manual promotion and do not provide synchronous replication. Option B is wrong because DynamoDB Global Tables are for NoSQL workloads, not Oracle relational databases, and migrating would require significant application changes. Option C is wrong because taking regular snapshots and restoring in a different Availability Zone is a manual process that does not provide automatic failover; it results in data loss from the last snapshot and downtime during restore.

Full explanation →
1370
Multi-Selecthard

A database engineer is troubleshooting slow query performance on an Amazon RDS for PostgreSQL instance. The instance is db.r5.large with 500 GB of General Purpose SSD (gp2) storage. CloudWatch metrics show high Read Latency and high Read IOPS, but low CPU utilization. Which TWO actions should the engineer take to improve performance?

Select 2 answers
A.Create a read replica and offload read queries to it.
B.Increase the DB instance class to a larger size, such as db.r5.2xlarge.
C.Enable Multi-AZ to use the standby for read traffic.
D.Optimize queries by adding appropriate indexes.
E.Switch from General Purpose SSD (gp2) to Provisioned IOPS SSD (io1) with a higher IOPS rate.
AnswersA, E

Read replicas reduce the read IOPS on the primary, which can lower latency on the primary.

Why this answer

A is correct because creating a read replica offloads read queries from the primary instance, reducing the read IOPS and read latency on the primary. This directly addresses the high Read Latency and high Read IOPS metrics without requiring a larger instance class or storage change, especially since CPU utilization is low, indicating the bottleneck is I/O, not compute.

Exam trap

The trap here is that candidates often assume Multi-AZ can serve read traffic (like in SQL Server or Oracle), but Amazon RDS for PostgreSQL Multi-AZ does not support read-only queries on the standby; only read replicas can offload reads.

Full explanation →
1371
Multi-Selecthard

A company is running a production Amazon Aurora MySQL-Compatible Edition database. The database has recently experienced several failovers due to replica lag. The DBA needs to implement monitoring to detect replica lag early. Which THREE metrics should be monitored to assess replication health? (Select THREE.)

Select 3 answers
A.DatabaseConnections
B.ActiveTransactions
C.ReplicaLag
D.BufferCacheHitRatio
E.AuroraReplicaLag
AnswersA, C, E

Increased DatabaseConnections can be a symptom of application retries during failover, making it an indirect indicator of replication issues.

Why this answer

(DatabaseConnections) can indirectly indicate replication issues if application retries increase due to failovers. Option C (ReplicaLag) is the standard MySQL metric measuring replication lag. Option E (AuroraReplicaLag) is the direct Aurora-specific metric for replica lag.

The other options are not directly related to replication health: ActiveTransactions (B) measures transaction volume, not replication lag, and BufferCacheHitRatio (D) is about cache efficiency.

Full explanation →
1372
MCQhard

A company is migrating a 1 TB Oracle database to Amazon RDS for Oracle. The source database has a high volume of small transactions. The migration must minimize source database impact. Which AWS DMS configuration should be used?

A.Use batch-optimized apply mode
B.Use full load only and disable ongoing replication
C.Use full load with CDC (ongoing replication)
D.Use multiple DMS tasks to parallelize the migration
AnswerB

Full load only does not require CDC, minimizing impact.

Why this answer

Using full load only with ongoing replication disabled minimizes source database impact by avoiding the overhead of capturing and applying continuous change data capture (CDC) logs. The high volume of small transactions would otherwise generate a large number of redo log switches and increase I/O on the source, which is contrary to the requirement to minimize impact. A full load only migration transfers a snapshot of the data once, reducing the source load to a single consistent read operation.

Exam trap

The trap here is that candidates often assume CDC is always necessary for a complete migration, but the question explicitly prioritizes minimizing source impact over minimizing downtime, making full load only the correct choice despite the lack of ongoing replication.

How to eliminate wrong answers

Option A is wrong because batch-optimized apply mode is a target-side optimization that batches changes for apply, but it does not reduce source impact; it still requires CDC to capture changes, which would generate significant redo log overhead on the source. Option C is wrong because full load with CDC (ongoing replication) requires continuous reading of the source redo logs to capture changes, which adds persistent I/O and log generation overhead, directly conflicting with the requirement to minimize source database impact. Option D is wrong because using multiple DMS tasks to parallelize the migration increases the number of concurrent connections and read operations on the source, amplifying rather than minimizing the impact.

Full explanation →
1373
MCQmedium

A company is using an Amazon RDS for PostgreSQL database to store sensitive customer data. The security team requires that all data be encrypted at rest and in transit, and that access to the database is restricted to only specific applications. Currently, the database is encrypted at rest using AWS KMS, and connections are made over SSL. However, the security team wants to ensure that even if the database credentials are compromised, an attacker cannot access the database from unauthorized IP addresses. What should be done to meet this requirement?

A.Attach a resource-based policy to the RDS instance to allow only specific IAM roles.
B.Create a new RDS instance in a VPC with a network ACL that allows inbound traffic only from specific IP ranges, and migrate the data.
C.Modify the security group associated with the RDS instance to allow inbound traffic only from the application's IP addresses.
D.Enable IAM database authentication for the RDS instance.
AnswerC

Security groups act as a virtual firewall and can restrict inbound traffic based on IP addresses.

Why this answer

Modifying the security group associated with the RDS instance to allow inbound traffic only from the application's IP addresses restricts network access at the instance level, preventing unauthorized IP addresses from connecting even if credentials are compromised. Option A (attaching a resource-based policy) does not restrict network access—it controls IAM permissions. Option B (creating a new RDS instance in a VPC with a network ACL) is unnecessarily complex and involves migrating data; the requirement can be met by modifying the existing security group without creating a new instance.

Option D (enabling IAM database authentication) authenticates database users via IAM but does not restrict source IP addresses, so it does not meet the stated requirement.

Full explanation →
1374
Multi-Selectmedium

Which TWO of the following are advantages of using Amazon Aurora over standard RDS for MySQL?

Select 2 answers
A.Aurora automatically fails over to a read replica in case of primary failure.
B.Aurora is compatible with PostgreSQL, so you can migrate from SQL Server easily.
C.Aurora can deliver up to 5x the throughput of standard MySQL on the same hardware.
D.Aurora supports up to 15 read replicas, while RDS for MySQL only supports 5.
E.Aurora provides higher durability with 6 copies of data across 3 AZs.
AnswersC, E

Aurora's architecture provides significant performance improvements.

Why this answer

Amazon Aurora uses a distributed, SSD-backed storage subsystem that separates compute from storage, enabling it to deliver up to 5x the throughput of standard MySQL running on the same hardware. This performance gain comes from the Aurora storage engine's ability to reduce I/O operations and parallelize writes across multiple storage nodes.

Exam trap

The trap here is that candidates may confuse the number of read replicas supported by RDS for MySQL (which is 15, not 5) and assume Aurora's higher replica count is a unique advantage, while in fact both services support the same limit.

Full explanation →
1375
MCQhard

A gaming company uses Amazon DynamoDB with global tables across two regions. They notice increased write latency and throttling during peak hours. The access pattern is mostly writes to a small set of hot partitions. Which design change would best address this?

A.Implement write sharding using a random suffix on the partition key
B.Enable DynamoDB Accelerator (DAX)
C.Switch to DynamoDB on-demand capacity mode
D.Increase write capacity using auto scaling
AnswerA

Write sharding distributes writes evenly across partitions.

Why this answer

The issue is hot partitions caused by a small set of partition keys receiving the majority of writes. By implementing write sharding with a random suffix on the partition key, you distribute writes across multiple partitions, reducing throttling and write latency. This directly addresses the root cause of uneven access patterns, unlike the other options that either cache reads, adjust capacity mode, or scale capacity without solving the partition-level bottleneck.

Exam trap

The trap here is that candidates often confuse throughput scaling (options C and D) with partition-level distribution, failing to recognize that hot partitions require a key design change, not just capacity adjustments.

How to eliminate wrong answers

Option B is wrong because DynamoDB Accelerator (DAX) is an in-memory cache that primarily improves read performance, not write latency or throttling on hot partitions. Option C is wrong because switching to on-demand capacity mode handles traffic spikes but does not resolve the underlying hot partition issue; throttling can still occur at the partition level if a single partition exceeds its throughput limit. Option D is wrong because increasing write capacity with auto scaling only raises the table-level throughput, but if writes are concentrated on a few partitions, those partitions will still hit their individual limits and cause throttling.

Full explanation →
1376
MCQeasy

A company is using Amazon RDS for MySQL and needs to restrict access to the database to only specific Amazon EC2 instances in the same VPC. Which security mechanism should be used?

A.Configure a VPC security group that allows inbound traffic from the EC2 security groups.
B.Create an IAM policy that allows the EC2 instances to connect to the RDS instance.
C.Create a DB subnet group that includes only the subnets where the EC2 instances reside.
D.Modify the network ACL for the DB subnet to allow traffic from the EC2 instances' IP addresses.
AnswerA

DB security groups are only applicable in EC2-Classic, not in a VPC. In a VPC, you should use a VPC security group to control traffic to the RDS instance.

Why this answer

In a VPC, RDS instances use VPC security groups to control inbound traffic. Configuring a VPC security group that allows inbound traffic from the EC2 security groups is the correct mechanism. Option B is incorrect because IAM policies control API access, not network connectivity.

Option C is incorrect because subnet groups define subnets for deployment, not security rules. Option D is incorrect because network ACLs are stateless, apply to entire subnets, and cannot reference security groups or specific instances.

