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AWS Certified Database Specialty DBS-C01 (DBS-C01) — Questions 901–975

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

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901
Multi-Selecthard

A database administrator is responsible for managing an Amazon RDS for Oracle DB instance. The DBA needs to ensure that all changes to the DB instance's security group rules are logged for audit purposes. Which TWO services can be used together to achieve this? (Select TWO.)

Select 2 answers
A.Amazon Inspector
B.AWS Config
C.AWS CloudTrail
D.Amazon CloudWatch Logs
E.Amazon VPC Flow Logs
AnswersC, D

CloudTrail records API calls that modify security group rules.

Why this answer

AWS CloudTrail logs API calls made to the AWS account, including changes to security group rules (e.g., AuthorizeSecurityGroupIngress). By sending CloudTrail logs to Amazon CloudWatch Logs, you can create metrics filters and alarms to monitor and alert on specific API calls, enabling audit logging of security group rule changes. AWS Config tracks configuration changes but does not provide real-time log streaming to CloudWatch Logs for API calls.

VPC Flow Logs capture network traffic, not API calls. Amazon Inspector is a vulnerability assessment service, not for logging API calls.

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902
MCQmedium

A social media application stores user posts in a DynamoDB table with a partition key of user_id and a sort key of timestamp. The most frequent query is to retrieve the 10 most recent posts for a given user. Which secondary index design would optimize this query?

A.Create a GSI with user_id as the partition key and timestamp as the sort key.
B.Use DynamoDB Streams to populate an Amazon Elasticsearch cluster for search queries.
C.Create an LSI with user_id as the partition key and timestamp as the sort key.
D.Query the base table using the sort key timestamp with a limit of 10.
AnswerC

An LSI uses the same partition key and a different sort key, enabling efficient range queries.

Why this answer

A Local Secondary Index (LSI) on the base table with the same partition key (user_id) and a sort key of timestamp allows efficient retrieval of the 10 most recent posts for a given user. An LSI shares the same partition key as the base table, so querying by user_id with ScanIndexForward=false and Limit=10 returns the most recent items without needing to replicate data or incur additional write costs.

Exam trap

The trap here is that candidates may choose Option A (GSI) because they think a GSI is always the answer for query optimization, but they overlook that an LSI is more appropriate when the partition key is the same as the base table and the query pattern is a simple range query on an existing attribute, avoiding unnecessary cost and complexity.

How to eliminate wrong answers

Option A is wrong because a Global Secondary Index (GSI) with user_id as partition key and timestamp as sort key would work but is unnecessary and incurs additional storage and write costs, whereas an LSI is more cost-effective since it shares the base table's partition key and doesn't require separate throughput provisioning. Option B is wrong because DynamoDB Streams feeding an Amazon Elasticsearch cluster is over-engineered for a simple query that can be handled directly by DynamoDB, and it introduces latency, complexity, and operational overhead without performance benefit for this specific access pattern. Option D is wrong because the base table's sort key is timestamp, but querying it directly with a limit of 10 would require specifying a user_id in the KeyConditionExpression, which is already the partition key; however, the base table's sort key is timestamp, so a Query with user_id and a limit of 10 on the base table would work, but the question asks for a secondary index design to optimize the query, and the base table itself is not a secondary index.

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903
MCQhard

A company is migrating an on-premises Oracle database to Amazon RDS for Oracle using AWS DMS. The migration fails with an error indicating that DMS cannot capture changes from the source because the archive log mode is not enabled. What should be done?

A.Disable ongoing replication and perform a full load only.
B.Set up an Oracle Data Guard standby and use it as the source.
C.Use AWS Schema Conversion Tool (SCT) to convert the schema.
D.Enable archive log mode on the source Oracle database.
AnswerD

DMS requires archive logging to capture changes for ongoing replication.

Why this answer

D is correct because AWS DMS requires archive log mode to be enabled on the source Oracle database for ongoing replication (CDC). Without archive logs, DMS cannot capture the redo data needed to apply changes to the target. Enabling archive log mode ensures the redo logs are preserved and accessible for change data capture.

Exam trap

The trap here is that candidates may think disabling CDC (Option A) is a valid workaround, but the question implies the migration requires ongoing replication, and the error explicitly points to the missing archive log mode, not a choice to skip CDC.

