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AWS Certified Database Specialty DBS-C01 (DBS-C01) — Questions 976–1050

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

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976

Multi-Selecthard

A company is deploying a new application using Amazon DynamoDB. They need to ensure data durability and availability. Which THREE steps should they take? (Choose three.)

Select 3 answers

A.Configure Auto Scaling for read and write capacity

B.Enable DynamoDB Accelerator (DAX)

C.Encrypt data at rest using AWS KMS

D.Enable point-in-time recovery (PITR)

E.Use DynamoDB Global Tables

AnswersA, D, E

Ensures availability under load.

Why this answer

DynamoDB Auto Scaling automatically adjusts provisioned read/write capacity based on actual traffic patterns, preventing throttling while controlling costs. This directly supports availability by ensuring the table can handle demand spikes without manual intervention.

Exam trap

The trap here is confusing performance-enhancing features (DAX, encryption) with durability and availability mechanisms, leading candidates to select options that improve speed or security instead of data persistence and regional resilience.

Full explanation →

977

MCQeasy

A company needs to store time-series data from IoT sensors. Each sensor sends a reading every minute. The data is rarely accessed after 30 days. The query pattern is to retrieve all readings for a specific sensor within a time range. Which AWS database is most cost-effective?

A.Amazon Timestream

B.Amazon Redshift

C.Amazon DynamoDB with Time-to-Live (TTL)

D.Amazon RDS for PostgreSQL with partitioning

AnswerA

Optimized for time-series data with automatic storage tiering.

Why this answer

Amazon Timestream is purpose-built for time-series data, offering automatic tiered storage (in-memory for recent data and magnetic for historical data) and built-in time-series functions. For IoT sensors sending readings every minute with rare access after 30 days, Timestream's cost-effective storage tiering and efficient querying for a specific sensor within a time range make it the most suitable and economical choice.

Exam trap

The trap here is that candidates often choose DynamoDB with TTL because of its scalability and automatic expiry, overlooking that TTL only deletes data after a fixed period and does not optimize for time-range queries or cost-effective storage of cold data, which Timestream handles natively.

How to eliminate wrong answers

Option B (Amazon Redshift) is wrong because it is a columnar data warehouse designed for complex analytical queries on large datasets, not for high-frequency time-series ingestion with low-latency point-in-time queries; its per-hour pricing and overhead make it cost-prohibitive for this use case. Option C (Amazon DynamoDB with TTL) is wrong because while TTL can expire old data, DynamoDB lacks native time-series optimizations, requiring complex composite key designs and manual partitioning to query all readings for a sensor within a time range, leading to higher costs due to read/write capacity units and storage for rarely accessed data. Option D (Amazon RDS for PostgreSQL with partitioning) is wrong because it requires manual partition management, incurs costs for provisioned storage and I/O even for cold data, and lacks automatic tiered storage, making it less cost-effective than a purpose-built time-series database for this workload.

Full explanation →

978

Multi-Selecteasy

A company is setting up an Amazon Aurora MySQL-compatible database. The security policy requires that all traffic between the application and database be encrypted in transit. Which TWO steps should be taken?

Select 2 answers

A.Use a Network Load Balancer with a TLS listener in front of the Aurora cluster.

B.Place the Aurora cluster in a private subnet with a VPN connection.

C.Set the 'require_secure_transport' parameter to 'ON' in the DB cluster parameter group.

D.Configure the application connection string to use the SSL-enabled endpoint (port 3306 with SSL).

E.Enable encryption at rest using AWS KMS.

AnswersC, D

This enforces SSL connections from clients.

Why this answer

To encrypt data in transit, you need to enforce SSL on the server side and ensure the client uses SSL. Setting the 'require_secure_transport' parameter to 'ON' in the DB cluster parameter group (Option C) forces the server to require SSL connections. Configuring the application connection string to use the SSL-enabled endpoint (Option D) ensures the client connects using SSL.

Option A is unnecessary because Aurora supports SSL natively without a load balancer. Option B does not encrypt traffic; a VPN only secures the network path but does not enforce encryption at the database layer. Option E encrypts data at rest, not in transit.

Full explanation →

979

MCQhard

A company is planning to migrate a 3 TB PostgreSQL database from an on-premises data center to Amazon Aurora PostgreSQL. The migration must have near-zero downtime. The on-premises database is using a custom pg_hba.conf that restricts access to specific IP ranges. Which combination of steps will meet the requirements?

A.Create an Aurora Replica of the on-premises database using cross-Region replication.

B.Use pg_dump and pg_restore to export and import the database during a maintenance window.

C.Use AWS DMS with ongoing replication to an Aurora DB cluster. Configure the source endpoint with the appropriate connection settings.

D.Use AWS SCT to convert the schema and then perform a full data load using AWS DMS.

AnswerC

DMS supports ongoing replication, and the source endpoint can be configured to connect to the on-premises database.

Why this answer

AWS DMS with ongoing replication (CDC) enables near-zero downtime migration by continuously synchronizing changes from the source PostgreSQL database to the target Aurora PostgreSQL cluster after an initial full load. Configuring the source endpoint with the correct connection settings, including the custom pg_hba.conf IP ranges, allows DMS to connect securely. This approach meets the requirement for minimal downtime, unlike batch export/import methods.

Exam trap

The trap here is that candidates may confuse AWS DMS with schema conversion tools (SCT) or assume that Aurora Replicas can be used for external replication, when in fact DMS with CDC is the correct service for heterogeneous or homogeneous migrations with minimal downtime.

How to eliminate wrong answers

Option A is wrong because Aurora Replicas are read replicas within an Aurora cluster, not a replication mechanism from an on-premises database; cross-Region replication refers to Aurora-to-Aurora replication, not from an external PostgreSQL instance. Option B is wrong because pg_dump and pg_restore require a maintenance window with downtime to export and import the full 3 TB database, which violates the near-zero downtime requirement. Option D is wrong because AWS SCT is used for schema conversion when migrating to a different database engine (e.g., Oracle to Aurora), but the source and target are both PostgreSQL, so no schema conversion is needed; DMS alone can handle the migration without SCT.

Full explanation →

980

Multi-Selecthard

A company is migrating an on-premises SQL Server database to Amazon RDS for SQL Server. During the migration, the DBA notices that the network latency between the source and target is high. Which THREE actions can the DBA take to reduce the impact of network latency on migration performance?

Select 3 answers

A.Increase the size of the RDS instance

B.Use multiple parallel tasks in AWS DMS

C.Use AWS Direct Connect for a dedicated network connection

D.Enable data compression for the migration task

E.Use AWS Site-to-Site VPN

AnswersB, C, D

Parallel tasks improve throughput, mitigating latency effects.

Why this answer

Options B, C, and D are correct. Using multiple parallel tasks in AWS DMS (option B) improves throughput by transferring data in parallel. Using AWS Direct Connect (option C) provides a dedicated, low-latency network connection.

Enabling data compression (option D) reduces the amount of data transferred, mitigating latency impact. Option A is incorrect because increasing the RDS instance size does not reduce network latency; it only affects database processing capacity. Option E is incorrect because using a VPN can add overhead and may not reduce latency.

Full explanation →

981

MCQmedium

A financial services company needs a database to store transaction records with strict ACID compliance and the ability to run complex JOIN queries for reporting. The workload is read-heavy with occasional batch inserts. Which AWS database service should they choose?

A.Amazon RDS for PostgreSQL

B.Amazon DynamoDB

C.Amazon Aurora Serverless

D.Amazon Timestream

AnswerA

PostgreSQL is fully ACID-compliant and supports complex queries.

Why this answer

Amazon RDS for PostgreSQL is the correct choice because it provides full ACID compliance through its support for PostgreSQL's MVCC (Multi-Version Concurrency Control) and WAL (Write-Ahead Logging), ensuring transactional integrity for financial transaction records. It also excels at complex JOIN queries for reporting, leveraging PostgreSQL's advanced query optimizer and support for foreign keys, indexes, and CTEs. The read-heavy workload with occasional batch inserts is well-suited to RDS for PostgreSQL, as it can scale read replicas and handle batch operations efficiently with proper indexing.

Exam trap

The trap here is that candidates often choose DynamoDB for its scalability and ACID support, overlooking that it cannot perform complex JOINs required for relational reporting, or they select Aurora Serverless assuming it is always the best Aurora option, not realizing it is designed for variable workloads rather than steady read-heavy patterns.

How to eliminate wrong answers

Option B (Amazon DynamoDB) is wrong because it is a NoSQL key-value and document database that does not support complex JOIN queries natively, and while it offers ACID transactions via DynamoDB Transactions, it is optimized for single-digit millisecond latency at any scale, not for relational reporting with multi-table joins. Option C (Amazon Aurora Serverless) is wrong because, although it is MySQL/PostgreSQL-compatible and ACID-compliant, it is designed for intermittent, unpredictable workloads with auto-scaling, not for a read-heavy workload with occasional batch inserts where provisioned Aurora or RDS would be more cost-effective and performant. Option D (Amazon Timestream) is wrong because it is a time-series database optimized for IoT and operational applications, storing data with timestamps and supporting limited SQL-like queries, but it lacks full ACID compliance and is not designed for complex JOINs on relational transaction records.

Full explanation →

982

Multi-Selecthard

A company is designing a database for a social media analytics platform that requires: 1) storing relationships between users, posts, and interests; 2) running complex graph queries like 'find all friends of friends who like topic X'; 3) high availability with multi-region replication. Which TWO AWS services should they consider? (Choose TWO.)

Select 2 answers

A.Amazon Neptune

B.Amazon Redshift

C.Amazon Aurora Global Database

D.Amazon DynamoDB with Global Tables

E.Amazon ElastiCache for Redis

AnswersA, C

Neptune is purpose-built for graph queries.

Why this answer

Amazon Neptune is a fully managed graph database service optimized for storing and querying highly connected data, such as user, post, and interest relationships. It supports property graph and RDF models, enabling complex graph queries like 'find all friends of friends who like topic X' using Gremlin or SPARQL. This directly satisfies the requirement for graph query capabilities.

Amazon Aurora Global Database provides multi-region replication with low-latency global read operations and automatic failover, meeting the high availability and multi-region replication requirement. Together, Neptune handles the graph query workload, while Aurora Global Database provides the global distribution and disaster recovery needed for the analytics platform.

Exam trap

The trap here is that candidates may confuse DynamoDB Global Tables (which provides multi-region replication) with graph database capabilities, overlooking that DynamoDB lacks native graph query support for multi-hop traversals like 'friends of friends'.

Full explanation →

983

Multi-Selecthard

A company is deploying an Amazon DynamoDB table with server-side encryption using a customer-managed AWS KMS key. The security team requires that the key be automatically rotated every year and that access to the key be logged. Which THREE steps should be taken?

Select 3 answers

A.Enable automatic key rotation for the KMS key.

B.Use AWS CloudHSM to store the key and manage rotation.

C.Enable AWS CloudTrail to log KMS API calls.

D.Create a KMS key policy that allows only specific IAM roles to use the key.

E.Configure the DynamoDB table to use client-side encryption.

AnswersA, C, D

KMS supports automatic rotation yearly.

Why this answer

To meet the requirements: (1) Enable automatic key rotation (Option A) to satisfy the automatic rotation requirement. (2) Enable AWS CloudTrail to log KMS API calls (Option C) to log access to the key. (3) Create a KMS key policy that restricts usage to specific IAM roles (Option D) to control access. Option B is incorrect because AWS CloudHSM is a hardware security module, not used for KMS key rotation or logging. Option E is incorrect because client-side encryption is not relevant; server-side encryption with KMS is already used.

Full explanation →

984

MCQhard

A company is using Amazon DynamoDB with global tables to support a multi-region application. They notice that writes to the table in us-east-1 are not replicating to the table in eu-west-1. The replication lag metric shows increasing values. What is the most likely cause?

A.The table in eu-west-1 has a lower write capacity and is throttling incoming writes.

B.DynamoDB Streams is disabled on the table in us-east-1.

C.The DynamoDB table in us-east-1 is not accessible from eu-west-1 due to VPC restrictions.

D.The table in eu-west-1 has been removed as a replica region from the global table configuration.