Full explanation →
1377
MCQmedium

A data engineer is troubleshooting a slow-running query on an Amazon Redshift cluster. The query involves large table joins and aggregations. Which diagnostic step should be taken FIRST to understand the query execution plan and identify bottlenecks?

A.Monitor the WLM queue metrics for the query.
B.Check the SVV_TABLE_INFO view for table distribution and sort keys.
C.Run the EXPLAIN command on the query to review the execution plan.
D.Query the STL_QUERY system table to review the query text.
AnswerC

EXPLAIN reveals how Redshift will execute the query.

Why this answer

The EXPLAIN command is the first and most direct step to understand how Redshift plans to execute a query, including join types, data distribution, and aggregation strategies. It reveals the execution plan without running the query, allowing the engineer to identify bottlenecks like nested loop joins or missing sort key optimization before any other diagnostic step.

Exam trap

The trap here is that candidates often jump to checking table design (Option B) or historical logs (Option D) first, but the EXPLAIN command is the fastest way to see the actual query execution plan and pinpoint join or aggregation bottlenecks.

How to eliminate wrong answers

Option A is wrong because WLM queue metrics show resource contention and queue wait times, not the internal execution plan or join strategies. Option B is wrong because SVV_TABLE_INFO provides table design metadata (distribution keys, sort keys, compression) but does not show how a specific query will be executed. Option D is wrong because STL_QUERY stores query text and historical execution details, but it does not show the execution plan; the EXPLAIN command is needed for that.

Full explanation →
1378
MCQmedium

A company is using Amazon RDS for PostgreSQL and needs to implement column-level encryption for sensitive data. The application must be able to encrypt and decrypt data transparently. Which approach should be taken?

A.Enable RDS encryption at rest using a KMS key, which will automatically encrypt all columns.
B.Use AWS Lambda to encrypt data before writing to the database and decrypt after reading.
C.Use the AWS KMS Encrypt and Decrypt APIs directly in the application code.
D.Install the pgcrypto extension on the RDS instance and use its functions to encrypt data at the column level.
AnswerD

pgcrypto provides transparent column-level encryption.

Why this answer

Pgcrypto is a PostgreSQL extension that provides column-level encryption functions, allowing the application to encrypt and decrypt data transparently at the column level. Option A is incorrect because RDS encryption at rest encrypts the entire database storage, not individual columns. Option B is incorrect because using AWS Lambda would require application modifications and introduce additional latency.

Option C is incorrect because AWS KMS is a key management service, not a direct column-level encryption solution for databases, and using its APIs would require building encryption logic in the application.

Full explanation →
1379
MCQmedium

A company is using Amazon DynamoDB with a TTL attribute to automatically delete expired items. The security team is concerned that deleted items might still be recoverable from backups. They need to ensure that once an item is deleted by TTL, it is not included in future on-demand backups. Additionally, they want to ensure that the TTL deletion itself is logged for audit purposes. What should they do?

A.Disable TTL and implement a custom deletion process that logs deletions before removing items.
B.Enable DynamoDB Streams on the table and use a Lambda function to log TTL deletion events to CloudWatch Logs.
C.Use AWS CloudTrail to log the UpdateTimeToLive API call.
D.Enable AWS CloudTrail data events for DynamoDB to capture TTL deletions.
AnswerB

Streams capture TTL deletions as REMOVE events.

Why this answer

DynamoDB Streams can capture TTL deletions as 'REMOVE' events. By processing these events with a Lambda function and logging them to CloudWatch Logs, the security team can audit TTL deletions. On-demand backups reflect the current table state, so items deleted by TTL before the backup is taken will not be included.

Option A is incorrect because disabling TTL and implementing a custom deletion process adds complexity and may not be as efficient. Option C is incorrect because CloudTrail logs the UpdateTimeToLive API call (control plane), not the actual TTL deletions (data plane). Option D is incorrect because CloudTrail data events for DynamoDB capture GetItem, PutItem, etc., but not TTL deletions.

Full explanation →
1380
MCQhard

A company has an Amazon RDS for SQL Server DB instance that stores financial data. The security team requires that the data be encrypted at rest using a customer-managed key stored in AWS KMS. Additionally, they want to ensure that the key cannot be deleted without authorization. What should be done?

A.Create a customer-managed KMS key, enable key rotation, and set a deletion protection policy.
B.Enable encryption on the RDS instance and use the default KMS key.
C.Use AWS CloudHSM to generate and store the encryption key, and associate it with the RDS instance.
D.Enable AWS CloudTrail to log key deletion attempts.
AnswerA

KMS supports customer-managed keys with rotation and deletion protection.

Why this answer

Creating a customer-managed KMS key allows you to control key rotation and deletion protection, meeting the security requirements. Option B is wrong because using the default KMS key does not provide a customer-managed key. Option C is wrong because AWS CloudHSM is not directly used for RDS encryption at rest; KMS is the required service.

Option D is wrong because AWS CloudTrail only logs actions but does not prevent key deletion.

Exam trap

Candidates may confuse key rotation with deletion protection; both are required here. Deleting the KMS key would render encrypted data unrecoverable.

Full explanation →
1381
MCQmedium

A company has a requirement to automatically rotate the password for an Amazon RDS for MySQL DB instance every 90 days. The password is stored in AWS Secrets Manager. Which combination of steps will meet this requirement?

A.Enable IAM database authentication for the RDS instance and rotate the IAM keys every 90 days.
B.Store the password in AWS Systems Manager Parameter Store and configure a scheduled AWS Lambda function to update the parameter and the RDS password.
C.Store the password in Secrets Manager and configure automatic rotation with a Lambda function that updates the RDS password every 90 days.
D.Use an AWS Lambda function to manually update the RDS password and store the new password in Secrets Manager, triggered by a CloudWatch Events rule every 90 days.
AnswerC

Secrets Manager supports automatic rotation for RDS with a custom Lambda rotation function.

Why this answer

AWS Secrets Manager provides built-in support for automatic rotation of RDS database passwords using a custom or pre-built AWS Lambda rotation function. You can configure the rotation interval to 90 days to meet the requirement. Option A is incorrect because IAM database authentication does not rotate passwords; it uses IAM roles and credentials, not password rotation.

Option B is incorrect because AWS Systems Manager Parameter Store does not have native rotation capabilities for RDS passwords; it requires a custom solution. Option D is incorrect while it describes a manual approach via Lambda and CloudWatch Events, it does not leverage Secrets Manager's automatic rotation feature, which is the recommended and simplest method.

Full explanation →
1382
MCQeasy

A developer needs to securely store database credentials for an application that runs on Amazon EC2 and connects to an Amazon RDS for PostgreSQL database. The credentials must be automatically rotated every 90 days. Which AWS service should the developer use to meet these requirements?

A.AWS Systems Manager Parameter Store
B.AWS CloudHSM
C.AWS Identity and Access Management (IAM) roles
D.AWS Secrets Manager
AnswerD

Supports automatic rotation of database credentials.

Why this answer

AWS Secrets Manager. Secrets Manager is designed to securely store and manage secrets such as database credentials. It natively supports automatic rotation of credentials for Amazon RDS databases, including PostgreSQL, with a customizable rotation interval (e.g., every 90 days).

Option A (AWS Systems Manager Parameter Store) can store secrets but does not provide built-in automatic rotation for RDS credentials. Option B (AWS CloudHSM) provides hardware security modules for encryption key storage, not for managing database credentials. Option C (IAM roles) allow EC2 instances to assume roles for API access but do not store or rotate database credentials; while IAM database authentication can be used with RDS PostgreSQL, it does not meet the requirement to store and rotate credentials automatically.

Full explanation →
1383
MCQhard

An application using the above IAM policy is trying to perform a Scan operation on the 'Orders' table. What will happen?

A.The Scan operation will succeed because the Deny is on all resources but the Allow is specific to the table.
B.The Scan operation will succeed because the policy allows other operations on the table.
C.The Scan operation will fail because the policy does not explicitly allow Scan.
D.The Scan operation will fail because the explicit Deny on dynamodb:Scan overrides the Allow.
AnswerD

Explicit Deny always overrides Allow.

Why this answer

D is correct because IAM policy evaluation follows an explicit deny override: any explicit Deny statement for an action overrides any Allow for that same action, regardless of resource specificity. Since the policy includes an explicit Deny on dynamodb:Scan for all resources, the Scan operation on the 'Orders' table will be denied, even though an Allow statement grants other DynamoDB actions on that table.

Exam trap

The trap here is that candidates assume a resource-specific Allow (e.g., on the 'Orders' table) will override a broad Deny on all resources, but AWS IAM's explicit deny always wins, regardless of resource specificity.

How to eliminate wrong answers

Option A is wrong because an explicit Deny on all resources overrides a resource-specific Allow for the same action; AWS IAM evaluates Deny statements before Allow statements, so the Deny on dynamodb:Scan blocks the operation. Option B is wrong because allowing other operations (e.g., GetItem, PutItem) does not imply Scan is allowed; each action must be explicitly permitted unless a wildcard is used, and the explicit Deny on Scan overrides any implicit or explicit Allow. Option C is wrong because the failure is not due to a missing explicit Allow for Scan—it is due to the explicit Deny on Scan, which takes precedence over any Allow.