How to eliminate wrong answers

Option A is wrong because disabling ongoing replication and performing only a full load would not meet the migration requirement if the company needs continuous replication or minimal downtime; the error specifically indicates DMS cannot capture changes, so the solution must address the logging issue, not avoid CDC. Option B is wrong because setting up an Oracle Data Guard standby does not resolve the archive log mode requirement on the source; DMS would still need archive logs on the source or the standby to capture changes, and this adds unnecessary complexity. Option C is wrong because AWS Schema Conversion Tool (SCT) is used for schema conversion, not for enabling change data capture or resolving archive log mode issues; the error is about DMS’s inability to capture changes, not schema incompatibility.

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904
MCQmedium

A company is experiencing high read latency on their Amazon RDS for MySQL Multi-AZ DB instance. The application performs many small, random reads. Which configuration change would most likely reduce the read latency without incurring additional compute costs?

A.Increase the allocated storage size to improve I/O throughput.
B.Enable Performance Insights to monitor and optimize queries.
C.Enable Multi-AZ DB cluster deployment with two readable standby instances and route read traffic to the standby.
D.Increase the instance size from db.r5.large to db.r5.xlarge.
AnswerC

This offloads read traffic to the standby, reducing load on the primary and improving read latency.

Why this answer

The Multi-AZ DB cluster deployment for Amazon RDS includes two readable standby instances. By routing read traffic to these standbys, the load on the primary is reduced, which can decrease read latency without additional compute costs since the standbys are already provisioned. Option A is incorrect because increasing allocated storage primarily improves I/O throughput for sequential operations but may not significantly reduce latency for many small random reads.

Option B is incorrect because Performance Insights is a monitoring tool that helps identify performance bottlenecks but does not directly reduce read latency. Option D is incorrect because increasing the instance size (e.g., from db.r5.large to db.r5.xlarge) would incur additional compute costs.

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905
Multi-Selecteasy

Which TWO of the following are valid methods to secure data at rest for an Amazon RDS for MySQL DB instance?

Select 1 answer
A.Enable SSL/TLS for connections to the DB instance.
B.Enable encryption at rest using AWS KMS.
C.Use the '--enable-encryption' flag when creating the DB instance.
D.Implement Transparent Data Encryption (TDE) using Oracle-compatible settings.
E.Use Amazon RDS encryption at the table level using the ENCRYPT option.
AnswersB

RDS supports encryption at rest using KMS.

Why this answer

(encryption at rest using AWS KMS) is correct because Amazon RDS for MySQL supports encryption at rest using AWS KMS, which encrypts the underlying storage and automated backups. Option C is incorrect because there is no '--enable-encryption' flag in the AWS CLI; the correct parameter to enable encryption during instance creation is '--storage-encrypted'. Therefore, option C does not represent a valid method.

Option A is incorrect because SSL/TLS secures data in transit, not at rest. Option D is incorrect because Transparent Data Encryption (TDE) is not supported for MySQL on RDS; it is available for Oracle and SQL Server. Option E is incorrect because Amazon RDS encryption is applied at the storage level, not the table level.

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906
Multi-Selecteasy

A company is deploying a new Amazon DynamoDB table with on-demand capacity. The table will store session data for a web application. Which THREE features should be enabled to improve performance and durability?

Select 3 answers
A.Auto scaling
B.Time to Live (TTL)
C.DynamoDB Accelerator (DAX)
D.Point-in-time recovery (PITR)
E.Global tables
AnswersC, D, E

DynamoDB Accelerator (DAX) provides an in-memory cache that reduces read latency to microseconds, significantly improving performance.

Why this answer

For an on-demand DynamoDB table, Auto scaling is not applicable because on-demand mode automatically scales throughput. To improve performance, use DynamoDB Accelerator (DAX) which provides an in-memory cache for microsecond read latency. For durability, enable Point-in-time recovery (PITR) for continuous backups and Global tables for multi-region replication, which also provides disaster recovery and low-latency global reads.

Time to Live (TTL) helps manage data lifecycle but does not directly improve performance or durability.

Exam trap

The trap is that candidates may think Auto Scaling is necessary for on-demand tables, but on-demand mode already scales automatically. They might also believe TTL improves performance by removing old data, but its primary purpose is data lifecycle management, not performance. For performance, DAX is key; for durability, PITR and Global tables are needed.

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907
Multi-Selectmedium

A database engineer is troubleshooting high CPU usage on an Amazon RDS for PostgreSQL instance. Amazon CloudWatch shows CPU Utilization consistently above 90% during business hours. Which combination of actions should the engineer take to identify the root cause? (Choose TWO.)