AnswerD

If the replica region is removed, replication stops.

Why this answer

If the replica region (eu-west-1) has been removed from the global table configuration, DynamoDB will stop replicating writes to that region, and the replication lag metric will show increasing values. Option A is incorrect: while a lower write capacity in eu-west-1 could cause throttling and replication lag, it would not completely stop replication; the lag metric would still show some activity. Option B is incorrect: DynamoDB Streams must be enabled on the source table for global tables to function, but disabling them would halt replication entirely, not cause increasing lag.

Option C is incorrect: global tables replicate asynchronously over DynamoDB's internal network and do not require VPC connectivity between regions.

Full explanation →

985

Multi-Selecthard

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

Select 3 answers

A.Check the Write Latency metric on the primary instance.

B.Increase the instance size of the Aurora Replicas.

C.Check the Performance Insights for the Aurora Replicas to identify long-running queries.

D.Monitor the Aurora ReplicaLag metric in Amazon CloudWatch.

E.Evaluate the DB Connection Count metric to see if the replicas are overloaded.

AnswersC, D, E

Long-running queries can cause read latency.

Why this answer

Performance Insights provides a detailed view of database load, including wait events, SQL queries, and host-level metrics, which can pinpoint long-running queries causing high read latency on the Aurora Replicas. By analyzing the top SQL statements and their execution plans, the specialist can identify inefficient queries or contention issues that degrade read performance.

Exam trap

The trap here is that candidates may confuse write latency on the primary with read latency on replicas, or assume scaling the replica size is a diagnostic step rather than a remediation, leading them to select Option A or B instead of focusing on monitoring and analysis metrics.

Full explanation →

986

MCQhard

A company uses Amazon DynamoDB with fine-grained access control using IAM policies. A developer reports that an application role can read and write to the 'orders' table but cannot perform a Scan operation on a Global Secondary Index (GSI). The IAM policy attached to the role includes 'dynamodb:Scan' and the resource ARN for the table. What is the likely reason for this failure?

A.The policy has a condition that restricts Scan operations based on the time of day.

B.The role does not have permission to read from the base table.

C.The IAM policy does not include the GSI's ARN in the resource statement.

D.The IAM policy does not allow the 'dynamodb:Scan' action on the table.

AnswerC

This is correct because GSIs have separate ARNs in DynamoDB. The policy must explicitly include the GSI ARN to allow operations like Scan on that index.

Why this answer

DynamoDB Global Secondary Indexes (GSIs) have their own Amazon Resource Names (ARNs) separate from the base table. When using IAM policies with fine-grained access control, the policy must explicitly include the ARN of the GSI in the resource statement to allow operations like Scan on that GSI. Without the GSI ARN, the Scan operation on the GSI is denied by default, even if the policy allows 'dynamodb:Scan' on the base table.

Option A is incorrect because the policy does not mention time-of-day conditions. Option B is incorrect because the role can read from the base table, but the issue is with the GSI. Option D is incorrect because the action 'dynamodb:Scan' is allowed on the table but not on the GSI.

Full explanation →

987

MCQeasy

A database administrator needs to audit all SQL queries executed on an Amazon RDS for PostgreSQL instance, including SELECT statements. Which two most efficient ways to achieve this?

A.Enable AWS CloudTrail for the RDS instance.

B.Install the pgAudit extension and configure it to log all statements.

C.Configure the DB instance to export logs to Amazon CloudWatch Logs.

D.Enable Enhanced Monitoring for the DB instance.

AnswerB, C

Correct. The pgAudit extension is the native way to log all SQL statements, including SELECTs, on PostgreSQL. It provides granular auditing.

Why this answer

The pgAudit extension for PostgreSQL can be installed on RDS and configured to log all SQL statements, including SELECT queries. Option C is also correct because configuring the DB instance to export logs to Amazon CloudWatch Logs, when combined with enabling statement logging via the RDS parameter group (e.g., setting log_statement = 'all'), allows centralized log management and analysis, effectively auditing all queries. Option A is wrong because AWS CloudTrail does not capture SQL query content; it logs API actions on the RDS instance itself.

Option D is wrong because Enhanced Monitoring provides OS-level metrics, not SQL query logs. Therefore, both B and C are correct.

Exam trap

Some candidates may think CloudTrail logs SQL queries, but it only records API actions. Others may overlook that exporting logs to CloudWatch alone does not capture query content unless statement logging is enabled in the parameter group.

Full explanation →

988

MCQhard

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

A.Disable synchronous commit on the primary to reduce log generation

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

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

D.Increase the Provisioned IOPS on both the primary and standby instances to improve log write throughput

AnswerD

Higher IOPS reduces the time to flush transaction logs, reducing replica lag.

Why this answer

Increasing Provisioned IOPS on both the primary and standby instances improves I/O throughput for writing transaction logs, which is the bottleneck causing replica lag. Higher IOPS reduces the time needed to generate logs on the primary and apply them on the standby. Option A is incorrect because disabling synchronous commit would reduce durability and may still not achieve the required lag reduction.

Option B is incorrect since the standby instance size is already equal to the primary; the issue is I/O performance, not compute capacity. Option C is incorrect because reducing batch frequency impacts application responsiveness and does not address the I/O bottleneck.

Full explanation →

989

MCQeasy

A company is migrating its on-premises Oracle database to Amazon RDS for Oracle. The database is 2 TB in size and contains both transactional and analytical workloads. The company uses Oracle Data Pump for migration. During the migration, the database specialist notices that the export job on the source database is taking longer than expected and is generating significant I/O, impacting production performance. The company needs to minimize the impact on the source database while completing the migration within a 48-hour window. The source database is currently running on a dedicated server with sufficient CPU and memory. Which course of action should the database specialist take?

A.Use AWS Database Migration Service (AWS DMS) with full load and change data capture (CDC) to migrate the database.

B.Use Data Pump to export to a networked file system (NFS) mount point to offload storage I/O to the network.

C.Use Oracle Data Pump in network_link mode to directly import the data from the source to the target RDS instance without intermediate files.

D.Increase the parallel workers for the Data Pump export job to 8 to speed up the export and reduce the time window.

AnswerC

Correct: This minimizes source I/O as data is transferred directly.

Why this answer

Oracle Data Pump's network_link mode allows the source database to export data directly to the target RDS for Oracle instance over a database link, bypassing the need to write intermediate dump files to disk. This eliminates the I/O overhead on the source server's storage subsystem, which is the primary cause of the production performance impact, while still completing the migration within the 48-hour window.

Exam trap

The trap here is that candidates often assume increasing parallelism (Option D) is always the best way to speed up a Data Pump job, but they overlook that the primary issue is I/O impact on production, not throughput, and that network_link mode eliminates the I/O bottleneck entirely.

How to eliminate wrong answers

Option A is wrong because AWS DMS with full load and CDC would still require reading the source database, which can generate significant I/O and impact production performance; additionally, DMS may not support all Oracle-specific features or data types that Data Pump handles natively. Option B is wrong because exporting to an NFS mount point still writes dump files to a networked file system, which does not eliminate the I/O on the source server's local storage and can introduce network latency and additional overhead. Option D is wrong because increasing parallel workers for the Data Pump export job would increase I/O and CPU consumption on the source database, exacerbating the production performance impact rather than minimizing it.

Full explanation →

990

MCQhard

A database administrator is troubleshooting an Amazon RDS for PostgreSQL DB instance that is experiencing high CPU utilization. The administrator runs the following query to find the current running queries: SELECT pid, now() - pg_stat_activity.query_start AS duration, query, state FROM pg_stat_activity WHERE state = 'active'; The output shows a high number of queries with a state of 'active' and durations exceeding several minutes. What should the administrator do FIRST to reduce CPU utilization?

A.Modify the max_connections parameter to limit concurrent sessions.

B.Scale up the DB instance to a larger instance class.

C.Use pg_terminate_backend to terminate the long-running queries.

D.Enable pg_stat_statements to collect query performance data.

AnswerC

Terminating long-running queries immediately reduces CPU usage.

Why this answer

The immediate cause of high CPU utilization is the long-running active queries consuming resources. Using pg_terminate_backend to terminate these queries will quickly free up CPU cycles, providing immediate relief. This is the first troubleshooting step before making configuration changes or scaling, as it directly addresses the symptom shown in the pg_stat_activity output.

Exam trap

The trap here is that candidates may choose to scale up or adjust parameters first, overlooking that the immediate problem is the active long-running queries, which can be resolved quickly with pg_terminate_backend without incurring cost or configuration changes.

How to eliminate wrong answers

Option A is wrong because modifying max_connections limits the number of concurrent sessions but does not address the existing long-running queries that are already consuming CPU; it may also cause application errors if connections are rejected. Option B is wrong because scaling up the DB instance to a larger class is a reactive and costly measure that does not resolve the root cause of inefficient queries; it should only be considered after optimizing queries or confirming a sustained workload increase. Option D is wrong because enabling pg_stat_statements collects query performance data for analysis but does not reduce current CPU utilization; it is a diagnostic tool for future optimization, not an immediate fix.

Full explanation →

991

Multi-Selecthard

Which THREE factors should be considered when planning a cross-Region disaster recovery strategy for an Amazon Aurora MySQL DB cluster? (Choose 3.)

Select 3 answers

A.Provisioned IOPS for the primary cluster.

B.Network latency between source and target Regions.

C.Storage costs in the target Region.

D.Failover priority of reader instances.

E.Recovery Point Objective (RPO) requirements.

AnswersB, C, E

Latency affects replication lag and RTO.

Why this answer

Network latency between source and target Regions directly impacts the replication lag for Amazon Aurora Global Database. Aurora uses asynchronous replication across Regions, and higher latency increases the delay between committing a transaction on the primary cluster and it being available on the secondary cluster, which affects the achievable Recovery Point Objective (RPO).

Exam trap

The trap here is that candidates confuse local failover mechanisms (reader instance priority) with cross-Region disaster recovery, or mistakenly think that provisioned IOPS or storage costs in the target Region are primary considerations when RPO and replication lag are the critical factors.

Full explanation →

992

MCQhard

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

A.Enable DynamoDB adaptive capacity to automatically handle uneven access patterns.

B.Deploy DAX to cache frequent writes and reduce write load.

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

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

AnswerA

Adaptive capacity automatically adjusts throughput to handle uneven access patterns, helping with hot partitions and write throttling.

Why this answer

Adaptive capacity automatically adjusts throughput to handle uneven access patterns, helping with hot partitions. Option B is wrong because DAX caches reads, not writes; it does not help with write throttling. Option C is wrong because reducing WCU would worsen throttling, not resolve it.

Option D is wrong because enabling auto scaling adjusts capacity but does not directly address hot partitions; it may help with overall throughput but not uneven distribution.

Full explanation →

993

MCQhard

A company is building a real-time analytics dashboard from IoT sensor data. Data arrives as time-series with millions of writes per second. The dashboard queries the last hour of data with aggregations. Which database design is most cost-effective?

A.Amazon DynamoDB with TTL and DAX

B.Amazon Redshift with streaming ingestion

C.Amazon Timestream

D.Amazon RDS for PostgreSQL with TimescaleDB extension

AnswerC

Optimized for time-series with low cost for high write throughput and efficient recent data queries.

Why this answer

Amazon Timestream is purpose-built for time-series data, offering automatic tiered storage (in-memory for recent data and magnetic for historical) and built-in aggregation functions optimized for time-based queries. This design handles millions of writes per second cost-effectively, as it eliminates the need for manual sharding or TTL management, and its serverless model charges only for data written and queried, making it ideal for real-time analytics on the last hour of IoT sensor data.

Exam trap

The trap here is that candidates often choose DynamoDB with TTL and DAX because they associate it with high write throughput and caching, but they overlook that time-series aggregation queries require native time-based functions and cost-efficient storage tiering, which Timestream uniquely provides.