Full explanation →
1384
MCQhard

A social media application uses Amazon DynamoDB with a table that has a partition key of 'user_id' and a sort key of 'post_timestamp'. The application frequently queries for the 10 most recent posts by a specific user. The query pattern uses a 'begins_with' condition on the sort key with a timestamp prefix. Recently, the query latency has increased significantly for users with many posts. Which design change would improve query performance?

A.Create a local secondary index (LSI) with 'user_id' as partition key and 'post_timestamp' as sort key, and query using reverse order with a limit of 10.
B.Enable DynamoDB Accelerator (DAX) to cache the query results.
C.Create a global secondary index (GSI) with 'post_timestamp' as partition key and 'user_id' as sort key.
D.Change the table's partition key to 'post_id' to distribute data more evenly.
AnswerA

Creating an LSI with 'user_id' as partition key and 'post_timestamp' as sort key allows querying in reverse order with a limit of 10, efficiently retrieving the most recent posts for a user.

Why this answer

The optimal approach to retrieve the 10 most recent posts for a user is to query the table or an index with the partition key 'user_id' and use ScanIndexForward=false with a Limit of 10. Option A achieves this by creating a Local Secondary Index (LSI) with the same partition key and sort key as the base table. While the base table itself can be queried in reverse order, creating an LSI dedicated to this query pattern can improve performance by offloading reads from the base table index, reducing contention and ensuring fast consistent reads.

The LSI can be provisioned with its own read capacity to handle the frequent queries for the most recent posts per user, thus improving overall query performance. Options B, C, and D do not effectively address the specific requirement of per-user recent posts. Option B (DAX) may reduce latency but does not fix the underlying inefficient query pattern (scanning many items per user).

Option C (GSI with timestamp as partition key) would allow querying posts globally by time, not per user. Option D (changing partition key to post_id) would break the ability to query all posts by a user. Therefore, Option A is the correct design change.

Exam trap

A common trap is to think that a Local Secondary Index must have a different sort key than the base table. However, DynamoDB allows creating an LSI with the same sort key as the base table; this can be used to provision separate capacity for specific query patterns. Another trap is overlooking that the base table already supports reverse-order queries with ScanIndexForward=false, but creating an LSI can still be beneficial for workload isolation.

How to eliminate wrong answers

Option B is wrong because DynamoDB Accelerator (DAX) caches query results to reduce latency for repeated queries, but it does not address the underlying issue of inefficient scanning caused by the 'begins_with' condition on a large number of items per partition; DAX would only help if the same query is repeated frequently, not for the general query pattern. Option C is wrong because a GSI with 'post_timestamp' as partition key and 'user_id' as sort key would not efficiently retrieve the 10 most recent posts for a specific user, as the partition key is timestamp-based, requiring a scan across all partitions to filter by user_id. Option D is wrong because changing the partition key to 'post_id' would break the existing query pattern that relies on user_id to find posts for a specific user, and it would not improve performance for the 'most recent posts by user' query.

Full explanation →
1385
Multi-Selectmedium

A security team needs to audit all SQL statements executed against an Amazon Aurora MySQL DB cluster. Which combination of actions should be taken to achieve this? (Choose TWO.)

Select 2 answers
A.Enable AWS CloudTrail for the Aurora DB cluster.
B.Enable Enhanced Monitoring for the DB cluster.
C.Enable RDS event subscription for the DB cluster.
D.Set the server_audit_logging parameter to 1 in the DB cluster parameter group.
E.Configure the DB cluster to publish audit logs to Amazon CloudWatch Logs.
AnswersD, E

This enables the audit plugin for Aurora MySQL.

Why this answer

Options D and E are correct. To audit SQL statements in an Aurora MySQL DB cluster, you need to enable the Aurora MySQL audit plugin. This is done by setting the `server_audit_logging` parameter to 1 in the DB cluster parameter group (option D).

Then you can configure the DB cluster to publish the audit logs to Amazon CloudWatch Logs (option E) for centralized monitoring and analysis. Option A is incorrect because AWS CloudTrail captures API calls, not SQL statements. Option B is incorrect because Enhanced Monitoring captures OS-level metrics, not SQL queries.

Option C is incorrect because RDS event subscriptions notify about events like DB instance changes, not SQL execution.

Full explanation →
1386
Multi-Selecteasy

Which TWO of the following are advantages of using Amazon DynamoDB over Amazon RDS for MySQL for a workload that requires high scalability and low maintenance? (Select TWO.)

Select 2 answers
A.Strong consistency by default
B.Support for complex joins and transactions
C.No need to manage database servers or patches
D.Built-in read replicas for scaling reads
E.Automatic scaling of read/write capacity
AnswersC, E

DynamoDB is serverless and fully managed.

Why this answer

Amazon DynamoDB is a fully managed NoSQL database service that eliminates the need for server provisioning, patching, or maintenance. Unlike Amazon RDS for MySQL, where you are responsible for managing the underlying DB instance (including OS and database engine patches), DynamoDB abstracts all infrastructure management, allowing you to focus solely on data access patterns.

Exam trap

The trap here is that candidates often confuse DynamoDB’s optional strong consistency with a default setting, or they assume that a NoSQL database like DynamoDB supports SQL-style joins, leading them to select options A or B despite those being features of relational databases like RDS for MySQL.

Full explanation →
1387
MCQmedium

A company is using Amazon RDS for MySQL with Multi-AZ deployment. The application team reports increased latency during peak hours. Which AWS service should the database specialist use to identify the root cause?

A.Enable Performance Insights for the RDS instance and analyze the database load.
B.Enable AWS Config to track configuration changes to the RDS instance.
C.Use the CloudWatch Metrics Dashboard to analyze database connections.
D.Run an Amazon Inspector assessment on the RDS instance.
AnswerA

Performance Insights provides detailed database performance analysis and helps identify bottlenecks.

Why this answer

Performance Insights provides detailed database performance metrics and helps identify bottlenecks such as high load or slow queries. Option B is wrong because AWS Config tracks configuration changes, not performance. Option C is wrong because CloudWatch Metrics Dashboard shows aggregated metrics but lacks the granular database analysis needed for root cause identification.

Option D is wrong because Amazon Inspector is a security assessment tool, not a performance monitoring service.

Full explanation →
1388
MCQeasy

A company is migrating a 100 GB MySQL database to Amazon Aurora MySQL. The migration must have minimal downtime and the source database is currently in use. Which approach should the company take?

A.Create an Aurora Replica from the on-premises MySQL database and promote it.
B.Export the database using mysqldump and import it into Aurora during a maintenance window.
C.Take a physical backup of the MySQL database, upload to S3, and restore to Aurora.
D.Use AWS DMS with ongoing replication from the source MySQL database to Aurora.
AnswerD

DMS supports ongoing replication to keep the target in sync with minimal downtime.

Why this answer

AWS DMS with ongoing replication (change data capture, CDC) is the correct approach because it allows a live migration with minimal downtime. DMS performs a full load of the 100 GB database and then continuously replicates incremental changes from the source MySQL to the target Aurora MySQL until you cut over, keeping the source fully operational throughout the process.

Exam trap

The trap here is that candidates often confuse creating an Aurora Replica (which is an Aurora-only feature) with the ability to replicate from an external MySQL database, or they assume a physical backup can be directly restored into Aurora without understanding the proprietary storage format differences.

How to eliminate wrong answers

Option A is wrong because an Aurora Replica can only be created from an existing Aurora DB cluster, not from an on-premises MySQL database; there is no mechanism to directly create an Aurora Replica from an external source. Option B is wrong because exporting with mysqldump and importing during a maintenance window requires taking the source database offline or severely limiting writes, which contradicts the requirement for minimal downtime. Option C is wrong because taking a physical backup of MySQL and restoring to Aurora is not directly supported; Aurora can only restore from its own native backups or snapshots, and while you can upload MySQL backup files to S3, you cannot restore them into Aurora without additional conversion steps that are not natively available.

Full explanation →
1389
Multi-Selectmedium

A company is using Amazon DynamoDB with provisioned capacity. They notice an increase in throttled write requests. The workload consists of writes to a single partition key. Which TWO actions would help reduce throttling?

Select 2 answers
A.Add a global secondary index with a different partition key.
B.Increase the provisioned write capacity units.
C.Enable DynamoDB auto scaling with adaptive capacity.
D.Use DynamoDB Accelerator (DAX) for write caching.
E.Implement write sharding by adding a suffix to the partition key.
AnswersB, C

More capacity directly reduces throttling.

Why this answer

Options B and C are correct. Increasing provisioned write capacity (B) directly addresses throttling by allowing more writes per second. Enabling DynamoDB auto scaling with adaptive capacity (C) automatically adjusts capacity based on traffic and helps handle uneven access patterns, including hot partitions.

Option A (adding a global secondary index with a different partition key) does not reduce throttling on the base table; it only provides an alternative query path. Option D (DAX) is a read cache and does not assist with write throttling. Option E (write sharding) can help distribute writes across partitions, but the question asks for two actions from the given list; B and C are the most direct and effective.

Full explanation →
1390
MCQhard

Refer to the exhibit. A DBA is monitoring a DMS migration task from on-premises Oracle to Amazon RDS for Oracle. The full load completed successfully with 50 tables. However, the DBA notices that the CDC phase has not started. What is the most likely reason?

A.The task was not configured with CDC enabled
B.The full load encountered errors on some tables
C.The source Oracle database is not generating redo logs
D.The replication task was stopped after the full load completed
AnswerD

The StopDate is set, indicating the task stopped before CDC could begin.