Select 2 answers
A.Enable Enhanced Monitoring and review OS process list.
B.Review the slow query log to identify long-running queries.
C.Scale up the DB instance to a larger instance class.
D.Enable Performance Insights and review the top SQL queries.
E.Install pg_stat_statements extension and query it.
AnswersB, D

Slow query logs can reveal queries that consume significant CPU resources.

Why this answer

Reviewing the slow query log directly identifies long-running queries that can cause sustained high CPU usage on RDS for PostgreSQL. Option D is correct because Performance Insights provides a visual dashboard of database load and top SQL queries, making it easy to pinpoint which queries are consuming the most CPU resources.

Exam trap

The trap here is that candidates often choose Option E (pg_stat_statements) thinking it is the only way to get query-level details, but Performance Insights (Option D) provides the same data with less effort and is the recommended AWS-native approach for this scenario.

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908
MCQhard

A company attaches the above IAM policy to a user. The user tries to modify the DB instance 'prod-db' in us-east-1. What is the result?

A.The user cannot describe DB instances.
B.The user can modify prod-db because the Allow statement covers it.
C.The user cannot modify prod-db.
D.The user cannot modify any DB instance.
AnswerD

The explicit Deny with `Resource: "*"` denies `rds:ModifyDBInstance` on all DB instances, so the user cannot modify any DB instance. This option is correct.

Why this answer

The IAM policy includes an explicit Deny statement that denies the `rds:ModifyDBInstance` action on all resources (`Resource: "*"`). In AWS IAM, an explicit Deny overrides any Allow. Therefore, the user cannot modify any DB instance, including `prod-db`.

Options A and B are incorrect because the user can describe DB instances (unless denied) and the Allow does not override the Deny. Option C is incorrect because the Deny applies to all instances, not just `prod-db`.

Exam trap

Candidates often focus on the resource ARN in the Deny statement and miss that it is `"*"`, meaning it applies to all resources. They may think the Deny is scoped only to `prod-db` if they misread the policy.

How to eliminate wrong answers

Option A is wrong because the policy includes an Allow statement for `rds:DescribeDBInstances` on all resources, so the user can describe DB instances. Option B is wrong because the explicit Deny for `rds:ModifyDBInstance` overrides the Allow, preventing modification of `prod-db`. Option D is wrong because the Deny only applies to `rds:ModifyDBInstance`; the user can still modify other DB instances if allowed by other policies, but the explicit Deny blocks modification of any DB instance due to the wildcard resource in the Deny statement.

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909
MCQhard

A social media company runs a multi-region application on Amazon DynamoDB with global tables. The application is deployed in us-east-1 and eu-west-1. Recently, they enabled DynamoDB Streams on the table to trigger an AWS Lambda function for real-time analytics. The Lambda function runs in us-east-1. After enabling streams, they notice that the Lambda function is triggered multiple times for the same update, and the application's write latency in eu-west-1 has increased. The company has strict latency requirements. A database specialist is asked to resolve the issue. What should the database specialist recommend?

A.Increase the Lambda function's reserved concurrency to handle duplicate events faster.
B.Create a separate Lambda function in eu-west-1 and use a local stream.
C.Modify the Lambda function to check the 'awsRegion' attribute in the stream record and ignore records from other regions.
D.Disable DynamoDB Streams on the eu-west-1 replica table.
AnswerC

This prevents duplicate processing of the same write event.

Why this answer

Global tables replicate writes across regions, and each write generates a stream record. The Lambda function should filter on the 'awsRegion' attribute in the stream record to avoid processing the same update from different regions. Option A is wrong because increasing Lambda concurrency doesn't prevent duplicate processing.

Option B is wrong because using a dedicated stream for each region is not supported. Option D is wrong because disabling streams in eu-west-1 would break the replication.

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910
Multi-Selectmedium

A security administrator is setting up a new Amazon RDS for SQL Server database. The company requires that all data be encrypted at rest and in transit. Additionally, the database must be accessible only from a specific CIDR range. Which TWO actions should the administrator take? (Choose TWO.)

Select 2 answers
A.Enable encryption at rest using AWS KMS.
B.Configure a security group that allows inbound traffic from the specific CIDR range.
C.Enable encryption in transit by modifying the RDS option group to include SSL.
D.Modify the DB parameter group to restrict network access.
E.Use AWS CloudHSM to manage encryption keys for the database.
AnswersA, B

Encryption at rest is a requirement.