How to eliminate wrong answers

Option A is wrong because DynamoDB with TTL and DAX is optimized for key-value and low-latency point lookups, not for time-series aggregations over a sliding window; TTL only deletes expired data but does not provide native time-based aggregation functions, and DAX accelerates reads but does not reduce the cost or complexity of scanning and aggregating millions of writes per second. Option B is wrong because Redshift with streaming ingestion is designed for large-scale analytical queries on structured data, but its minimum cluster size and compute costs make it over-provisioned and expensive for real-time dashboards querying only the last hour of data; streaming ingestion adds latency and complexity for sub-second updates. Option D is wrong because RDS for PostgreSQL with TimescaleDB extension requires manual provisioning of compute and storage, incurs costs for idle capacity, and its hypertable partitioning and continuous aggregates still involve overhead for millions of writes per second, making it less cost-effective than a fully managed serverless time-series service like Timestream.

Full explanation →

994

Multi-Selecthard

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

Select 2 answers

A.Increase the maximum write capacity setting for the auto scaling policy.

B.Review the partition key design to ensure even access distribution.

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

D.Enable DynamoDB Streams to offload write traffic.

E.Disable auto scaling and use On-Demand capacity mode.

AnswersA, B

Allows auto scaling to scale higher.

Why this answer

Increases the maximum write capacity setting, allowing the auto scaling policy to scale higher and handle the increased traffic. Option B addresses hot partitions by reviewing the partition key design to ensure even distribution of writes, which reduces throttling even if capacity is sufficient. Option C (DAX) is a caching layer for reads, not writes, so it does not resolve write throttling.

Option D (DynamoDB Streams) captures changes but does not offload write traffic; it's a separate feature for streaming data. Option E (On-Demand mode) would help, but the question asks to resolve throttling with auto scaling enabled; On-Demand is an alternative, not a fix within auto scaling, but it's not the best choice given the context.

Full explanation →

995

Multi-Selectmedium

Which TWO database services are suitable for storing time-series data with high ingestion rates? (Select TWO.)

Select 2 answers

A.Amazon Timestream

B.Amazon RDS for MySQL

C.Amazon ElastiCache for Memcached

D.Amazon DynamoDB with TTL

E.Amazon Redshift

AnswersA, D

Purpose-built for time-series.

Why this answer

Amazon Timestream is a purpose-built time-series database that automatically scales to handle high ingestion rates, with built-in functions for time-series data analysis and storage tiering. Amazon DynamoDB with TTL is suitable because it can ingest high-velocity time-series data at scale, and the TTL feature automatically expires old records, making it a cost-effective choice for time-series workloads that require fast point-in-time lookups.

Exam trap

The trap here is that candidates often choose Amazon RDS or Redshift because they are familiar with SQL, but they overlook that these services are not designed for high-frequency time-series ingestion and lack the automatic data lifecycle management that Timestream and DynamoDB with TTL provide.

Full explanation →

996

MCQmedium

A company is designing a database for a social media application that needs to handle friend-of-friend queries and recommendation engine traversals. Which AWS database is best suited?

A.Amazon DynamoDB

B.Amazon DocumentDB (with MongoDB compatibility)

C.Amazon ElastiCache for Redis

D.Amazon Neptune

AnswerD

Graph database optimized for traversals and relationships.

Why this answer

Amazon Neptune is a fully managed graph database service optimized for storing and querying highly connected data. It supports both property graph (Apache TinkerPop/Gremlin) and RDF (SPARQL) models, making it ideal for friend-of-friend queries and recommendation engine traversals that require navigating complex relationships with low latency.

Exam trap

The trap here is that candidates often choose DynamoDB or ElastiCache because they associate social media with key-value or in-memory caching, overlooking that graph databases are specifically designed for relationship-heavy traversals like friend-of-friend queries.

How to eliminate wrong answers

Option A is wrong because Amazon DynamoDB is a key-value and document database that lacks native graph traversal capabilities; performing friend-of-friend queries would require multiple expensive queries and client-side joins, leading to poor performance at scale. Option B is wrong because Amazon DocumentDB (MongoDB-compatible) is a document database that does not support graph-specific query patterns like Gremlin or SPARQL, and its aggregation pipeline is inefficient for multi-hop relationship traversals. Option C is wrong because Amazon ElastiCache for Redis is an in-memory data store that can model graphs using data structures like sets or sorted sets, but it lacks a native graph query language and optimized traversal engine, making it unsuitable for complex multi-level recommendation traversals at scale.

Full explanation →

997

MCQhard

A company is migrating a 1 TB SQL Server database to Amazon RDS for SQL Server. They use AWS DMS with full load and ongoing replication. After the full load completes, they observe that the ongoing replication is falling behind and the target database is not consistent. The source database has high transaction volume. What is the most effective way to improve replication performance?

A.Disable CloudWatch logging for the DMS replication instance.

B.Configure multiple parallel load tasks for ongoing replication.

C.Enable batch apply mode in the DMS task.

D.Increase the size of the DMS replication instance (e.g., from dms.c5.xlarge to dms.c5.2xlarge).

AnswerD

A larger instance provides more resources to process transactions faster.

Why this answer

Increasing the DMS replication instance size (Option D) provides more CPU and memory resources, which directly addresses the bottleneck caused by high transaction volume on the source. A larger instance can process and apply changes faster, reducing the replication lag and improving consistency. This is the most effective single action because it scales the entire replication pipeline without altering the task configuration.

Exam trap

The trap here is that candidates often confuse full-load parallelism (multiple tasks) with CDC performance, or assume that disabling logging or toggling batch apply will fix lag, when the root cause is insufficient instance compute capacity for high-volume transactional workloads.

How to eliminate wrong answers

Option A is wrong because disabling CloudWatch logging does not free up significant resources; DMS logging overhead is minimal and not a primary cause of replication lag. Option B is wrong because multiple parallel load tasks are used for the full load phase, not for ongoing replication (CDC), which uses a single continuous change capture task. Option C is wrong because batch apply mode is already enabled by default in DMS for ongoing replication; toggling it off would worsen performance, not improve it.

Full explanation →

998

MCQeasy

A retail company runs its inventory management system on Amazon RDS for PostgreSQL. The application performs frequent updates to inventory counts. The operations team notices that write latency increases significantly during peak sales hours. The database is a single db.r5.large instance with General Purpose SSD (gp2) storage. The CPU utilization is around 40% during peaks, but the write latency spikes. The team suspects a storage bottleneck. Which change would most effectively reduce write latency?

A.Enable Multi-AZ deployment.

B.Change storage type to Provisioned IOPS (io2) with sufficient IOPS.

C.Add a read replica to offload read traffic.

D.Scale up to a db.r5.xlarge instance.

AnswerB

Provisioned IOPS provides consistent low-latency write performance.

Why this answer

Provisioned IOPS (io2) storage provides consistent low-latency I/O performance compared to gp2, which can have variable performance due to burst credits. Option A (Multi-AZ) improves availability, not latency. Option C (read replica) offloads reads, not writes.

Option D (scale up instance) increases compute capacity but may not address the I/O bottleneck if the issue is storage performance.

Full explanation →

999

MCQmedium

A company uses Amazon RDS for MySQL with encryption at rest enabled. The security team mandates that all access to the database must be authenticated using IAM database authentication. The database administrator has created a user 'app_user' and granted appropriate privileges. However, when the application tries to connect using an IAM authentication token, it receives an 'Access denied' error. Which action should be taken to resolve this issue?

A.Create a password for 'app_user' and use that password in the connection string.

B.Modify the security group to allow inbound traffic on port 3306 from the application's IP address.

C.Ensure the IAM role used by the application has a name that matches the database user 'app_user' and has the necessary policy attached.

D.Download the latest Amazon RDS CA certificate and add it to the application trust store.

AnswerC

IAM database authentication requires the database user name to match the IAM user or role name (or a mapped DB user) that has been granted the rds_iam role.

Why this answer

IAM database authentication for RDS MySQL requires that the database user name matches the IAM role or user name used to generate the authentication token. The IAM role must have a policy that allows the `rds-db:connect` action with a resource ARN specifying the database user. Without this exact match, the authentication token is rejected, resulting in an 'Access denied' error.

Exam trap

The trap here is that candidates often confuse IAM database authentication with network-level security (security groups) or TLS certificate management, when the core requirement is the exact name match between the database user and the IAM principal (role or user) used to generate the token.

How to eliminate wrong answers

Option A is wrong because IAM database authentication does not use a static password; it relies on a temporary authentication token generated via the AWS CLI or SDK, and creating a password would bypass the mandated IAM authentication. Option B is wrong because the 'Access denied' error is an authentication failure, not a network connectivity issue; security group rules control network access, not IAM token validation. Option D is wrong because the CA certificate is used for TLS/SSL encryption of the connection, not for IAM authentication; the error is unrelated to certificate trust.

Full explanation →

1000

MCQmedium

A company is designing a database for a ride-sharing application that requires real-time location updates and driver-passenger matching. The database must support geospatial queries to find nearby drivers within a radius. The expected throughput is 10,000 writes per second and 5,000 reads per second. The company wants a fully managed solution with low latency. The application team has experience with PostgreSQL. Which database design should they choose?

A.Use Amazon Aurora PostgreSQL with the PostGIS extension and use read replicas for scaling reads.

B.Use Amazon DynamoDB with a geospatial library to encode locations into a partition key.

C.Use Amazon DynamoDB with a global secondary index on a geohash attribute for proximity queries.

D.Use Amazon RDS for MySQL with spatial indexes and Multi-AZ deployment.

AnswerA

Aurora PostgreSQL with PostGIS provides geospatial support, scalability, and managed service.

Why this answer

Amazon Aurora PostgreSQL supports PostGIS for geospatial queries, can scale to handle the throughput with write replicas and auto-scaling, and is fully managed. Option B (DynamoDB) is not ideal for geospatial queries; it requires complex partitioning. Option C (DynamoDB with GSI) still not good for radius queries.

Option D (RDS MySQL) has geospatial support but may not scale as well as Aurora.

Full explanation →

1001

Multi-Selectmedium

A company is building a real-time leaderboard for a mobile game using Amazon DynamoDB. The leaderboard displays the top 100 players by score. The table has 'game_id' as partition key and 'player_id' as sort key. The score is updated frequently. Which THREE design patterns should the company implement to ensure low-latency reads for the leaderboard? (Choose three.)

Select 3 answers

A.Use 'ScanIndexForward: false' on the base table to get top players.

B.Enable DynamoDB Accelerator (DAX) to cache the leaderboard query.

C.Use 'begins_with' operator on the sort key to filter by score range.

D.Create a global secondary index (GSI) with 'game_id' as partition key and 'score' as sort key.

E.Use DynamoDB Streams and AWS Lambda to maintain a separate leaderboard table.

AnswersB, D, E

Caches the query results for low-latency reads.

Why this answer

DynamoDB Accelerator (DAX) provides an in-memory cache that can serve repeated leaderboard queries with microsecond latency, reducing the read load on the base table and avoiding throttling. For a real-time leaderboard that is read frequently, DAX ensures low-latency responses without needing to query DynamoDB directly each time.

Exam trap

The trap here is that candidates may think using ScanIndexForward on the base table (Option A) is sufficient, but they overlook that the base table's sort key is player_id, not score, so it cannot sort by score without a GSI or separate table.

Full explanation →

1002

MCQeasy

A company needs to ensure that all changes to an Amazon RDS DB instance's security group are logged for auditing purposes. Which AWS service should be enabled?

A.Amazon RDS Event Subscriptions

B.AWS CloudTrail

C.AWS Config

D.VPC Flow Logs

AnswerB

CloudTrail logs all API actions, including security group modifications.

Why this answer

AWS CloudTrail is the correct service because it records API calls made to the AWS environment, including actions like AuthorizeSecurityGroupIngress and RevokeSecurityGroupEgress that modify security group rules. When enabled, CloudTrail logs all changes to Amazon RDS security groups as management events, providing an immutable audit trail that can be stored in Amazon S3 for compliance and auditing purposes.

Exam trap

The trap here is that candidates confuse AWS Config's configuration tracking with CloudTrail's API logging, not realizing that Config evaluates resource compliance but does not capture the who, what, and when of API calls that change security groups.

How to eliminate wrong answers

Option A is wrong because Amazon RDS Event Subscriptions notify you about database instance events (e.g., failover, backup completion) but do not log or record API-level changes to security group configurations. Option C is wrong because AWS Config evaluates resource configurations against desired policies and tracks configuration changes, but it does not provide the detailed API call logs required for auditing who made the change and when; it relies on CloudTrail for that data. Option D is wrong because VPC Flow Logs capture IP traffic metadata (source/destination IP, ports, protocol) at the network interface level, not management-plane API calls that modify security group rules.