Why this answer

The most likely reason the CDC phase has not started is that the replication task was stopped after the full load completed. In AWS DMS, after the full load finishes, the task must remain in a running state to transition to the CDC phase. If the task is stopped, CDC cannot begin, and the DBA would need to resume or restart the task to capture ongoing changes.

Exam trap

The trap here is that candidates may assume CDC automatically starts after full load, but AWS DMS requires the task to remain in a running state; stopping the task halts the transition to CDC, and this is a common oversight in migration planning.

How to eliminate wrong answers

Option A is wrong because if CDC were not enabled, the task would not have a CDC phase at all, and the full load would simply complete without any expectation of CDC starting; the question states the DBA notices CDC has not started, implying it was expected. Option B is wrong because the full load completed successfully with 50 tables, so there were no errors on tables that would prevent CDC from starting. Option C is wrong because if the source Oracle database were not generating redo logs, CDC would fail or produce errors, but the task would still attempt to start the CDC phase; the absence of redo logs would cause a failure, not a failure to start.

Full explanation →
1391
Multi-Selectmedium

A database specialist is troubleshooting an Amazon RDS for PostgreSQL instance that has high replication lag between the primary and a read replica. Which TWO metrics should the specialist review to identify the cause? (Select TWO.)

Select 2 answers
A.WriteIOPS on the primary
B.ReadIOPS on the replica
C.DatabaseConnections on the primary
D.ReplicaLag
E.NetworkThroughput between primary and replica
AnswersB, D

High read activity on the replica can cause lag.

Why this answer

High ReadIOPS on the read replica can indicate heavy read activity, which may cause replication lag as the replica struggles to apply changes fast enough. Option D is correct because ReplicaLag directly measures the time delay between the primary and replica, confirming the presence of lag. Option A is incorrect because WriteIOPS on the primary reflects write workload, not lag cause.

Option C is incorrect because DatabaseConnections do not directly affect replication lag. Option E is incorrect because NetworkThroughput is not a metric that directly indicates replication lag; it could be a factor but is not a standard RDS metric for diagnosing lag.

Full explanation →
1392
Multi-Selectmedium

A company is designing a database for an e-commerce platform that needs to store product catalog data. The data is highly relational with many-to-many relationships between products, categories, and suppliers. The platform requires ACID transactions and complex joins. Which TWO AWS database solutions are suitable for this workload? (Choose TWO.)

Select 2 answers
A.Amazon Aurora MySQL
B.Amazon RDS for PostgreSQL
C.Amazon ElastiCache for Redis
D.Amazon Neptune
E.Amazon DynamoDB
AnswersA, B

Aurora is a relational database with ACID support and complex join capabilities.

Why this answer

Amazon Aurora MySQL is a fully ACID-compliant relational database that supports complex joins and many-to-many relationships through foreign keys and junction tables. It is optimized for high-throughput e-commerce workloads with features like auto-scaling storage and up to 15 low-latency read replicas, making it suitable for product catalog data that requires transactional consistency.

Exam trap

The trap here is that candidates often choose DynamoDB for its scalability, overlooking that it cannot handle complex joins and many-to-many relational structures, or they select Neptune thinking it is suitable for any connected data, but it lacks SQL-based ACID transactions and relational integrity needed for product catalogs.

Full explanation →
1393
MCQhard

Refer to the exhibit. A DBA is troubleshooting an issue where an IAM user cannot view CloudWatch metrics for an RDS DB instance. The IAM policy attached to the user is shown above. What is the MOST likely reason the user cannot view the metrics?

A.The policy does not include cloudwatch:DescribeAlarms
B.The policy does not include rds:DescribeDBInstances
C.The policy uses a Resource element of '*' which is not allowed for CloudWatch
D.The policy does not include cloudwatch:GetMetricStatistics
AnswerA

The CloudWatch console often requires DescribeAlarms to view metrics.

Why this answer

The IAM policy includes 'cloudwatch:GetMetricStatistics' and 'cloudwatch:ListMetrics', which should allow retrieving metric data via API. However, the AWS Management Console for CloudWatch Metrics also requires 'cloudwatch:DescribeAlarms' to display alarm overlays on the metrics graph. Without this action, the console may fail to load the metrics page entirely.

Option A is correct because the missing permission is 'cloudwatch:DescribeAlarms'. Option B is incorrect because 'rds:DescribeDBInstances' is not needed to view metrics; the policy already includes it. Option C is incorrect because using a Resource of '*' is allowed for CloudWatch actions; it does not prevent viewing metrics.

Option D is incorrect because 'cloudwatch:GetMetricStatistics' is already present in the policy.

Full explanation →
1394
MCQmedium

A security engineer creates the IAM policy shown in the exhibit and attaches it to an IAM user. What is the effect of this policy?

A.The user can delete any database except 'prod-db'.
B.The user can describe all databases except 'prod-db'.
C.The user can modify 'prod-db' but cannot delete it.
D.The user can modify any database except 'prod-db'.
AnswerC

Correct. The user can modify all databases because ModifyDBInstance is allowed, but cannot delete 'prod-db' due to the explicit deny. For other databases, deletion is also denied implicitly.

Why this answer

The IAM policy allows DescribeDBInstances and ModifyDBInstance on all resources, but explicitly denies DeleteDBInstance on the database 'prod-db'. Since an explicit deny overrides any allow, the user cannot delete 'prod-db'. However, the policy does not allow DeleteDBInstance on any database, so the user cannot delete any database.

For 'prod-db', the user can still modify and describe it because the deny is only for the delete action. Thus, the user can modify 'prod-db' but cannot delete it.

Exam trap

The trap is that the explicit deny on DeleteDBInstance for 'prod-db' might be misinterpreted as also blocking ModifyDBInstance for that database, but it only affects the delete action.

Full explanation →
1395
MCQeasy

A company has an RDS for SQL Server DB instance that stores sensitive data. The database administrator needs to ensure that all connections to the database use SSL/TLS encryption. What should the administrator do?

A.Configure the security group to only allow traffic from specific IP addresses.
B.Set the 'rds.force_ssl' parameter to 1 in the DB parameter group.
C.Enable AWS CloudTrail to monitor connections.
D.Delete the DB instance and create a new one with encryption enabled.
AnswerB

This forces all connections to use SSL.

Why this answer

Setting 'rds.force_ssl' to 1 in the DB parameter group forces all connections to the RDS for SQL Server DB instance to use SSL/TLS encryption. Option A is incorrect because security group rules control network access, not encryption. Option C is incorrect because AWS CloudTrail logs API calls, not database connections.

Option D is incorrect because deleting and recreating the instance would not, by itself, enforce SSL; encryption at rest is separate from SSL enforcement.

Full explanation →
1396
Multi-Selectmedium

A company is designing a security strategy for an Amazon RDS for MySQL instance that stores Personally Identifiable Information (PII). Which TWO measures should be implemented to protect the data at rest?

Select 2 answers
A.Enable automatic backups with encryption.
B.Enable Amazon GuardDuty to monitor for suspicious activity.
C.Enable deletion protection on the DB instance.
D.Enable encryption at rest using AWS KMS.
E.Enable encryption in transit using SSL/TLS.
AnswersA, D

Automatic backups with encryption ensure that backup data is encrypted at rest, providing protection for stored backups.

Why this answer

Options A and D are correct. Option A: Enabling automatic backups with encryption ensures that backup data is encrypted at rest. Option D: Enabling encryption at rest using AWS KMS encrypts the underlying storage of the DB instance.

Option B is wrong because Amazon GuardDuty is a threat detection service, not for data at rest protection. Option C is wrong because deletion protection prevents accidental deletion but does not protect data at rest. Option E is wrong because encryption in transit (SSL/TLS) protects data in motion, not at rest.

Full explanation →
1397
MCQeasy

A company uses Amazon DynamoDB with provisioned capacity. The application team reports occasional ProvisionedThroughputExceededException errors. The database administrator notices that the errors occur during periods of high traffic. What is the most cost-effective way to handle these errors without over-provisioning capacity?

A.Increase the provisioned read and write capacity to the peak traffic level.
B.Use DynamoDB Accelerator (DAX) to cache frequently accessed items.
C.Implement exponential backoff and retry logic in the application.
D.Switch to on-demand capacity mode.
AnswerC

Exponential backoff retries handle throttling errors efficiently without over-provisioning.

Why this answer

Implementing exponential backoff and retry logic allows the application to handle throttling errors gracefully by retrying requests after a delay, which is the most cost-effective solution as it avoids over-provisioning capacity. Option A is incorrect because increasing capacity to peak levels is costly and inefficient. Option B is incorrect because DAX is a caching layer that reduces read load but does not help with write throttling or prevent ProvisionedThroughputExceededException errors for writes.

Option D is incorrect because switching to on-demand capacity mode can be more expensive for predictable traffic patterns, and the question asks for the most cost-effective approach without over-provisioning.

Full explanation →
1398
MCQhard

A company is using Amazon ElastiCache for Redis to cache frequently accessed data. Recently, the application has been experiencing increased latency. The database specialist suspects that the cache hit ratio has decreased. Which CloudWatch metric should the specialist analyze to confirm this suspicion?

A.Monitor the 'CurrConnections' metric to see if there are too many connections.
B.Monitor the 'Evictions' metric to see if keys are being evicted.
C.Monitor 'CacheHits' and 'CacheMisses' metrics to calculate the hit ratio.
D.Monitor the 'ReplicationLag' metric to check replication delay.
AnswerC

Cache hit ratio = CacheHits / (CacheHits + CacheMisses).