Why this answer

Enabling encryption at rest using AWS KMS is a straightforward way to meet the encryption-at-rest requirement for RDS. Option B: Configuring a security group to allow inbound traffic from the specific CIDR range restricts network access to the database. Option C is incorrect because encryption in transit is handled by the database engine (e.g., SSL/TLS) and is not an RDS option group feature; you enable it on the client side or by modifying the DB parameter group.

Option D is incorrect because DB parameter groups do not control network access; they manage database engine parameters. Option E is incorrect because AWS KMS is the default service for RDS encryption at rest; CloudHSM is an alternative for key management but not required.

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911
MCQhard

A company is designing a new e-commerce platform using Amazon DynamoDB. The workload requires single-digit millisecond latency for user session data, which is accessed by session token. The session data is temporary and should be automatically deleted after 24 hours. Which DynamoDB design should the database specialist recommend?

A.Create an AWS Lambda function that runs every hour and deletes expired session data
B.Store session data in Amazon S3 with a lifecycle policy to delete objects after 24 hours
C.Use DynamoDB Accelerator (DAX) to cache session data and set a 24-hour TTL on the cache
D.Enable DynamoDB Time to Live (TTL) on the session token attribute
AnswerD

TTL automatically deletes items after a specified expiry timestamp, meeting the 24-hour deletion requirement.

Why this answer

DynamoDB Time to Live (TTL) automatically deletes expired items after a specified timestamp, making it ideal for session data that must be removed after 24 hours. This approach requires no additional infrastructure, meets the single-digit millisecond latency requirement by using the session token as the primary key, and ensures automatic cleanup without manual intervention or added cost.

Exam trap

The trap here is that candidates may confuse DynamoDB TTL with a feature that provides real-time or immediate deletion, when in fact TTL deletes items asynchronously in the background, typically within a few minutes to 48 hours, which is acceptable for temporary session data but not for compliance-driven immediate removal.

How to eliminate wrong answers

Option A is wrong because using a Lambda function to delete expired data adds operational complexity, potential cost, and latency, and does not guarantee immediate deletion at the exact 24-hour mark, whereas DynamoDB TTL provides a native, serverless solution. Option B is wrong because Amazon S3 does not support single-digit millisecond latency for session data access and is not designed for real-time key-value lookups required by session token access. Option C is wrong because DAX is a caching layer that improves read performance but does not provide automatic deletion of expired data; setting a TTL on the cache only evicts items from the cache, not from the underlying DynamoDB table, leaving stale data in the table.

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912
Multi-Selectmedium

A company is using Amazon RDS for MySQL with Multi-AZ deployment. The primary instance recently experienced an unexpected failover due to a hardware failure. The database is 2 TB in size and has high write throughput. Which TWO actions should the database administrator take to minimize recovery time and ensure data durability?

Select 2 answers
A.Increase the DB instance class to a larger size to handle the write load.
B.Disable Multi-AZ and use a single-AZ deployment to avoid future failovers.
C.Enable automated backups with a retention period of 1 day to allow point-in-time recovery.
D.Take a manual snapshot of the DB instance immediately.
E.Verify that the failover completed successfully by checking the RDS event log and monitoring the new primary's status.
AnswersC, E

Automated backups enable point-in-time recovery, helping restore to the latest transaction.

Why this answer

Enabling automated backups with a retention period of 1 day allows for point-in-time recovery, which minimizes data loss and recovery time. Option E is correct because verifying that the failover completed successfully through the RDS event log and monitoring the new primary's status ensures data durability and confirms that the Multi-AZ deployment functioned properly. Option A is wrong because increasing the instance class does not directly reduce recovery time from a failover.

Option B is wrong because disabling Multi-AZ reduces availability and does not prevent hardware failures. Option D is wrong because manual snapshots are not as immediate as automated backups for point-in-time recovery and would take longer to restore.

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913
MCQeasy

A company is using Amazon Redshift for data warehousing. The operations team notices that queries are running slower than usual. Which Amazon Redshift system view should be used to identify the queries that are consuming the most resources?

A.SVV_VACUUM_PROGRESS
B.STL_QUERY
C.PG_TABLE_DEF
D.STV_TBL_PERM
AnswerB

STL_QUERY records all query execution details, including duration and resource usage.

Why this answer

STL_QUERY (option B) is the correct system view because it stores detailed information about query execution, including resource consumption. This allows the operations team to identify which queries are consuming the most resources. The other options are incorrect: SVV_VACUUM_PROGRESS (option A) shows the progress of vacuum operations, not query performance.