Full explanation →

1003

MCQeasy

A company wants to audit all SQL statements executed on their Amazon Aurora MySQL database for compliance. Which AWS feature should be enabled?

A.Database Activity Streams

B.VPC Flow Logs

C.Amazon CloudWatch Logs

D.AWS CloudTrail

AnswerA

Database Activity Streams provide a stream of database activities including SQL queries.

Why this answer

Database Activity Streams provide a stream of database activities including SQL queries. Option B is wrong because VPC Flow Logs capture network traffic, not SQL statements. Option C is wrong because CloudWatch Logs can store logs but the source should be Database Activity Streams; CloudWatch Logs alone does not audit SQL statements.

Option D is wrong because CloudTrail logs API calls, not SQL statements.

Full explanation →

1004

Matchingmedium

Match each AWS database feature to its description.

Drag a concept onto its matching description — or click a concept then click the description.

Concepts

Matches

Synchronous standby replica for high availability

Asynchronous read-only copy for scaling reads

Automatic adjustment of Aurora replica count

In-memory cache for DynamoDB with microsecond latency

Connection pooling for RDS to handle Lambda bursts

Why these pairings

Multi-AZ is for high availability and failover; Read Replicas offload read traffic; Auto Scaling adjusts capacity; Point-in-Time Recovery enables granular restores. Common confusions involve swapping Multi-AZ and Read Replicas definitions.

Full explanation →

1005

MCQmedium

A company is running an Amazon Aurora MySQL-Compatible DB cluster. The application is experiencing increased latency during peak hours. The DB cluster's CPU utilization is at 40%, but the ReadReplicaLag metric is high. Which of the following is the most likely cause?

A.The writer instance is experiencing I/O bottlenecks, causing slow replication

B.The read replica is receiving too many write requests

C.The DB cluster parameter group is misconfigured

D.The binary log retention period is too long

AnswerA

I/O bottlenecks on the writer can delay writing to the binary log, increasing replica lag.

Why this answer

High replica lag indicates the replica is falling behind, often due to insufficient I/O capacity on the writer or replica. Option B is wrong because read replicas handle SELECT queries. Option C is wrong because the DB cluster parameter group is shared and misconfiguration is not the most likely cause for high replica lag.

Option D is wrong because binary log retention period does not affect replica lag directly.

Full explanation →

1006

MCQeasy

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

A.Check the target utilization percentage in the auto scaling policy

B.Verify that the auto scaling role has the necessary IAM permissions

C.Check whether the table class is DynamoDB Standard-IA

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

AnswerA

If target utilization is high (e.g., 90%), scaling may not trigger until sustained high usage.

Why this answer

The target utilization percentage in the auto scaling policy determines when scaling triggers. If set too high (e.g., 90%), the table may not scale until near full consumption, causing throttling during spikes. Option B is incorrect: IAM permissions for the auto scaling role are typically preconfigured and less likely to cause scaling failures—permission issues would appear in CloudTrail, not as a gradual throttling problem.

Option C is incorrect: table class (Standard vs. Standard-IA) affects storage costs, not write capacity scaling behavior. Option D is incorrect: while a GSI can throttle if its write capacity is insufficient, the primary table's auto scaling is independent; the question states the table itself does not scale, so the GSI is not the first check.

Full explanation →

1007

MCQhard

A company is migrating an on-premises Oracle database to Amazon RDS for Oracle. The security team requires that all network traffic between the application servers and the database be encrypted using TLS. The application servers are in a different VPC connected via VPC Peering. What is the simplest way to enforce encryption in transit?

A.Modify the security group for the RDS instance to only allow traffic on port 443.

B.Enable the rds.force_ssl parameter in the DB parameter group and configure the application to use SSL connections.

C.Use a default DB parameter group, as it already enforces SSL.

D.Set up a VPN connection between the two VPCs and route all traffic through it.

AnswerB

This enforces SSL at the database level, and the client can connect using SSL certificates.

Why this answer

Enabling the rds.force_ssl parameter in the DB parameter group forces all connections to use SSL, satisfying the encryption-in-transit requirement. The application must be configured to connect using SSL with the appropriate certificate. Option A is wrong because changing the security group to port 443 does not enforce encryption; port 443 is for HTTPS, not for Oracle database traffic, and SSL is configured at the database level.

Option C is wrong because the default parameter group does not enforce SSL; the rds.force_ssl parameter must be explicitly set. Option D is wrong because a VPN adds unnecessary complexity and does not directly enforce TLS encryption for database connections.

Full explanation →

1008

MCQmedium

A company is using Amazon ElastiCache for Redis as a caching layer. The application is experiencing higher latency than expected. The team suspects that cache evictions are occurring due to memory pressure. Which ElastiCache metric should be monitored to confirm this?

A.CurrConnections

B.Evictions

C.ReplicationLag

D.CacheHits

AnswerB

Evictions metric directly counts the number of keys evicted due to memory pressure.

Why this answer

The `Evictions` metric in ElastiCache for Redis tracks the number of keys evicted due to memory pressure, which directly indicates cache evictions. Option A is incorrect because `CurrConnections` shows the number of current client connections, not evictions. Option C is incorrect because `ReplicationLag` measures the delay between the primary and replica nodes, not evictions.

Option D is incorrect because `CacheHits` shows the number of successful key lookups, not evictions.

Full explanation →

1009

MCQeasy

A database administrator needs to monitor the number of connections to an Amazon RDS for PostgreSQL DB instance. Which Amazon CloudWatch metric should be used?

A.DatabaseConnections

B.CPUUtilization

C.WriteLatency

D.FreeableMemory

AnswerA

This metric tracks the number of connections to the DB instance.

Why this answer

The DatabaseConnections metric in Amazon CloudWatch directly reports the number of client connections to the RDS for PostgreSQL DB instance. This metric is published by the RDS service and reflects the count of active connections as seen by the database engine, making it the correct choice for monitoring connection counts.

Exam trap

The trap here is that candidates might confuse CPUUtilization or memory metrics with connection monitoring, but AWS explicitly provides the DatabaseConnections metric for this purpose, and the exam tests your knowledge of which specific CloudWatch metric maps to which operational need.

How to eliminate wrong answers

Option B (CPUUtilization) is wrong because it measures the percentage of CPU used by the instance, not the number of connections. Option C (WriteLatency) is wrong because it tracks the average time for write operations to complete, which is a performance metric unrelated to connection count. Option D (FreeableMemory) is wrong because it indicates the amount of available RAM on the instance, not the number of active connections.

Full explanation →

1010

Multi-Selectmedium

A database specialist is monitoring an Amazon RDS for MySQL instance and notices that the 'CPUUtilization' metric is consistently above 90%. Which TWO actions should the specialist take to investigate the issue? (Select TWO.)

Select 2 answers

A.Disable the MySQL query cache to free up CPU.

B.Use Performance Insights to identify the queries that are consuming the most CPU.

C.Increase the instance class to provide more CPU resources.

D.Enable Multi-AZ to distribute the load to a standby instance.

E.Enable Enhanced Monitoring to get OS-level metrics for the RDS instance.

AnswersB, E

Performance Insights shows top queries by load.

Why this answer

Performance Insights identifies the queries that are consuming the most CPU, helping to locate the source of high CPU utilization. Option E is correct because Enhanced Monitoring provides OS-level metrics (e.g., CPU, memory, disk I/O) that help investigate the issue. Option A is incorrect because disabling the query cache does not help investigate CPU usage; it may even increase CPU load temporarily.

Option C is incorrect because increasing the instance class is a remediation step, not an investigation action. Option D is incorrect because Multi-AZ does not distribute read load to a standby instance; the standby is only for failover, not for load distribution.

Full explanation →

1011

MCQeasy

A company wants to migrate an on-premises MySQL database to Amazon RDS for MySQL with minimal downtime. They plan to use AWS DMS. Which source database configuration is required to enable ongoing replication (CDC)?

A.Enable binary logging (binlog) on the source database

B.Set auto_increment_increment to 1

C.Enable the slow query log

D.Enable the query cache

AnswerA

Binlog captures changes for CDC.

Why this answer

AWS DMS requires binary logging (binlog) enabled on the source MySQL database to capture ongoing changes for continuous replication (CDC). The binlog records all data modifications (INSERT, UPDATE, DELETE) in a sequential log, which DMS reads to apply those changes to the target RDS instance with minimal downtime. Without binlog enabled, DMS can only perform a full load migration and cannot support ongoing replication.

Exam trap

The trap here is that candidates may confuse MySQL's binary log with other logs (slow query log, query cache) or assume that auto-increment settings are required for replication, when in fact only binlog enables the change stream DMS needs for CDC.

How to eliminate wrong answers

Option B is wrong because setting auto_increment_increment to 1 is the default behavior and does not enable CDC; it controls the step size for auto-increment columns, not replication logging. Option C is wrong because the slow query log captures queries that exceed a defined execution time threshold, which is used for performance tuning and has no role in CDC replication. Option D is wrong because the query cache is a deprecated MySQL feature that caches SELECT results for performance; it does not provide the change data capture stream that DMS requires.

Full explanation →

1012

Multi-Selecthard

A company is migrating a 5 TB Oracle database to Amazon RDS for Oracle. The migration must have minimal downtime and support a cutover window of less than 30 minutes. The company is using AWS DMS with ongoing CDC. Which THREE steps should be taken to minimize cutover time? (Choose three.)

Select 3 answers

A.Configure AWS DMS to use ongoing replication and set the target to apply changes until the source is caught up.

B.Pause application writes to the source database and allow CDC to catch up.

C.Validate that the target database is consistent with the source using AWS DMS data validation.

D.Take a full backup of the source database and restore it to the target before cutover.

E.Use AWS Schema Conversion Tool (SCT) to convert remaining schema objects before cutover.

AnswersA, B, C

Ongoing replication with CDC catches up the target.

Why this answer

AWS DMS ongoing replication (CDC) continuously captures and applies changes from the source Oracle database to the target RDS for Oracle instance. By configuring the target to apply changes until the source is fully caught up, you ensure that at cutover the target is nearly synchronized, minimizing the final downtime to just the time needed to apply the last few transactions.

Exam trap

The trap here is that candidates might think a full backup/restore (Option D) is necessary for migration, but for minimal downtime with CDC, the initial full load is done via DMS itself, not a separate backup, and SCT (Option E) is a pre-migration tool, not a cutover step.

Full explanation →

1013

MCQhard

A company runs a production Amazon RDS for MySQL Multi-AZ DB instance with 10 TB of storage. The application team notices that database write latency has increased from 5 ms to 80 ms over the past week. The DB instance is a db.r5.8xlarge with 500 GB of gp2 storage. CloudWatch metrics show WriteIOPS averaging 10,000, WriteThroughput at 50 MB/s, and BurstBalance decreasing from 100% to 20% over the same period. The DB instance has no reserved IOPS. The database workload is write-heavy with frequent small transactions. The company needs to resolve the high write latency while minimizing costs and downtime. Which solution should the database specialist recommend?

A.Create a read replica to offload write traffic to the replica and use it for failover.

B.Upgrade the DB instance to a db.r5.16xlarge with more vCPUs and memory to handle higher write throughput.

C.Enable Performance Insights to identify slow queries and add database-level caching to reduce write load.

D.Modify the DB instance to increase the allocated storage to 1 TB using gp2, allowing the volume to operate with higher baseline IOPS and burst credits.

AnswerD

Increasing volume size boosts baseline IOPS and burst balance, resolving the bottleneck without changing instance type.

Why this answer

Increasing gp2 storage from 500 GB to 1 TB raises the baseline IOPS from 1,500 (3 IOPS/GB) to 3,000, and also increases the burst credit pool size. With WriteIOPS averaging 10,000, the volume is depleting burst credits faster than they can be replenished, causing BurstBalance to drop to 20% and latency to spike. Doubling the storage provides a higher baseline and more credits, reducing reliance on burst and resolving the latency issue without changing instance class or adding cost for reserved IOPS.