Why this answer

The cache hit ratio is calculated as CacheHits / (CacheHits + CacheMisses). Monitoring the 'CacheHits' and 'CacheMisses' CloudWatch metrics allows the specialist to compute the hit ratio and confirm whether it has decreased, which would explain increased latency. Option A is incorrect because CurrConnections shows the number of connections, not the cache hit ratio.

Option B is incorrect because Evictions indicates keys being evicted, which can affect the cache but does not directly measure the hit ratio. Option D is incorrect because ReplicationLag measures replication delay, not cache performance.

Full explanation →
1399
MCQhard

A company is designing a document management system using Amazon DocumentDB. Each document is up to 10 MB. The application needs to retrieve multiple documents by their IDs in a single request. The IDs are known at query time. Which query pattern is most efficient?

A.Use a find operation with the $or operator on the _id field.
B.Use a scan operation with a filter on the _id field.
C.Use a find operation with the $in operator on the _id field.
D.Issue multiple get operations in parallel.
AnswerC

Uses index on _id efficiently.

Why this answer

The `$in` operator on the `_id` field allows DocumentDB to use the primary key index directly, retrieving multiple documents in a single round trip with minimal overhead. This is the most efficient pattern because it leverages the clustered index on `_id` and avoids the performance penalty of multiple queries or full scans.

Exam trap

The trap here is that candidates often assume parallel `get` operations (Option D) are fastest because they think concurrency equals speed, but they overlook the overhead of multiple network round trips and the fact that DocumentDB's `$in` operator performs a single index seek for all IDs, which is far more efficient under load.

How to eliminate wrong answers

Option A is wrong because the `$or` operator on `_id` forces DocumentDB to evaluate each condition separately, often resulting in an index scan or a collection scan rather than a single index seek, which is less efficient than `$in`. Option B is wrong because a scan operation with a filter on `_id` ignores the primary key index entirely, reading every document in the collection and then filtering, which is extremely inefficient for large collections. Option D is wrong because issuing multiple `get` operations in parallel increases network round trips and connection overhead, and DocumentDB does not benefit from parallel single-document lookups as much as a batched index seek via `$in`.

Full explanation →
1400
MCQeasy

A developer needs to migrate a 500 GB MongoDB database to Amazon DocumentDB. Which approach will minimize application downtime?

A.Write a custom script to migrate data in batches
B.Export data from MongoDB, upload to S3, and import into DocumentDB
C.Use AWS DMS with ongoing replication from MongoDB to DocumentDB
D.Use AWS DataSync to transfer data files
AnswerC

Minimal downtime with live migration.

Why this answer

AWS DMS with ongoing replication (change data capture) allows you to perform a full load of the existing 500 GB MongoDB data into DocumentDB while continuously replicating new changes from the source. This minimizes application downtime because you can cut over to DocumentDB only after the target is fully synchronized, rather than taking the source offline for the entire migration duration.

Exam trap

The trap here is that candidates often choose Option B (export/import) because it seems straightforward, but they overlook the need for ongoing replication to minimize downtime, which is the key requirement in the question.

How to eliminate wrong answers

Option A is wrong because writing a custom script to migrate data in batches would require the application to be offline or heavily throttled during each batch transfer, and it lacks built-in change data capture to handle ongoing writes, leading to significant downtime. Option B is wrong because exporting data from MongoDB, uploading to S3, and importing into DocumentDB is a one-time bulk operation that does not capture incremental changes; the application must be stopped during the export and import windows to maintain consistency, causing extended downtime. Option D is wrong because AWS DataSync is designed for transferring files over NFS/SMB and does not support MongoDB’s document model or BSON format, making it incapable of migrating a MongoDB database to DocumentDB.

Full explanation →
1401
MCQhard

A company is using Amazon Aurora MySQL-Compatible Edition. The database administrator notices that the Aurora cluster has a high number of binary log (binlog) files in the cluster volume, consuming significant storage. The binlog retention period is set to 24 hours. What is the most efficient way to reduce the storage consumed by binlog files without compromising point-in-time recovery (PITR)?

A.Manually delete binlog files from the DB instance using the 'PURGE BINARY LOGS' command.
B.Use the 'Binary Log Export' feature to export binlogs to Amazon S3 and delete them from the cluster.
C.Disable binary logging on the Aurora cluster by setting the binlog_format parameter to OFF.
D.Reduce the binlog retention period to 1 hour.
AnswerC

Aurora does not require binlogs for PITR; disabling them saves storage and improves performance.

Why this answer

Aurora MySQL does not rely on binlogs for point-in-time recovery (PITR); it uses its own storage-based recovery. Disabling binary logging eliminates binlog generation entirely, saving storage and reducing I/O overhead. Option A is incorrect because manually deleting binlog files is not recommended by AWS and does not address the root cause.

Option B is incorrect because exporting binlogs to Amazon S3 does not reduce the storage consumed within the cluster volume. Option D is incorrect because reducing the retention period only limits how long binlogs are kept, but binlogs still accumulate and consume storage until they expire.

Full explanation →
1402
MCQeasy

A startup needs a fully managed relational database with automated backups and scaling. They expect unpredictable workloads. Which AWS service meets these requirements?

A.Amazon DynamoDB
B.Amazon Redshift
C.Amazon Aurora Serverless
D.Amazon ElastiCache
AnswerC

Fully managed relational database with auto-scaling and backups.

Why this answer

Amazon Aurora Serverless is a fully managed relational database that automatically scales capacity up or down based on application demand, making it ideal for unpredictable workloads. It also provides automated backups, continuous backups to Amazon S3, and point-in-time recovery, meeting all stated requirements.

Exam trap

The trap here is that candidates often confuse DynamoDB's on-demand scaling with relational database requirements, overlooking that DynamoDB is NoSQL and not relational, or they mistakenly think Redshift's scaling capabilities apply to transactional workloads.

How to eliminate wrong answers

Option A is wrong because Amazon DynamoDB is a NoSQL key-value and document database, not a relational database, so it does not meet the requirement for a relational database. Option B is wrong because Amazon Redshift is a petabyte-scale data warehouse optimized for analytical workloads, not a transactional relational database, and it does not automatically scale for unpredictable transactional workloads. Option D is wrong because Amazon ElastiCache is an in-memory caching service (supporting Redis or Memcached), not a relational database, and it does not provide automated backups or scaling for persistent relational data.

Full explanation →
1403
MCQhard

A company is using Amazon Aurora MySQL-Compatible Edition. The security team requires that all connections to the database use SSL/TLS. The application currently connects using a standard JDBC connection string without SSL. What changes are needed to enforce SSL connections?

A.Modify the DB cluster parameter group to set require_secure_transport to ON.
B.Modify the DB cluster parameter group to set require_secure_transport to ON and update the application connection string to use SSL parameters.
C.Modify the security group to only allow traffic on port 3306 with the SSL flag.
D.Create an IAM role that requires SSL for database access and assign it to the application.
AnswerB

This enforces SSL and ensures the application uses it.

Why this answer

To enforce SSL for all connections to an Amazon Aurora MySQL database, you must modify the DB cluster parameter group to set require_secure_transport to ON, which rejects non-SSL connections. Additionally, the application's JDBC connection string must be updated to include SSL parameters (e.g., useSSL=true) so that the application initiates an SSL connection. Option A is incomplete because modifying the parameter group alone does not enforce SSL on existing connections if the application does not use SSL.

Option C is incorrect because security groups do not have an SSL flag; they control network access based on IP and port. Option D is incorrect because IAM roles do not enforce SSL encryption; they handle authentication and authorization.

Full explanation →
1404
MCQmedium

A company is using Amazon DynamoDB to store customer session data. The security team requires that all data is encrypted at rest using a customer-managed KMS key, and that access to the key is restricted to specific IAM roles. The company also wants to ensure that DynamoDB Accelerator (DAX) cluster is encrypted. Which steps should be taken to meet these requirements?

A.Create the DynamoDB table with encryption using a customer-managed KMS key. Create the DAX cluster and enable encryption at rest using the same KMS key.
B.Create the DynamoDB table without encryption, then use the AWS CLI to enable encryption after creation.
C.Create the DynamoDB table with default encryption, and create the DAX cluster with a separate customer-managed KMS key.
D.Create the DynamoDB table with encryption using a customer-managed key, and enable encryption in transit on the DAX cluster using TLS.
AnswerA

Correct. DynamoDB tables support encryption at rest with a customer-managed KMS key at creation. DAX clusters can also be encrypted at rest using the same or a different KMS key.

Why this answer

DynamoDB tables can be encrypted at rest with a customer-managed KMS key at creation, and DAX clusters also support encryption at rest using the same or a different KMS key, meeting the requirement. Option B is wrong because DynamoDB encryption cannot be enabled after table creation; it must be specified at creation. Option C is wrong because it uses default encryption (AWS managed key) for the table, not a customer-managed key.

Option D is wrong because it refers to encryption in transit (TLS) for DAX, but the requirement is for encryption at rest; DAX encryption at rest is not addressed.

Full explanation →
1405
MCQeasy

A developer reports that an Amazon ElastiCache for Redis cluster's memory usage is consistently above 90%. The application uses Redis for caching and session storage. Which configuration change would MOST effectively reduce memory pressure?