Exam trap

AWS often tests the misconception that increasing instance size (vCPUs/memory) resolves storage I/O bottlenecks, when in fact gp2 burst credit exhaustion is a storage-level issue that requires increasing volume size or switching to provisioned IOPS.

How to eliminate wrong answers

Option A is wrong because a read replica cannot offload write traffic; it only serves read queries and does not reduce write latency on the primary. Option B is wrong because upgrading to a db.r5.16xlarge increases CPU and memory but does not change the gp2 volume's IOPS baseline or burst credit behavior, so write latency caused by storage exhaustion would persist. Option C is wrong because Performance Insights and caching address query performance and read load, not the underlying gp2 burst credit depletion causing high write latency; the workload is write-heavy with frequent small transactions, not slow queries.

Full explanation →

1014

MCQmedium

A company is deploying a new web application that uses Amazon RDS for MySQL. To improve read performance, they add a read replica. However, the application reports stale data. What is the most likely cause?

A.The application is using a connection pool that caches connections to the primary.

B.The read replica is in a different AWS Region.

C.The replication is asynchronous and there is replication lag.

D.The application is reading from the replica before the write is committed on the primary.

AnswerC

Asynchronous replication causes eventual consistency.

Why this answer

Amazon RDS for MySQL uses asynchronous replication for read replicas. The primary instance commits writes and then sends the binary log (binlog) events to the replica, which applies them. This asynchronous nature introduces replication lag, meaning the replica may not have the most recent writes.

If the application reads from the replica before the lag is resolved, it will see stale (older) data.

Exam trap

The trap here is that candidates often confuse asynchronous replication with synchronous replication, assuming the replica always has the latest data, or they incorrectly attribute the issue to geographic distance or connection pooling rather than the fundamental replication lag inherent in MySQL's async model.

How to eliminate wrong answers

Option A is wrong because a connection pool that caches connections to the primary would not cause stale data on a read replica; it would simply direct reads to the primary, which always has the latest data. Option B is wrong because a read replica in a different AWS Region can introduce additional latency but does not inherently cause stale data; replication lag is the primary cause, and cross-region replicas still replicate asynchronously. Option D is wrong because the application cannot read from the replica before the write is committed on the primary; the write must be committed on the primary before it is even sent to the replica, so the issue is the time it takes for the replica to apply the committed change, not the commit order.

Full explanation →

1015

MCQmedium

A company uses Amazon Aurora MySQL-Compatible Edition and needs to audit all SQL statements executed by database users. Which feature should be enabled?

A.Enable the database engine audit logs (advanced auditing).

B.Enable AWS CloudTrail data events for the DB instance.

C.Enable database activity streams.

D.Enable RDS Enhanced Monitoring.

AnswerA

Aurora MySQL can log SQL statements using the audit plugin.

Why this answer

Aurora MySQL supports advanced auditing through database engine audit logs. Option B is wrong because CloudTrail logs API calls, not SQL queries. Option C is wrong because RDS Enhanced Monitoring provides OS metrics, not SQL audit.

Option D is wrong because database activity streams capture activity in near real-time but are designed for monitoring and not primarily for SQL audit logging; however, they can be used, but the most direct answer is advanced auditing.

Full explanation →

1016

MCQmedium

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. The DBA has enabled 'require_secure_transport' parameter in the DB parameter group. However, after applying the change and rebooting, some applications that were previously connecting successfully are now failing. What is the most likely cause?

A.The security group inbound rule for the database port is missing.

B.The option group does not have the SSL option enabled.

C.The application is not configured to connect using SSL/TLS.

D.The DB subnet group does not include the application's subnet.

AnswerC

Enabling require_secure_transport forces the server to accept only SSL connections. If the application does not use SSL, the connection will be rejected.

Why this answer

Enabling 'require_secure_transport' in the MySQL parameter group forces the server to accept only SSL/TLS connections. Applications that are not configured with SSL will be rejected, causing them to fail after this change. Option A is incorrect because the security group inbound rule missing would have prevented connections entirely, not just after this change.

Option B is incorrect because the SSL option in the option group is separate from the 'require_secure_transport' parameter; the parameter group controls this setting. Option D is incorrect because the DB subnet group determines network placement, not SSL enforcement.

Full explanation →

1017

MCQhard

A database administrator has the IAM policy shown above attached to their user. When they try to run the AWS CLI command `aws rds describe-db-instances --region us-west-2`, they receive an access denied error. Why does this fail?

A.The user must use multi-factor authentication (MFA) to perform the describe action.

B.The user does not have permission to describe DB instances in any region.

C.The policy includes a condition key that denies access when the region is not us-east-1.

D.The policy explicitly restricts the resource to a specific DB instance ARN in us-east-1, and the command specifies a different region.

AnswerD

The resource ARN includes region us-east-1, so it does not apply to us-west-2.

Why this answer

The IAM policy includes two Allow statements. The first statement grants rds:DescribeDBInstances only on a specific DB instance ARN in us-east-1. The second statement grants the same action on any resource (*).

Because IAM evaluates the effective permissions as the union of all statements, the user is only allowed to describe that specific instance in us-east-1; describing instances in any other region (such as us-west-2) is not explicitly allowed for that resource, so the request is denied. Option D is correct because it acknowledges that the resource ARN restriction from the first statement limits the allowed region. Option A is incorrect because there is no MFA condition in the policy.

Option B is incorrect because the user can describe the specific instance in us-east-1. Option C is incorrect because the policy restricts access via a resource ARN, not a condition key.

Full explanation →

1018

Multi-Selectmedium

A company is running a critical application on Amazon DynamoDB. The table has a partition key of 'user_id' and a sort key of 'timestamp'. The application frequently queries for all items for a given user within a date range. The read capacity is often throttled during peak hours. Which THREE steps should the database specialist take to resolve the throttling?

Select 3 answers

A.Enable DynamoDB adaptive capacity to automatically adjust throughput

B.Change the partition key to 'timestamp' to improve read distribution

C.Decrease the provisioned write capacity units (WCU) to free up resources for reads

D.Increase the provisioned read capacity units (RCU) for the table

E.Implement DynamoDB Accelerator (DAX) to cache frequent reads

AnswersA, D, E

Adaptive capacity helps manage uneven access patterns and reduces throttling.

Why this answer

DynamoDB adaptive capacity automatically manages throughput to accommodate uneven access patterns, such as when a single 'user_id' partition receives more reads than provisioned. It allows the table to absorb throttling by redistributing unused capacity from other partitions, which directly addresses the peak-hour throttling without manual intervention.

Exam trap

The trap here is that candidates may think decreasing WCU can reallocate resources to reads, but DynamoDB's read and write capacity are independent, so reducing one does not benefit the other.

Full explanation →

1019

MCQmedium

A company is deploying a multi-AZ Aurora MySQL database. They need to ensure that failover happens automatically with minimal data loss. Which configuration should be used?

A.Deploy an Aurora Global Database with a primary in one region and a secondary in another region.

B.Use a Multi-AZ RDS for MySQL deployment.

C.Create an Aurora cluster with one primary and two readers in different Availability Zones.

D.Configure a single-AZ Aurora instance and enable cross-Region replication.

AnswerC

Aurora automatically fails over to a reader with minimal data loss.

Why this answer

An Aurora cluster with one primary and two readers in different Availability Zones provides automatic failover with minimal data loss. Aurora uses a shared cluster volume that is replicated six ways across three AZs, ensuring that during a failover, the promoted reader has access to all committed transactions with no data loss. This configuration meets the requirement for multi-AZ high availability and automatic failover within a single region.

Exam trap

The trap here is that candidates often confuse Multi-AZ RDS for MySQL with Aurora's multi-AZ architecture, not realizing that Aurora's distributed storage and reader promotion provide superior automatic failover with minimal data loss compared to traditional RDS Multi-AZ.

How to eliminate wrong answers

Option A is wrong because an Aurora Global Database is designed for cross-region disaster recovery, not for automatic failover within a single region; it introduces replication lag of up to 1 second, which can result in data loss during a failover. Option B is wrong because Multi-AZ RDS for MySQL uses synchronous replication to a standby instance, but it is not Aurora and does not leverage Aurora's distributed storage engine, which provides faster failover and better durability. Option D is wrong because a single-AZ Aurora instance with cross-Region replication does not provide automatic failover within the same region; it only replicates asynchronously to another region, and a failure in the primary AZ would cause downtime until manual intervention.

Full explanation →

1020

MCQeasy

A company has an Amazon DynamoDB table with auto scaling enabled. During a traffic spike, the application experiences high write latencies. Which action should the company take to troubleshoot the latency issue?

A.Monitor the ThrottledWriteEvents metric in CloudWatch.

B.Switch the table to on-demand capacity mode.

C.Disable auto scaling and manually increase write capacity.

D.Increase the read capacity of the table.

AnswerA

Correct. Monitoring ThrottledWriteEvents in CloudWatch helps determine if auto scaling is keeping up with write demand. Elevated throttling indicates that provisioned capacity is insufficient, leading to high latencies.

Why this answer

ThrottledWriteEvents metric indicates write requests that were throttled due to exceeding provisioned capacity. If this metric spikes during traffic spikes, it suggests that auto scaling is not scaling fast enough, leading to high write latencies. Option B is incorrect because switching to on-demand is a remediation, not a troubleshooting step; it does not help identify the root cause.

Option C is incorrect because disabling auto scaling and manually increasing capacity might solve the issue temporarily but is not a troubleshooting approach; it bypasses auto scaling logic. Option D is incorrect because read capacity does not affect write latencies.

Full explanation →

1021

MCQhard

A company is running a MongoDB-compatible Amazon DocumentDB cluster. The application is experiencing high write latency during peak hours. The cluster has one writer and two readers. The DBA suspects that the issue is due to a large number of indexes on the collection. What should the DBA do to verify this hypothesis without affecting production?

A.Take a snapshot of the cluster and restore it to a new cluster to test index changes.

B.Use AWS DMS to replicate data to a test cluster and perform index testing there.

C.Enable the profiler on the production cluster to capture slow queries and analyze the index usage.

D.Create a clone of the cluster using the 'Clone Cluster' feature and test index changes on the clone.

AnswerD

Cloning creates a copy of the cluster quickly without impacting the source.

Why this answer

Cloning the cluster using the 'Clone Cluster' feature creates a separate, isolated environment where index changes can be tested without affecting production. Option A is incorrect because taking a snapshot and restoring to a new cluster is time-consuming and does not provide a real-time copy of the current state for testing. Option B is incorrect because AWS DMS is designed for database migration, not for testing index performance.

Option C is incorrect because enabling the profiler captures slow queries but does not allow testing of index changes; it only helps identify existing issues.

Full explanation →

1022

MCQhard

A company is using an Amazon RDS for SQL Server database with Multi-AZ deployment. They need to migrate to Amazon RDS for SQL Server in a different AWS region. The database is 1 TB and cannot tolerate more than 15 minutes of downtime. Which strategy minimizes downtime?

A.Create a cross-region read replica in the target region and promote it to a standalone instance.

B.Take a snapshot of the source RDS instance and restore it in the target region. Then update the application connection string.

C.Set up cross-region replication using native SQL Server log shipping or DMS with ongoing replication, then perform a DNS cutover to the target instance.

D.Use AWS DMS to perform a full load to a new RDS instance in the target region. During the final sync, stop the source and resume from the target.

AnswerC

This approach keeps the target synchronized and allows a quick cutover with minimal downtime.

Why this answer

It combines native SQL Server log shipping or AWS DMS with ongoing replication to keep the target region instance nearly synchronized with the source, enabling a DNS cutover with minimal downtime. This approach meets the 15-minute downtime requirement by allowing a controlled final sync and cutover, unlike snapshot restore or full-load-only methods that require longer outages. Multi-AZ deployment does not prevent cross-region replication, and log shipping or DMS can handle the 1 TB database within the downtime window if properly configured.

Exam trap

The trap here is that candidates often assume cross-region read replicas are available for all RDS engines, but SQL Server does not support them, leading them to choose Option A without verifying engine-specific limitations.