A.Enable eviction with volatile-LRU policy
B.Scale up to a larger node type
C.Enable AOF persistence to free memory
D.Disable replication to reduce memory overhead
AnswerA

Correct. The volatile-LRU eviction policy removes keys with TTLs (like session data) that are least recently used, effectively freeing memory while preserving critical cached data without TTLs.

Why this answer

Enabling eviction with the volatile-LRU policy automatically removes the least recently used keys that have a TTL set, freeing memory without manual intervention. Option B (scaling up) reduces memory pressure but at higher cost; it is not the most effective change since eviction addresses the root cause by discarding stale data. Option C (AOF persistence) writes data to disk, not freeing memory; it can actually increase memory usage during writes.

Option D (disabling replication) may free some memory used for replica buffers but does not address the high memory utilization caused by cached data, and it impacts availability.

Full explanation →
1406
Multi-Selectmedium

A company is running an Amazon RDS for MySQL DB instance in a VPC. The security team requires that all connections to the database use SSL/TLS. Which combination of steps should be taken to enforce this? (Choose two.)

Select 2 answers
A.Create a new DB subnet group that isolates the DB instance in a private subnet without internet access.
B.Update the security group for the DB instance to deny inbound traffic on port 3306 from sources that do not have SSL.
C.Require database users to connect using the --ssl-ca parameter with the RDS certificate.
D.Modify the DB parameter group associated with the DB instance, setting the 'rds.force_ssl' parameter to 1.
E.Modify the DB option group associated with the DB instance, enabling the SSL option.
AnswersC, D

This ensures the client verifies the server certificate, which is necessary for SSL connections.

Why this answer

To enforce SSL on RDS MySQL, you set the rds.force_ssl parameter to 1 in the DB parameter group and require users to connect using the --ssl-ca option. The option group is for features like Oracle TDE, not SSL enforcement. Security group rules control network access, not encryption enforcement.

Option E is unnecessary if you set the parameter.

Full explanation →
1407
Multi-Selecthard

A team is using Amazon DynamoDB with auto scaling enabled. They notice that some requests are returning ProvisionedThroughputExceededException errors during a sudden traffic spike. The application uses strong consistent reads. Which two actions would help mitigate the throttling without over-provisioning capacity? (Choose two.)

Select 2 answers
A.Implement DynamoDB Accelerator (DAX) to cache read results.
B.Enable DynamoDB adaptive capacity.
C.Switch to eventually consistent reads for all queries.
D.Disable auto scaling and manually set higher capacity.
E.Use DynamoDB burst capacity for the spike.
AnswersA, B

Correct. DAX reduces the number of read requests to the table, lowering the consumed read capacity.

Why this answer

DAX acts as an in-memory cache for DynamoDB, reducing the number of read requests that consume provisioned throughput, thus mitigating throttling without increasing capacity. Option B is correct because adaptive capacity enables DynamoDB to automatically use unused throughput from other partitions to absorb traffic spikes, reducing ProvisionedThroughputExceededException errors. Option C is wrong because while eventually consistent reads consume half the read capacity, the application uses strong consistent reads, which may be required for data consistency, and this switch may not be acceptable.

Option D is wrong because disabling auto scaling and manually setting higher capacity would lead to over-provisioning and increased cost, contrary to the goal of mitigating throttling without over-provisioning. Option E is wrong because burst capacity is limited and not guaranteed; it can handle short spikes but is not a reliable mitigation for sudden spikes.

Full explanation →
1408
MCQmedium

A company uses Amazon DynamoDB to store session data for a web application. During peak hours, they experience occasional ProvisionedThroughputExceededException errors. The table has a read capacity of 1000 RCU and a write capacity of 500 WCU. The application uses strongly consistent reads. The traffic pattern shows short bursts of reads exceeding 1000 RCU. What is the MOST cost-effective way to handle these bursts without changing the application?

A.Enable DynamoDB Auto Scaling to adjust RCU dynamically
B.Increase RCU to 2000 and enable Auto Scaling
C.Switch to eventually consistent reads
D.Use DynamoDB Accelerator (DAX) for caching reads
AnswerA

Correct. Auto Scaling dynamically adjusts RCU based on demand, handling bursts cost-effectively without application changes.

Why this answer

Enabling DynamoDB Auto Scaling (Option A) is the most cost-effective solution because it automatically adjusts read capacity in response to traffic patterns, handling short bursts without manual intervention or over-provisioning. It does not require application changes. Option B is more expensive due to higher static capacity.

Option C requires switching to eventually consistent reads, which would change application behavior and may not be acceptable for session data requiring strong consistency. Option D adds cost and complexity with DAX without addressing read capacity limits directly.

Full explanation →
1409
MCQmedium

A company is migrating a 2 TB Oracle database to Amazon Aurora PostgreSQL. The on-premises database is 2 TB and the network link is 1 Gbps. The migration must complete within 48 hours with minimal data loss. The DBA has extracted the schema using AWS SCT and created an Aurora cluster. The DBA now needs to transfer the data. The DBA tried using AWS DMS over the network but estimates it will take 72 hours due to network overhead. The DBA also considered using an AWS Snowball Edge device but is concerned about the time to order and ship the device. Which approach should the DBA take to meet the deadline?

A.Use AWS DMS with change data capture and optimize the network by enabling compression
B.Use AWS S3 to upload the database dump and then load into Aurora
C.Use AWS Snowball Edge to transfer the database dump and then use DMS for change data capture
D.Use AWS DMS to perform a full load and then stop replication, accepting some data loss
AnswerC

Correct. Snowball Edge provides fast bulk transfer, and DMS CDC captures changes during and after the transfer, minimizing data loss and meeting the deadline.

Why this answer

It combines the speed of Snowball Edge for the initial bulk data transfer (2 TB) with AWS DMS for ongoing change data capture (CDC) to minimize data loss. Although ordering and shipping a Snowball device takes time, once received, the data transfer is much faster than over a 1 Gbps network, which would take over 48 hours. DMS alone (option A) is estimated to take 72 hours, exceeding the deadline.

Option B (S3 upload) still relies on network speed and would be too slow. Option D (full load only) abandons CDC and does not meet the minimal data loss requirement.

Full explanation →
1410
MCQeasy

A company uses Amazon DynamoDB for a session management store. The application writes and reads session data frequently. The team notices that write requests occasionally fail with ProvisionedThroughputExceededException. They want a cost-effective solution to handle these bursts. What should they do?

A.Increase the provisioned write capacity to a higher fixed value
B.Use DynamoDB Accelerator (DAX) to cache writes
C.Implement an Amazon SQS queue to buffer writes
D.Enable DynamoDB Auto Scaling for the table
AnswerD

Auto Scaling adjusts capacity based on actual usage, handling bursts cost-effectively.

Why this answer

DynamoDB Auto Scaling dynamically adjusts the provisioned write capacity based on actual traffic patterns, handling bursts cost-effectively by scaling up during spikes and down during lulls. This avoids the fixed-cost overhead of a higher provisioned value (Option A) and directly addresses the ProvisionedThroughputExceededException by ensuring sufficient capacity during bursts.

Exam trap

The trap here is that candidates often confuse DAX (a read cache) with a write buffer, or assume that a higher fixed capacity is the only way to handle bursts, ignoring the cost-efficiency requirement that points to Auto Scaling.

How to eliminate wrong answers

Option A is wrong because increasing provisioned write capacity to a higher fixed value would eliminate the bursts but at a constant higher cost, which is not cost-effective for variable workloads. Option B is wrong because DynamoDB Accelerator (DAX) is an in-memory cache for reads, not writes; it cannot buffer or absorb write bursts or prevent ProvisionedThroughputExceededException on write operations. Option C is wrong because while an SQS queue can buffer write requests, it introduces asynchronous processing and latency, which is unsuitable for a session management store that requires immediate, synchronous writes to maintain session consistency.

Full explanation →
1411
MCQhard

A company is migrating a 5 TB Oracle database to Amazon RDS for Oracle. The database has several large tables with LOB columns. The migration must have minimal downtime. Which approach should be taken?

A.Use Oracle GoldenGate to replicate data to RDS.
B.Use AWS DMS with LOB support and ongoing replication from Oracle to RDS.
C.Use Oracle Data Pump to export the database and import into RDS.
D.Copy the database files to Amazon S3 and use the rdsadmin.rds_restore_from_s3 procedure.
AnswerB

AWS DMS supports LOBs and can replicate changes continuously, minimizing downtime.

Why this answer

AWS DMS with LOB support and ongoing replication (change data capture, CDC) is the correct approach because it enables a near-zero downtime migration by continuously replicating changes from the source Oracle database to Amazon RDS for Oracle after an initial full load. DMS handles large LOB columns efficiently using its 'Full LOB mode' or 'Limited LOB mode' settings, which are critical for tables with LOB data. The combination of full load + CDC ensures minimal downtime, as the target remains synchronized until cutover.

Exam trap

The trap here is that candidates may choose Oracle GoldenGate (Option A) because it is a well-known replication tool, but the exam expects you to recognize that AWS DMS is the native, fully managed service designed for minimal-downtime migrations to RDS, and GoldenGate introduces unnecessary complexity and cost.