How to eliminate wrong answers

Option A is wrong because Amazon RDS for SQL Server does not support cross-region read replicas; read replicas are only available within the same region for SQL Server, and promoting a replica that cannot be created across regions is not feasible. Option B is wrong because taking a snapshot and restoring it in the target region requires the source to be stopped or at least incur significant downtime during the snapshot creation and restore process, which for a 1 TB database would exceed 15 minutes. Option D is wrong because using AWS DMS for a full load only, without ongoing replication, means the target database will be out of sync with the source during the final sync, requiring a longer outage to capture and apply changes, which cannot guarantee the 15-minute downtime limit.

Full explanation →

1023

MCQmedium

A company is deploying a MySQL RDS instance using this CloudFormation template. After deployment, they notice that the database is not automatically backed up at the scheduled time. What is the most likely cause?

A.The storage type gp2 does not support automated backups.

B.The PreferredBackupWindow is not defined, so backups are disabled.

C.MultiAZ is enabled, which disables automated backups.

D.The BackupRetentionPeriod must be greater than 0 to enable automated backups; however, it is set to 7, so this is not the issue. The actual cause might be that the DB instance is using a storage engine not supported for backups (e.g., MyISAM).

AnswerD

RDS automated backups require InnoDB; MyISAM tables cause backup failure.

Why this answer

The BackupRetentionPeriod is set to 7, which is greater than 0, so automated backups are enabled. However, the most likely cause of the backup failure is that the DB instance is using a storage engine like MyISAM, which does not support point-in-time recovery or consistent automated backups in Amazon RDS. RDS automated backups rely on the InnoDB storage engine for crash recovery and transaction consistency; MyISAM tables can cause backup failures or incomplete backups.

Exam trap

The trap here is that candidates focus on the BackupRetentionPeriod being set to 7 (which is correct) and overlook the storage engine limitation, assuming all MySQL storage engines are equally supported for automated backups in RDS.

How to eliminate wrong answers

Option A is wrong because gp2 storage type fully supports automated backups; automated backups are supported on all storage types (gp2, gp3, io1, io2, magnetic) as long as the BackupRetentionPeriod is greater than 0. Option B is wrong because the PreferredBackupWindow is optional; if not defined, AWS automatically assigns a random backup window, it does not disable backups. Option C is wrong because MultiAZ does not disable automated backups; in fact, MultiAZ deployments still support automated backups and the backup window is used to take backups from the standby instance to reduce I/O impact.

Full explanation →

1024

MCQhard

A company has an Amazon Aurora MySQL DB cluster with one writer and two readers. The application is reporting increased read latency. The DB cluster is using Aurora Auto Scaling for readers. Which metric should be used to trigger scaling out readers?

A.ReadLatency

B.ReaderNodeCPU

C.CPUUtilization

D.AverageActiveConnections

AnswerD

Aurora Auto Scaling uses the number of active connections to the reader to add or remove readers.

Why this answer

Aurora Auto Scaling for readers uses the 'AverageActiveConnections' metric to determine when to add readers. Option D is correct. Option A is incorrect because average active connections is the trigger, not read latency.

Option B is incorrect because ReaderNodeCPU is not a predefined metric for auto scaling. Option C is incorrect because CPUUtilization is not the metric used for reader scaling.

Full explanation →

1025

MCQmedium

A database administrator is troubleshooting why Amazon RDS Enhanced Monitoring is not displaying metrics for a DB instance. The IAM role attached to the instance has the policy shown in the exhibit. What is the likely cause?

A.The policy denies the 'rds:DescribeDBInstances' action.

B.The policy is missing the 'rds:CreateDBInstance' action.

C.The policy does not include 'monitoring:ListMetrics'.

D.The resource is set to '*' instead of the specific DB instance ARN.

AnswerA

Correct. Denying 'rds:DescribeDBInstances' would prevent Enhanced Monitoring from retrieving instance details necessary to publish metrics.

Why this answer

Amazon RDS Enhanced Monitoring requires the IAM role to have permission to describe the DB instance using 'rds:DescribeDBInstances'. This action is necessary for the monitoring service to retrieve information about the instance so it can collect and publish OS metrics. If the policy explicitly denies this action, Enhanced Monitoring will fail to display metrics.

Option C is incorrect because 'monitoring:ListMetrics' is not required; Enhanced Monitoring publishes to CloudWatch Logs, not CloudWatch Metrics, and the IAM role needs logs permissions (e.g., 'logs:PutLogEvents') rather than monitoring actions. Options B and D are also incorrect: 'rds:CreateDBInstance' is unrelated to monitoring, and using a resource of '*' is acceptable as long as the trust policy allows RDS to assume the role.

Full explanation →

1026

MCQmedium

A company is deploying an RDS MySQL database using the above CloudFormation template. After deployment, the database automatically reboots during the maintenance window. The company wants to reduce the impact of maintenance events. Which parameter change would minimize unavailability?

A.Set AutoMinorVersionUpgrade to true in the template.

B.Increase BackupRetentionPeriod to 30 days to have more recovery points.

C.Increase AllocatedStorage to 500 GB to improve performance during maintenance.

D.Change PreferredMaintenanceWindow to a less busy time.

AnswerA

Automatic minor version upgrades ensure the database is updated during maintenance windows, reducing the need for manual updates that could cause longer downtime.

Why this answer

Setting AutoMinorVersionUpgrade to true ensures that minor version upgrades are applied automatically during the maintenance window. This prevents the need for a separate, manually scheduled maintenance event to apply these upgrades, thereby reducing the overall number of maintenance-related reboots and minimizing unavailability compared to the other options. Option B increases recovery points but does not reduce downtime; Option C improves performance but not maintenance impact; Option D shifts the downtime but does not reduce its length.

Exam trap

The trap here is that candidates often assume changing the maintenance window to a less busy time (Option D) reduces impact, but it only shifts the downtime without reducing its length. The correct answer (AutoMinorVersionUpgrade to true) ensures minor version upgrades are applied during the maintenance window, avoiding additional downtime from manual upgrades. However, note that AutoMinorVersionUpgrade does not enable Multi-AZ failover; that is a separate feature.

How to eliminate wrong answers

Option B is wrong because increasing BackupRetentionPeriod to 30 days provides more recovery points for point-in-time restore, but it does not reduce the impact of maintenance events; backups are taken asynchronously and do not affect availability during a reboot. Option C is wrong because increasing AllocatedStorage to 500 GB improves I/O performance and throughput, but it does not prevent or shorten the downtime caused by a maintenance reboot; storage size is unrelated to the failover or reboot process. Option D is wrong because changing PreferredMaintenanceWindow to a less busy time only shifts when the reboot occurs, but it does not reduce the duration or impact of the unavailability; the database still reboots and becomes unavailable during that window.

Full explanation →

1027

Multi-Selectmedium

A security engineer is designing access controls for an Amazon DynamoDB table containing customer data. Which TWO actions enforce least privilege access?

Select 2 answers

A.Use IAM conditions to restrict access to specific attributes.

B.Grant dynamodb:* action to all users.

C.Implement fine-grained access control using IAM policy conditions.

D.Use a resource-based policy on the DynamoDB table.

E.Attach a VPC endpoint policy that allows all actions.

AnswersA, C

Correct: IAM conditions can restrict access to specific DynamoDB attributes, enforcing least privilege.

Why this answer

Options A and C are correct. Using IAM conditions to restrict access to specific attributes (A) and implementing fine-grained access control using IAM policy conditions (C) both enforce least privilege by limiting the data and actions a user can access. Option B violates least privilege by granting all actions to all users.

Option D is incorrect because Amazon DynamoDB does not support resource-based policies; access is controlled via identity-based IAM policies. Option E is incorrect because a VPC endpoint policy allowing all actions does not restrict access.

Full explanation →

1028

MCQmedium

A company is migrating an on-premises MySQL database to Amazon Aurora MySQL. The database is 2 TB and the migration must have minimal downtime. The network bandwidth between the on-premises data center and AWS is 1 Gbps. Which migration approach is most appropriate?

A.Use mysqldump to export the data, upload to Amazon S3, and import into Aurora

B.Take a snapshot of the on-premises database and restore it to Aurora

C.Use AWS Snowball Edge to transfer the data physically to AWS

D.Use AWS Database Migration Service (DMS) with ongoing replication to keep the target in sync

AnswerD

DMS supports continuous replication, allowing minimal downtime migration.

Why this answer

AWS DMS with ongoing replication is the most appropriate approach because it allows a full load of the 2 TB database followed by continuous change data capture (CDC) to keep the target Aurora MySQL cluster in sync with minimal downtime. At 1 Gbps, the initial load would take approximately 5–6 hours, but the CDC phase reduces the final cutover downtime to seconds or minutes, meeting the minimal downtime requirement.

Exam trap

The trap here is that candidates often choose Snowball Edge for large datasets without considering that the question explicitly requires minimal downtime, and Snowball's physical shipping time (days) cannot meet that requirement, whereas DMS with CDC provides near-zero downtime.

How to eliminate wrong answers

Option A is wrong because mysqldump is a logical export that is slow and single-threaded by default, and for a 2 TB database it would take many hours or days to export, upload to S3, and import, causing unacceptable downtime. Option B is wrong because you cannot take a snapshot of an on-premises MySQL database and restore it directly to Aurora; snapshots are a native AWS feature for RDS/Aurora instances, not for on-premises databases. Option C is wrong because AWS Snowball Edge, while suitable for large data transfers over slow networks, introduces significant latency for physical shipping (typically days) and does not support ongoing replication, so it cannot achieve minimal downtime for a live migration.

Full explanation →

1029

Multi-Selectmedium

Which TWO metrics should be monitored to detect an Amazon RDS for MySQL instance that is experiencing memory pressure? (Choose 2.)

Select 2 answers

A.SwapUsage

B.DatabaseConnections

C.ReadIOPS

D.FreeableMemory

E.CPUUtilization

AnswersA, D

High swap usage indicates memory pressure.

Why this answer

A and D are correct. SwapUsage directly indicates memory pressure when it exceeds 0, as the operating system swaps memory to disk. FreeableMemory shows the amount of available memory, so a low value suggests memory pressure.

B (DatabaseConnections) is incorrect because it is an indirect indicator that can affect memory but is not a direct memory metric. C (ReadIOPS) is disk I/O, not memory. E (CPUUtilization) is CPU usage, not memory.

Full explanation →

1030

Multi-Selecthard

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

Select 3 answers

A.Review network latency between the writer and replica

B.Increase the instance size of the replica

C.Check the replica's CPU and memory utilization

D.Disable binary logging on the writer

E.Examine the replication thread status using SHOW SLAVE STATUS

AnswersA, C, E

Network latency can increase replication lag.

Why this answer

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

Option D (disabling binary logging) would break replication entirely.

Full explanation →

1031

Multi-Selectmedium

A company is running a PostgreSQL database on Amazon RDS. They need to improve read performance for a reporting application that runs complex queries. The reporting application can tolerate slightly stale data. Which THREE actions should they take? (Choose three.)

Select 3 answers

A.Modify the DB parameter group to optimize settings for reporting workloads, such as increasing shared_buffers and work_mem.

B.Upgrade the RDS instance to a larger instance class with more vCPUs and memory.

C.Create one or more read replicas of the RDS instance and direct reporting queries to them.

D.Enable Multi-AZ on the RDS instance.

E.Implement an Amazon ElastiCache cluster to cache query results.

AnswersA, B, C

Tuning PostgreSQL parameters can improve performance for complex queries.

Why this answer

Modifying the DB parameter group to increase shared_buffers and work_mem can significantly improve the performance of complex reporting queries by allowing more data to be cached in memory and larger sort operations to be performed in memory rather than on disk. Option B is correct because upgrading to a larger instance class with more vCPUs and memory directly addresses the compute and memory demands of complex queries, reducing query execution time. Option C is correct because creating read replicas offloads reporting traffic from the primary database, and since the reporting application can tolerate slightly stale data, the asynchronous replication lag is acceptable.

Exam trap

The trap here is that candidates often confuse Multi-AZ with read replicas, assuming that the standby instance in a Multi-AZ deployment can serve read traffic, but in RDS for PostgreSQL, the standby is not accessible for reads until a failover occurs.

Full explanation →

1032

MCQhard

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

A.Change the storage type to Provisioned IOPS (io1)

B.Enable RDS Proxy to pool database connections

C.Increase the instance size to a larger DB instance class

D.Add a Multi-AZ standby to offload writes

AnswerC

Vertical scaling can improve write throughput.

Why this answer

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

Exam trap

AWS often tests the misconception that Multi-AZ standby can offload writes, but in reality, the standby is a synchronous replica that does not accept write traffic and only provides failover redundancy.

How to eliminate wrong answers

Option A is wrong because changing to Provisioned IOPS (io1) improves I/O performance but does not address the underlying compute or memory bottleneck causing high write latency; it also incurs additional cost without guaranteeing latency reduction if the instance is already undersized. Option B is wrong because RDS Proxy pools database connections to reduce connection overhead and improve scalability, but it does not reduce write latency on the database itself; it is designed for connection management, not write performance. Option D is wrong because adding a Multi-AZ standby provides high availability and failover support, but it does not offload writes; the standby is a read-only replica that cannot handle write traffic, so it does not reduce write latency on the primary instance.

Full explanation →

1033

MCQhard

A company is designing a multi-tenant SaaS application on Amazon RDS for PostgreSQL. Tenants have vastly different data sizes and access patterns. The current design uses a separate schema per tenant, but some tenants experience slow queries while others are fine. Which approach would best isolate tenant performance and simplify management?

A.Use row-level security (RLS) policies within a single schema to restrict tenant data access.

B.Keep the current schema design and add a tenant_id index to all tables.

C.Use a separate database per tenant.

D.Use Amazon RDS Proxy to manage connections and improve performance.

AnswerC

Separate databases provide strong performance isolation and allow per-tenant resource allocation and backup.

Why this answer

Using a separate database per tenant provides the strongest resource isolation at the database instance level. This design prevents noisy neighbors—tenants with large data volumes or heavy access patterns from degrading the performance of other tenants—and simplifies management tasks such as backup, restore, and point-in-time recovery on a per-tenant basis. Amazon RDS for PostgreSQL supports multiple databases within a single DB instance, and each database operates with its own catalog, tables, and connection pool, ensuring that query execution and memory allocation are not shared across tenants.

Exam trap

The trap here is that candidates often confuse logical data isolation (RLS or schema-per-tenant) with performance isolation, assuming that indexing or connection pooling can solve resource contention, when in fact only physical separation (separate databases) guarantees that one tenant's workload does not impact another's performance.

How to eliminate wrong answers

Option A is wrong because row-level security (RLS) policies operate within a single schema and do not isolate performance; all queries still compete for the same shared buffer pool, CPU, and I/O resources, so a heavy tenant can still cause slowdowns for others. Option B is wrong because adding a tenant_id index to all tables only improves query performance for individual queries but does nothing to prevent resource contention between tenants; the underlying shared infrastructure remains a bottleneck. Option D is wrong because Amazon RDS Proxy manages connection pooling and reduces connection overhead, but it does not isolate tenant workloads or prevent resource contention at the database engine level; it is a connection management layer, not a performance isolation mechanism.

Full explanation →

1034

Multi-Selecthard

A company is designing a data lake on Amazon S3 with Amazon Redshift Spectrum for analytics. The data includes JSON logs from web servers. Which THREE design practices should the company follow to optimize query performance and cost?

Select 3 answers

A.Compress files using gzip or snappy.

B.Use many small files to maximize parallelism.

C.Partition the data by date (e.g., year/month/day) in S3.

D.Convert JSON files to Apache Parquet format.

E.Create indexes on the S3 data using AWS Glue.

AnswersA, C, D

Compression reduces storage and I/O.

Why this answer

Compressing JSON files with gzip or Snappy reduces the data size stored in S3, lowering storage costs and minimizing the amount of data that Redshift Spectrum must scan over the network. Redshift Spectrum can read compressed files directly, and compression often improves query performance by reducing I/O, even though it adds a small CPU overhead for decompression.

Exam trap

The DBS-C01 exam often tests the misconception that more files equals more parallelism, but in Redshift Spectrum, excessive small files increase overhead and reduce performance, while the correct approach is to use fewer, larger files in a columnar format with partitioning.

Full explanation →

1035

MCQeasy

A DBA sees the above error log entries for an Amazon RDS for PostgreSQL DB instance. What is the most likely cause?

A.The user 'myuser@mycompany.com' is using an incorrect password.

B.There is a network connectivity issue between the client and the database.

C.The database has reached its maximum number of connections.

D.The user 'myuser@mycompany.com' does not exist in the database.

AnswerA

Authentication failure typically indicates wrong password.

Why this answer

The error log entry shows authentication failure for user 'myuser@mycompany.com' with message 'password authentication failed for user'. In PostgreSQL, this specific error indicates the client provided an incorrect password for an existing user. The log does not show any connection limit exceeded or network timeout messages, and the user is referenced by name, confirming the user exists.

Exam trap

The trap here is that candidates confuse 'authentication failed' with 'user does not exist', but PostgreSQL deliberately returns different error messages for nonexistent roles versus incorrect passwords to avoid information leakage.

How to eliminate wrong answers

Option B is wrong because network connectivity issues would produce timeout or 'no route to host' errors, not password authentication failures. Option C is wrong because reaching maximum connections generates 'FATAL: sorry, too many clients already' errors, not password authentication failures. Option D is wrong because if the user did not exist, the error would be 'FATAL: role "myuser@mycompany.com" does not exist', not a password authentication failure.

Full explanation →

1036

Multi-Selecteasy

An Amazon RDS for MySQL instance is running out of storage. Which TWO actions can be taken to resolve this issue without downtime?

Select 2 answers

A.Enable storage auto scaling to automatically add storage.

B.Delete unnecessary data from the instance.

C.Modify the DB instance to increase allocated storage.

D.Take a snapshot and restore to a larger instance.

E.Convert the instance to Amazon Aurora.

AnswersA, C

Auto scaling adds storage when needed without manual intervention.

Why this answer

Amazon RDS for MySQL supports modifying storage to increase allocated storage without downtime (option C). You can also enable storage auto scaling to automatically increase storage when thresholds are met (option A). Deleting unnecessary data (option B) might free space but is not a reliable proactive measure and could involve manual effort.

Taking a snapshot and restoring to a larger instance (option D) requires downtime. Converting to Amazon Aurora (option E) is a migration process, not a direct fix for running out of storage.

Full explanation →

1037

MCQhard

A company is migrating a 5 TB MySQL database to Amazon Aurora MySQL. The migration must have zero downtime and the source database is continuously written to. The team plans to use AWS DMS with ongoing replication. However, they notice that the target Aurora instance is not receiving all changes from the source. Which configuration change is most likely required to resolve this?

A.Create an Amazon RDS read replica of the source database and use it as the source endpoint.

B.Set the target table preparation mode to 'Do nothing' in the DMS task.

C.Enable binary logging (binlog) on the source MySQL database with row-based logging.

D.Configure a VPC endpoint for the DMS replication instance.

AnswerC

DMS needs binlogs for ongoing replication; if not enabled, it cannot capture changes.

Why this answer

AWS DMS requires binary logging (binlog) with row-based logging on the source MySQL database to capture ongoing changes for continuous replication. Without binlog enabled, DMS cannot read the change data stream needed to replicate transactions to the target Aurora instance, causing missed changes. This is a mandatory prerequisite for any MySQL-to-Aurora migration using DMS with ongoing replication.

Exam trap

The trap here is that candidates may overlook the fundamental prerequisite of binary logging for DMS ongoing replication and instead focus on network connectivity (VPC endpoints) or task configuration settings that do not address the root cause of missing change capture.

How to eliminate wrong answers

Option A is wrong because creating an RDS read replica does not enable the binary logging needed for DMS to capture ongoing changes; the read replica itself would still lack binlog unless explicitly enabled, and it adds unnecessary complexity. Option B is wrong because setting the target table preparation mode to 'Do nothing' only affects how DMS handles existing tables on the target during full load, not the capture of ongoing changes from the source. Option D is wrong because a VPC endpoint is used for private connectivity between services within a VPC, but it does not enable or affect the binary logging configuration required for change data capture.

Full explanation →

1038

Multi-Selecteasy

A database administrator needs to monitor the free storage space on an Amazon RDS for SQL Server instance. Which TWO CloudWatch metrics should be used? (Choose two.)

Select 2 answers

A.DatabaseConnections

B.ReadLatency

C.BinLogDiskUsage

D.FreeStorageSpaceInPercent

E.FreeStorageSpace

AnswersD, E

Percentage of free storage space.

Why this answer

(FreeStorageSpaceInPercent) and Option E (FreeStorageSpace) are correct. Option A is wrong because DatabaseConnections measures the number of connections, not storage. Option B is wrong because ReadLatency measures read latency, not storage.

Option C is wrong because BinLogDiskUsage is a metric for MySQL/RDS for MySQL, not for SQL Server.

Full explanation →

1039

MCQhard

Refer to the exhibit. A company's production RDS MySQL instance 'mydb' is configured as shown. The application experiences write latency spikes during peak hours. Which action would most effectively reduce write latency?

A.Change the storage type to Provisioned IOPS (io1) and allocate sufficient IOPS.

B.Create a Read Replica in a different region.

C.Increase the allocated storage to 1 TB to improve baseline IOPS.

D.Enable Multi-AZ deployment to provide a standby instance.

AnswerA

Provisioned IOPS provides consistent, low-latency write performance.

Why this answer

The RDS MySQL instance uses gp2 storage with 500 GB, providing a baseline of 1500 IOPS (3 IOPS per GB). Write latency spikes during peak hours indicate that the workload is exceeding these IOPS, causing throttling. Changing to Provisioned IOPS (io1) allows allocating a higher, consistent IOPS level that matches the write workload, directly reducing write latency.

Option D (Multi-AZ) adds synchronous replication overhead, increasing write latency. Option B (Read Replica) offloads reads, not writes. Option C (increasing storage) raises baseline IOPS but is less efficient and more costly than provisioning exact IOPS with io1.

Exam trap

Multi-AZ improves availability but adds synchronous replication to the standby, which increases write latency. Candidates may incorrectly think Multi-AZ reduces write latency.

Full explanation →

1040

MCQhard

A company is using Amazon RDS for PostgreSQL with automated backups. The security team requires that backups be encrypted at rest. The DB instance is currently unencrypted. What is the most efficient way to enable encryption for future backups?

A.Enable encryption by setting the 'backup_encryption' parameter in the DB parameter group.

B.Modify the DB instance to enable encryption.

C.Enable encryption on the S3 bucket where backups are stored.

D.Create a snapshot of the DB instance, copy the snapshot with encryption enabled, and restore the encrypted snapshot to a new DB instance.

AnswerD

This is the standard method to migrate an unencrypted RDS instance to an encrypted one.

Why this answer

The correct method to enable encryption for future backups on an existing unencrypted Amazon RDS for PostgreSQL instance is to create a snapshot of the DB instance, copy the snapshot with encryption enabled, and restore the encrypted snapshot to a new DB instance. Option A is incorrect because there is no 'backup_encryption' parameter in the DB parameter group. Option B is incorrect because modifying the DB instance does not allow enabling encryption on an unencrypted instance; encryption can only be enabled at creation time.

Option C is incorrect because enabling encryption on the S3 bucket does not encrypt the RDS backups themselves; the backups must be encrypted at the source.

Full explanation →

1041

MCQeasy

A company wants to encrypt data at rest for an existing Amazon RDS for Oracle DB instance. The database is currently unencrypted. What is the simplest way to enable encryption with minimal downtime?

A.Create an encrypted read replica and promote it to a standalone instance.

B.Modify the DB instance and enable encryption using the RDS console.

C.Create a snapshot of the DB instance, copy the snapshot with encryption enabled, and restore the encrypted snapshot to a new DB instance.

D.Enable encryption directly on the existing DB instance by modifying the DB instance settings.

AnswerC

Creating an unencrypted snapshot, copying it with encryption enabled, and restoring to a new DB instance satisfies the requirement for minimal downtime because the original RDS for Oracle instance remains fully available during the snapshot and copy operations; only a brief DNS cutover occurs when the application is redirected to the new encrypted instance. This avoids the constraint that RDS does not support enabling encryption in-place on an existing unencrypted DB instance.