How to eliminate wrong answers

Option A is wrong because Oracle GoldenGate requires additional licensing costs and complex setup, and while it can achieve minimal downtime, it is not the simplest or most cost-effective AWS-native solution for this scenario; AWS DMS is the recommended service for database migrations to RDS. Option C is wrong because Oracle Data Pump is a logical export/import tool that requires the source database to be in a consistent state (often requiring downtime) and cannot provide ongoing replication, so it does not meet the minimal downtime requirement. Option D is wrong because the rdsadmin.rds_restore_from_s3 procedure is used for restoring physical backups (e.g., from RMAN or Oracle Transportable Tablespaces) to RDS, not for migrating a live database with minimal downtime, and it requires the database files to be in a specific format and typically involves downtime during the restore process.

Full explanation →
1412
Multi-Selecthard

Which TWO design patterns help meet ACID compliance requirements in a distributed database environment while maintaining high availability?

Select 2 answers
A.Use an eventually consistent read model to improve performance.
B.Implement Amazon Aurora Global Database for cross-Region ACID transactions.
C.Adopt a saga pattern to manage distributed transactions.
D.Use Amazon DynamoDB Transactions for multi-item ACID operations.
E.Implement DynamoDB Streams to capture changes for audit.
AnswersB, D

Aurora Global Database provides ACID within each region.

Why this answer

Amazon Aurora Global Database is designed to support cross-Region ACID transactions by using a primary cluster in one AWS Region and up to five secondary read-only clusters in other Regions. It replicates data asynchronously from the primary to the secondary Regions, but each secondary cluster can be promoted to a primary in under a minute, ensuring high availability while maintaining ACID compliance for transactions that span multiple Regions.

Exam trap

The DBS-C01 exam often tests the distinction between ACID-compliant distributed transactions and patterns that provide only eventual consistency or compensation-based semantics, leading candidates to select the saga pattern or eventually consistent models as valid ACID solutions.

Full explanation →
1413
MCQeasy

A gaming company uses Amazon DynamoDB as the database for user profiles and game state. The application requires strongly consistent reads for the user's own profile, but eventually consistent reads for leaderboard queries. How should the company design the table and queries?

A.Create two separate tables: one for strong consistency and one for eventual consistency.
B.Use the ConsistentRead parameter set to true for profile queries and false for leaderboard queries.
C.Enable DynamoDB Accelerator (DAX) for strong consistency on all reads.
D.Configure DynamoDB Streams to replicate data to a second table for strong consistency.
AnswerB

This allows per-request consistency control.

Why this answer

DynamoDB supports both strongly consistent reads and eventually consistent reads on the same table, controlled by the `ConsistentRead` parameter in the `GetItem`, `Query`, or `Scan` API calls. Setting `ConsistentRead=true` for profile queries ensures the most up-to-date data, while `ConsistentRead=false` (the default) for leaderboard queries provides lower latency and higher throughput, which is ideal for read-heavy, non-critical data. This design avoids the cost and complexity of multiple tables or additional services.

Exam trap

The trap here is that candidates often assume strong consistency requires a separate table or a caching layer like DAX, but DynamoDB natively supports both consistency models on the same table via a simple API parameter, making the other options over-engineered or incorrect.

How to eliminate wrong answers

Option A is wrong because creating two separate tables for consistency levels is unnecessary and wasteful; DynamoDB supports both consistency models on a single table via the `ConsistentRead` parameter. Option C is wrong because DAX is a caching layer that provides eventually consistent reads by default and does not guarantee strongly consistent reads; it is designed for read-heavy workloads with relaxed consistency, not for enforcing strong consistency. Option D is wrong because DynamoDB Streams is used for change data capture and replication, not for serving strongly consistent reads; replicating to a second table would introduce eventual consistency between tables and add latency and cost without solving the requirement.

Full explanation →
1414
MCQmedium

A company is migrating an on-premises Oracle OLTP database to Amazon Aurora PostgreSQL. The database has a complex schema with stored procedures, triggers, and sequences. During the migration, the team notices that the conversion tool reports several incompatibilities. Which strategy should the team use to handle the database schema changes with minimal downtime?

A.Deploy Amazon RDS for PostgreSQL with Babelfish to run Oracle PL/SQL code natively.
B.Use AWS Database Migration Service (DMS) with the AWS Schema Conversion Tool (SCT) to convert the schema and migrate data, then handle remaining incompatibilities during a cutover window.
C.Use pg_dump and pg_restore to migrate the schema, and then test and fix any errors.
D.Manually rewrite all stored procedures and triggers to PostgreSQL syntax before migration.
AnswerB

SCT automates schema conversion, and DMS supports minimal downtime via ongoing replication.

Why this answer

AWS DMS with SCT is the recommended approach for heterogeneous migrations like Oracle to Aurora PostgreSQL. SCT converts the schema (including stored procedures, triggers, and sequences) and identifies incompatibilities, while DMS handles ongoing replication to minimize downtime. The remaining incompatibilities can be resolved during a planned cutover window, which is the standard strategy for complex schema migrations with minimal downtime.

Exam trap

The trap here is that candidates may assume Babelfish can handle Oracle PL/SQL because it supports SQL Server T-SQL, but Babelfish is specifically for SQL Server compatibility, not Oracle.

How to eliminate wrong answers

Option A is wrong because Babelfish is designed for SQL Server T-SQL compatibility, not Oracle PL/SQL; it cannot run Oracle PL/SQL code natively. Option C is wrong because pg_dump and pg_restore are used for PostgreSQL-to-PostgreSQL migrations, not for converting Oracle schemas; they would fail on Oracle-specific syntax and do not handle schema conversion. Option D is wrong because manually rewriting all stored procedures and triggers before migration would cause significant downtime and is not a minimal-downtime strategy; SCT automates most of the conversion, and manual fixes are better handled during cutover.

Full explanation →
1415
MCQeasy

A developer is configuring an Amazon RDS for PostgreSQL DB instance. The application connects using IAM database authentication. Which setting must be enabled on the DB instance for IAM authentication to work?

A.Set the database port to 5432.
B.Set the 'rds.force_ssl' parameter to 1.
C.Ensure the DB instance is publicly accessible.
D.Change the master username to 'iam_user'.
AnswerB

Correct. The 'rds.force_ssl' parameter must be set to 1 to enforce TLS, which is required for IAM database authentication.

Why this answer

IAM database authentication for Amazon RDS PostgreSQL requires an encrypted connection to protect the authentication token. Setting the 'rds.force_ssl' parameter to 1 enforces TLS/SSL connections between the client and the database, which is a prerequisite for IAM authentication. Option A is incorrect because the port (default 5432) does not need to change for IAM.

Option C is incorrect because the DB instance can be private within a VPC; IAM authentication works over private or public connections as long as TLS is enforced. Option D is incorrect because the master username is not changed; IAM authentication uses database users that are mapped to IAM identities.

Full explanation →
1416
MCQeasy

A company is using Amazon RDS for MySQL. They want to audit all database logins and failed login attempts. Which option should they enable?

A.Set the parameter log_queries_not_using_indexes = 'ON' in the DB parameter group.
B.Set the parameter audit_log = 'ON' in the DB parameter group.
C.Set the parameter general_log = 'ON' in the DB parameter group.
D.Set the parameter slow_query_log = 'ON' in the DB parameter group.
AnswerB

The audit log captures connection events, including successful and failed logins.

Why this answer

Setting the parameter audit_log = 'ON' enables audit logging for MySQL, which logs connections and disconnections, including failed login attempts. Option A is wrong because log_queries_not_using_indexes logs queries that do not use indexes, not login attempts. Option C is wrong because general_log logs all queries, not just logins.

Option D is wrong because slow_query_log logs only slow queries.

Full explanation →
1417
MCQhard

A financial services company uses Amazon DynamoDB to store transaction records. The table has a partition key of 'account_id' and a sort key of 'transaction_time'. Recent queries for a specific account's transactions within a time range are experiencing high latency. The table has read capacity units set to auto-scaling. Which design change would most improve query performance?

A.Change the sort key to a composite attribute for better filtering.
B.Enable DynamoDB Accelerator (DAX) for the table.
C.Increase the read capacity units for the table.
D.Create a global secondary index with a different partition key.
AnswerD

GSI with a different key distributes reads across partitions.

Why this answer

Creating a global secondary index (GSI) with a different partition key can distribute read traffic across multiple partitions, avoiding hot partitions caused by frequent access to the same account_id. This improves query performance for time-range queries on a specific account. Option A (changing sort key) would not help if the partition itself is overloaded.

Option B (DAX) caches results but does not address hot partitions, and may not help if the queries are not cacheable. Option C (increasing RCUs) may not help if the existing partition is hot due to throttling at the partition level.

Full explanation →
1418
MCQmedium

A company is migrating a 10 TB SQL Server database to Amazon RDS for SQL Server. They need to minimize the migration time and cost. Which approach should they use?

A.Use AWS Snowball Edge to transfer backup files
B.Use AWS Schema Conversion Tool to convert schema and then bulk insert
C.Use AWS DMS with a full load and ongoing replication
D.Use native SQL Server backup to S3 and restore to RDS
AnswerC

Efficient and supports minimal downtime.

Why this answer

AWS DMS with a full load and ongoing replication is the most efficient approach for migrating a 10 TB SQL Server database to Amazon RDS for SQL Server because it performs a one-time full load of the existing data and then continuously replicates ongoing changes, minimizing downtime. DMS handles schema conversion automatically for homogeneous migrations (SQL Server to SQL Server) and supports resumable tasks, which reduces the risk of restarting from scratch on failure. This approach balances speed and cost by using the network for data transfer without the overhead of physical media or manual restore steps.

Exam trap

The trap here is that candidates often assume native backup-to-S3 (Option D) is the fastest because it's a familiar SQL Server tool, but they overlook that DMS's streaming and CDC capabilities minimize downtime and total migration time for large databases, especially when ongoing replication is needed.

How to eliminate wrong answers

Option A is wrong because AWS Snowball Edge is designed for offline data transfer of large datasets (typically >10 TB or limited bandwidth), but for a 10 TB database with reasonable network connectivity, using Snowball introduces significant latency for shipping, handling, and data ingestion, increasing total migration time and cost compared to direct DMS. Option B is wrong because the AWS Schema Conversion Tool (SCT) is used for heterogeneous migrations (e.g., Oracle to SQL Server) to convert schema objects; for a homogeneous SQL Server to SQL Server migration, SCT is unnecessary and adds complexity without benefit, and bulk insert alone does not minimize downtime or handle ongoing replication. Option D is wrong because native SQL Server backup to S3 and restore to RDS requires manual steps, does not support ongoing replication (so it incurs downtime for the final cutover), and is slower for large databases due to the need to upload full backup files to S3 and then restore, which can be network-intensive and less efficient than DMS's streaming approach.

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1419
MCQmedium

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?

A.Verify that the security group for the RDS instance allows inbound traffic from the application
B.Check if the subnet group for the RDS instance is correctly configured
C.Check the DNS resolution of the RDS endpoint from the application server
D.Restart the RDS instance to force a new connection
AnswerC

The CNAME record should be updated; stale DNS could cause connection failures.

Why this answer

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.

Full explanation →
1420
Multi-Selectmedium

A company is designing a new application that requires a relational database with read replicas for reporting. The application has unpredictable traffic patterns. The company wants to minimize operational overhead and automatically scale compute capacity. Which TWO services should the company consider?

Select 2 answers
A.Amazon DynamoDB Accelerator (DAX)
B.Amazon RDS for MySQL with Multi-AZ
C.Amazon RDS for PostgreSQL with read replicas
D.Amazon Aurora Serverless v2
E.Amazon RDS Proxy
AnswersD, E

Amazon Aurora Serverless v2 automatically scales compute capacity, supports read replicas for reporting, and minimizes operational overhead, making it a correct choice.

Why this answer

The company requires a relational database with read replicas for reporting, minimal operational overhead, and automatic scaling of compute capacity. Amazon Aurora Serverless v2 (D) meets all these requirements: it is a relational database compatible with PostgreSQL and MySQL, automatically scales compute capacity based on demand, and supports up to 15 read replicas for reporting workloads. Amazon RDS Proxy (E) provides connection pooling to handle unpredictable traffic patterns efficiently, reducing overhead and improving scalability.

Together, these two services fulfill the requirements without the need for an additional database instance, making option C unnecessary.

Exam trap

The trap here is that candidates may think they need a separate database service like RDS for PostgreSQL to get read replicas, not realizing that Aurora Serverless v2 already supports read replicas with automatic scaling. They might also overlook the role of RDS Proxy in managing unpredictable traffic.

Full explanation →
1421
MCQmedium

Refer to the exhibit. A company uses this DynamoDB table to store user session data. The application frequently queries by user_id alone to get all sessions for a user. However, the query is slow. What is the most likely cause?

A.The table's partition key is session_id, not user_id, so querying by user_id requires a scan.
B.The table has no sort key on user_id.
C.The table has too many items, causing slow scans.
D.The provisioned read capacity is too low.
AnswerA

Without a GSI on user_id, queries on user_id are scans.

Why this answer

The table's primary key is session_id, not user_id. Querying by user_id without a secondary index forces DynamoDB to perform a full table scan, which reads every item and is significantly slower than a query operation. This is the most likely cause of the slow performance.

Exam trap

The trap here is that candidates often assume any attribute can be queried efficiently, failing to recognize that DynamoDB requires a primary key or index for efficient lookups, and that a scan is the fallback for non-key attributes.

How to eliminate wrong answers

Option B is wrong because a sort key on user_id would not help; the table already has a sort key (timestamp), but the issue is that user_id is not the partition key, so queries by user_id still require a scan. Option C is wrong because while a large number of items can slow scans, the fundamental problem is the access pattern mismatch (scan vs. query), not just item count. Option D is wrong because low provisioned read capacity would cause throttling (ProvisionedThroughputExceededException), not inherently slow queries; the described slowness is due to scanning, not capacity limits.

Full explanation →
1422
MCQeasy

A company is migrating an on-premises MongoDB database to Amazon DocumentDB (with MongoDB compatibility). Which AWS service can perform the migration with minimal downtime using ongoing replication?

A.AWS Database Migration Service (AWS DMS)
B.Amazon CloudWatch
C.AWS Schema Conversion Tool (AWS SCT)
D.AWS Glue
AnswerA

DMS supports MongoDB to DocumentDB migration with ongoing replication.

Why this answer

AWS DMS supports MongoDB as a source and Amazon DocumentDB as a target, enabling live migration with ongoing replication via change data capture (CDC). This allows the company to migrate with minimal downtime by continuously applying changes from the source MongoDB to the target DocumentDB until cutover.

Exam trap

The trap here is that candidates often confuse AWS SCT's schema conversion capability with full migration including data replication, but SCT does not perform ongoing data sync or CDC, which is essential for minimal-downtime migrations.

How to eliminate wrong answers

Option B is wrong because Amazon CloudWatch is a monitoring and observability service, not a data migration tool; it cannot perform database replication or schema conversion. Option C is wrong because AWS Schema Conversion Tool (AWS SCT) is designed for converting database schemas (e.g., from Oracle to Amazon Aurora) but does not handle ongoing data replication or live migration for MongoDB to DocumentDB. Option D is wrong because AWS Glue is a serverless ETL service for data preparation and transformation, not a database migration service; it lacks the CDC capability needed for minimal-downtime replication of a MongoDB database.

Full explanation →
1423
Multi-Selecteasy

A company is designing a database for a social media application that requires storing user profiles, posts, and follower relationships. The application needs low-latency queries for user timelines and social graph traversals. Which TWO AWS database services should the database specialist consider? (Choose TWO.)

Select 2 answers
A.Amazon Timestream
B.Amazon Neptune
C.Amazon Redshift
D.Amazon RDS for MySQL
E.Amazon DynamoDB
AnswersB, E

Neptune is a graph database purpose-built for social graph traversals.

Why this answer

Amazon Neptune is a fully managed graph database service optimized for storing and querying highly connected data, such as social graphs. It supports property graph and RDF models, enabling low-latency traversals of follower relationships and user timelines using Gremlin or SPARQL queries.

Exam trap

The trap here is that candidates may choose Amazon RDS for MySQL (Option D) thinking relational databases can handle graph queries with joins, but they fail to recognize that Neptune provides native graph traversal performance that relational databases cannot match for deeply connected data.

Full explanation →
1424
MCQeasy

A database administrator needs to monitor Amazon RDS for PostgreSQL connections and terminate idle connections that have been open for more than 1 hour. Which combination of steps should be taken?

A.Use Amazon RDS Performance Insights to view active connections and manually terminate idle connections via the AWS Management Console.
B.Configure an RDS event subscription for 'connection' events and send to Amazon SNS. Use SNS to notify an EC2 instance that runs a script to terminate idle connections.
C.Enable audit logging and stream to Amazon CloudWatch Logs. Create a metric filter for connection events. Use CloudWatch Alarm to trigger a Lambda function that runs a SQL query to terminate idle connections.
D.Enable RDS Enhanced Monitoring and configure a CloudWatch alarm to invoke an AWS Lambda function that terminates idle connections.
AnswerC

Audit logs capture connection events; metric filter and Lambda automate termination.

Why this answer

The correct approach is Option C. Enable audit logging (e.g., using the pgaudit extension) and stream logs to CloudWatch Logs. Create a metric filter to identify connections idle for over 1 hour based on log patterns.

Set a CloudWatch Alarm to invoke an AWS Lambda function, which executes a SQL query like pg_terminate_backend(pid) to terminate idle connections. Option A: Performance Insights provides metrics but no automated termination. Option B: RDS event subscriptions do not include idle connection details.

Option D: Enhanced Monitoring provides OS-level metrics, not connection idle time.

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1425
Multi-Selectmedium

A company is designing a database for a global e-commerce platform with strong consistency requirements. The database must support cross-region disaster recovery with RPO < 1 second and RTO < 1 minute. Which TWO AWS database services meet these requirements?

Select 2 answers
A.Amazon Aurora Global Database
B.Amazon RDS Multi-AZ
C.Amazon Redshift with cross-region snapshot copy
D.Amazon ElastiCache for Redis with Global Datastore
E.Amazon DynamoDB Global Tables
AnswersA, E

Aurora Global Database replicates across regions with RPO of 1 second and RTO of 1 minute.

Why this answer

Amazon Aurora Global Database uses storage-based replication with a typical latency of under 1 second, supporting cross-region disaster recovery with an RPO of less than 1 second and an RTO of less than 1 minute by promoting a secondary region to primary. Amazon DynamoDB Global Tables provide multi-region, fully replicated tables with strong consistency and automatic failover, achieving RPO of less than 1 second and RTO of less than 1 minute through active-active replication.

Exam trap

The trap here is that candidates confuse Multi-AZ (single-region HA) with cross-region DR, or assume that snapshot-based replication (like Redshift) can meet sub-second RPO, when in reality only continuous replication services like Aurora Global Database and DynamoDB Global Tables can achieve such low RPO and RTO.

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