Why this answer

You cannot directly enable encryption on an existing unencrypted Amazon RDS instance. The correct process is to create a snapshot of the DB instance, copy the snapshot with encryption enabled, and then restore the encrypted snapshot to a new DB instance. You then redirect traffic to the new instance.

Option A is incorrect because creating a read replica does not encrypt the master instance, and the replica can only be encrypted if the master is already encrypted. Option B is incorrect because you cannot modify an existing DB instance to enable encryption through the RDS console. Option D is incorrect because enabling encryption directly on the existing DB instance is not supported.

Full explanation →

1042

MCQeasy

A company wants to restrict access to an Amazon DynamoDB table so that only specific IAM users can read and write data. What is the BEST way to achieve this?

A.Use a resource-based policy on the DynamoDB table.

B.Create an IAM policy that grants access to the DynamoDB table and attach it to the specific IAM users.

C.Use DynamoDB Accelerator (DAX) with IAM authentication.

D.Create a VPC endpoint for DynamoDB and allow only traffic from that VPC.

AnswerB

IAM policies are the standard way to control access to DynamoDB.

Why this answer

Using an IAM policy attached to specific IAM users is the most precise method to restrict access to a DynamoDB table. Resource-based policies are not supported for DynamoDB (Option A is wrong). VPC endpoints control network traffic but do not restrict which users can access the table (Option D is wrong).

DynamoDB Accelerator (DAX) with IAM authentication handles caching and authentication, not table-level access control (Option C is wrong).

Full explanation →

1043

Matchingmedium

Match each AWS service to its primary purpose.

Drag a concept onto its matching description — or click a concept then click the description.

Concepts

Matches

Relational database service with managed instances

NoSQL key-value and document database

In-memory caching service supporting Redis and Memcached

Petabyte-scale data warehouse

MongoDB-compatible document database

Why these pairings

These are core AWS database services with distinct use cases. Common confusions include mixing up Redshift (data warehouse) with ElastiCache (in-memory cache) and Neptune (graph) with DocumentDB (document).

Full explanation →

1044

MCQhard

A company is running an Amazon DynamoDB table with on-demand capacity. The application is experiencing high latency for GetItem operations. CloudWatch shows that 'ThrottledRequests' is zero, but 'ConsumedReadCapacityUnits' is consistently below the provisioned limit. The table has a global secondary index (GSI) with a different partition key. What is the most likely cause of the high latency?

A.High write activity on the base table causing read latency

B.Insufficient provisioned read capacity on the base table

C.Throttling on the global secondary index due to uneven access patterns

D.Throttling on the base table due to hot partitions

AnswerD

Hot partitions on the base table can lead to increased latency even if ThrottledRequests is zero, as requests may queue internally before throttling occurs.

Why this answer

While throttling on a Global Secondary Index (GSI) can cause backpressure and affect writes to the base table, it does not directly impact read operations like GetItem. In this scenario, with zero ThrottledRequests, throttling is not occurring on the base table or GSI. The high latency is more likely caused by a hot partition on the base table, which can still result in increased response times even before throttling metrics are triggered.

Option C is therefore incorrect; the correct answer is D.

Full explanation →

1045

MCQmedium

A database engineer is troubleshooting an Amazon Aurora MySQL DB cluster that is experiencing frequent failovers. The failovers are occurring during periods of low write activity. The engineer notices that the primary instance's writer endpoint is being used for read traffic from a legacy application. What is the most likely cause of the failovers?

A.A high replication lag between the primary and replica instances

B.A long-running write transaction is blocking the failover

C.Insufficient storage for the Aurora cluster volume

D.The primary instance is overloaded due to read traffic from the writer endpoint

AnswerD

Directing read traffic to the writer endpoint increases load, leading to failovers.

Why this answer

Using the writer endpoint for read traffic can overload the primary instance, leading to resource contention and failovers even during low write activity. Option A is incorrect because replication lag is not a direct cause of failovers; failovers are typically due to primary instance issues. Option B is incorrect because a long-running write transaction would not cause failovers during low write activity; it might cause replication lag but not failover.

Option C is incorrect because insufficient storage for the Aurora cluster volume would cause storage-related errors, not failovers; Aurora automatically scales storage.

Full explanation →

1046

MCQhard

A company runs an e-commerce platform on AWS. The application uses an Amazon RDS for MySQL Multi-AZ database instance. The security team recently conducted an audit and found that the database is accessible from the internet because the public accessibility setting is enabled. The security team has disabled public accessibility and updated the security group to allow inbound traffic only from the application's security group. However, after these changes, the application can no longer connect to the database. The application is running on EC2 instances in the same VPC. The RDS instance is also in the same VPC. The security group for the RDS instance allows inbound MySQL traffic (port 3306) from the application's security group. The application's security group allows all outbound traffic. The application's EC2 instances have a public IP address and are in a public subnet. The RDS instance is in a private subnet. The VPC has an internet gateway attached. What is the most likely cause of the connection failure?

A.The RDS instance is in a private subnet and cannot receive inbound traffic from the public subnet EC2 instances without a VPC peering or VPN.

B.The security group for the RDS instance still allows traffic from the public IP addresses of the EC2 instances, but the EC2 instances now have different public IPs.

C.The application is using the RDS public DNS name, which no longer resolves after disabling public accessibility.

D.The EC2 instances need a NAT gateway to communicate with the RDS instance in the private subnet.

AnswerB

Correct. The security group still allows inbound from the public IPs of the EC2 instances, but those IPs may have changed (e.g., after an instance restart). The connection fails because the old public IPs are no longer assigned to the EC2 instances.

Why this answer

The most likely cause is that the security group for the RDS instance still contains a rule allowing inbound traffic from the public IP addresses of the EC2 instances. After disabling public accessibility, the EC2 instances in the public subnet may receive new public IPs if they are stopped and started, or the original rule may have referenced specific public IPs that are no longer valid. The application's connection attempts using the old public IPs fail.

Although the security group was updated to allow traffic from the application's security group, the old rule might not have been removed, or the application was not using the security group reference. Since the EC2 instances are in the same VPC, the private IP communication should work with the security group reference, but if the old rule is still present and the public IPs changed, the connection might fail. Options A and D are incorrect because same-VPC communication does not require VPC peering or a NAT gateway.

Option C is incorrect because the public DNS name resolves to the private IP within the VPC even after public accessibility is disabled, so it should work.

Full explanation →

1047

Multi-Selectmedium

A company is migrating an on-premises PostgreSQL database to Amazon RDS for PostgreSQL. Which TWO AWS services can be used to assess the source database for compatibility and migration readiness?

Select 2 answers

A.Amazon Kinesis

B.AWS Schema Conversion Tool (SCT)

C.AWS Database Migration Service (DMS)

D.AWS Glue

E.Amazon CloudWatch

AnswersB, C

SCT assesses and converts schema.

Why this answer

AWS Schema Conversion Tool (SCT) is designed to assess source databases for compatibility by analyzing the schema, code objects, and dependencies, then generating a detailed assessment report that identifies conversion complexity and migration readiness. For PostgreSQL to Amazon RDS for PostgreSQL migrations, SCT validates that the source schema is compatible with the target RDS engine version and highlights any deprecated features or required modifications.

Exam trap

The trap here is that candidates confuse AWS DMS (which handles data migration and can perform some schema conversion via its built-in transformation rules) with the assessment and schema conversion capabilities of SCT, but DMS alone does not provide a detailed compatibility assessment report or code-level conversion analysis.

Full explanation →

1048

MCQhard

A company is running Amazon Redshift and notices that queries are slow. The administrator runs the STL_ALERT_EVENT_LOG and sees many 'Nested Loop Join' alerts. What is the MOST likely cause?

A.Insufficient concurrency scaling.

B.Incorrect sort keys on the tables.

C.Insufficient compression on the tables.

D.Incorrect distribution keys on the tables.

AnswerD

Missing dist keys cause large data movement.

Why this answer

Nested loop joins in Amazon Redshift occur when the query optimizer cannot use a hash join or merge join, often because the data required for the join is not co-located on the same compute nodes. Incorrect distribution keys cause data to be distributed unevenly or in a way that forces large amounts of data to be redistributed across the network, leading to nested loop joins as a fallback. This is the most likely cause because the STL_ALERT_EVENT_LOG specifically flags nested loop joins as a performance issue related to data distribution.

Exam trap

The trap here is that candidates often confuse distribution keys with sort keys or compression, assuming that poor data organization or storage efficiency directly causes join performance issues, when in fact the root cause is data co-location and the join algorithm selected by the optimizer.

How to eliminate wrong answers

Option A is wrong because insufficient concurrency scaling affects query throughput under high concurrency, not the join method chosen by the optimizer; nested loop joins are a query plan issue, not a resource contention issue. Option B is wrong because incorrect sort keys primarily affect range-restricted scans and min/max pruning, not the join strategy; sort keys do not directly cause nested loop joins. Option C is wrong because insufficient compression reduces I/O efficiency and storage footprint but does not change the join algorithm; the optimizer still chooses join methods based on data distribution and statistics, not compression.

Full explanation →

1049

MCQmedium

A company is using Amazon Redshift for data warehousing. The security team requires that all data is encrypted at rest using a hardware security module (HSM). Additionally, the team wants to ensure that only specific IAM roles can access the database. Which configuration will meet these requirements?

A.Create the Redshift cluster with encryption enabled using a KMS key, and configure the cluster to use an HSM for key storage.

B.Create the Redshift cluster with encryption enabled using an HSM, and attach an IAM role to the cluster to control access.

C.Create the Redshift cluster without encryption, then use the AWS CLI to enable encryption using a KMS key.

D.Create the Redshift cluster with encryption using a KMS key, and then import the HSM certificate into the cluster.

AnswerB

HSM provides hardware-based encryption and IAM roles can be used for access control.

Why this answer

Redshift supports HSM for encryption at rest, and IAM roles can be attached to the cluster for access control. Option A is wrong because HSM and KMS are mutually exclusive. Option C is wrong because KMS does not meet the HSM requirement.

Option D is wrong because encryption cannot be enabled after cluster creation.

Full explanation →

1050

MCQmedium

A gaming company uses Amazon DynamoDB for player profiles. The access pattern is to retrieve a player's profile by 'player_id'. Each profile includes a list of 'achievements' that can grow up to 400 KB. Recently, the application has been encountering 'ProvisionedThroughputExceededException' errors. The table has 1000 read capacity units (RCU) and 500 write capacity units (WCU). The average item size is 200 KB. What is the MOST likely cause of the throttling?

A.The item size exceeds the DynamoDB item size limit of 400 KB.

B.The read capacity units are set too low for the number of partitions.

C.The partition key 'player_id' is causing hot partitions.

D.The application is using strongly consistent reads, which consume double the read capacity.

AnswerD

Strongly consistent reads consume twice as many RCUs as eventually consistent reads.

Why this answer

Strongly consistent reads in DynamoDB consume twice the read capacity units (RCUs) as eventually consistent reads. With an average item size of 200 KB, each strongly consistent read consumes 200 KB / 4 KB = 50 RCUs (rounded up). If the application is using strongly consistent reads, a single read of a 200 KB item uses 50 RCUs, which can quickly exhaust the 1000 RCU table capacity, especially under concurrent access, leading to ProvisionedThroughputExceededException.

Exam trap

The trap here is that candidates may overlook the RCU consumption difference between strongly consistent and eventually consistent reads, assuming all reads consume the same capacity, and instead blame hot partitions or item size limits.

How to eliminate wrong answers

Option A is wrong because the item size limit for DynamoDB is 400 KB, and the profile includes a list of achievements that can grow up to 400 KB, so it does not exceed the limit. Option B is wrong because read capacity units are provisioned per table, not per partition; DynamoDB distributes RCUs across partitions automatically, and the total RCU of 1000 is sufficient for the described access pattern if reads are eventually consistent. Option C is wrong because while hot partitions can cause throttling, the access pattern is to retrieve by 'player_id', which is the partition key, and there is no indication of uneven access distribution; the primary issue is the high RCU consumption per read due to strongly consistent reads.

Full explanation →

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