PG_TABLE_DEF (option C) shows table definitions. STV_TBL_PERM (option D) shows table permissions, not query resource usage.

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914
MCQeasy

A company has an Amazon Aurora MySQL DB cluster with one writer and two readers. The application is experiencing high read latency. CloudWatch shows that the 'AuroraBinlogReplicaLag' metric is high for one of the reader instances. What is the most likely cause?

A.The Aurora cluster storage is experiencing high I/O latency
B.The reader instance is not keeping up with the write workload from the writer
C.The reader instance is undersized and needs to be scaled up
D.The DB cluster parameter group is misconfigured
AnswerB

Binlog replication lag means the reader is behind in applying changes.

Why this answer

The 'AuroraBinlogReplicaLag' metric measures the lag between the writer and a reader when using binary log replication. A high value indicates that the reader is not applying changes from the writer quickly enough, causing read latency. This is typically because the reader instance is not keeping up with the write workload from the writer.

Option A is incorrect because Aurora storage is shared and not the cause of replication lag. Option C is incorrect; while an undersized reader can contribute to lag, the primary cause is the reader not keeping pace with writes, not necessarily size. Option D is incorrect because parameter groups affect settings but not replication lag directly.

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915
MCQhard

A company is moving a self-hosted MySQL database to Amazon Aurora MySQL. The current database uses InnoDB with full-text indexes and triggers. The migration must be done with zero downtime. Which approach meets these requirements?

A.Use AWS DMS with ongoing replication from the source MySQL database
B.Use mysqldump to export the database and import into Aurora
C.Set up MySQL replication from on-premises to Aurora using native binlog replication
D.Export data to Amazon S3 and use Aurora's LOAD DATA FROM S3 command
AnswerA

AWS DMS with ongoing replication (change data capture) allows continuous synchronization from the source MySQL database to Amazon Aurora MySQL, enabling zero-downtime migration. DMS captures incremental changes from the source's binary logs and applies them to the target, keeping the database fully operational.

Why this answer

AWS DMS with ongoing replication (change data capture) allows continuous synchronization from the source MySQL database to Amazon Aurora MySQL, enabling a zero-downtime migration. DMS captures incremental changes from the source's binary logs (binlog) and applies them to the target, so the database remains fully operational during the migration. This approach also supports InnoDB tables, full-text indexes, and triggers, as DMS handles schema and data replication for these features.

Option C (native MySQL binlog replication) is supported by Aurora MySQL, but it requires an initial seed of data which introduces downtime because the source database typically needs to be in a read-only state for a consistent backup. In contrast, DMS with CDC avoids any initial downtime.

Exam trap

The trap here is that candidates may assume native MySQL binlog replication (Option C) is a fully supported zero-downtime option for migrating to Aurora MySQL. While Aurora does support this feature, it requires careful configuration and does not provide the managed CDC, schema conversion, and cutover capabilities that AWS DMS offers. Additionally, native replication requires manual handling of binary log positions and can lead to replication lag issues.

Therefore, DMS is the recommended approach for a zero-downtime migration, making Option C less suitable despite its apparent viability.

How to eliminate wrong answers

Option B is wrong because mysqldump performs a logical export that locks tables or requires a read-only period, causing downtime during the dump and import process, and it does not support ongoing replication for zero-downtime migration. Option C is wrong because native MySQL binlog replication to Aurora is not supported directly; Aurora MySQL uses a different replication mechanism (Aurora Replicas) and does not accept external binlog replication from an on-premises MySQL instance without additional configuration or a custom solution, and it may not handle full-text indexes and triggers seamlessly. Option D is wrong because exporting data to S3 and using LOAD DATA FROM S3 is a bulk load operation that requires the source database to be stopped or read-only during the export, and it does not provide ongoing replication, thus cannot achieve zero downtime.

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916
MCQhard

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

A.Enable automated backups and configure cross-Region snapshot copy. Use point-in-time recovery in the secondary region.
B.Deploy an Aurora Global Database with a secondary cluster in us-west-2. Configure the secondary cluster as a failover target.
C.Use AWS Database Migration Service (DMS) to continuously replicate changes to an Aurora cluster in us-west-2.
D.Create cross-Region read replicas in us-west-2 and set up a replication channel. In disaster, promote a replica.
AnswerB

Aurora Global Database provides replication across regions with typical RPO of seconds and RTO of minutes. It is the most appropriate solution for low RPO/RTO with minimal overhead.

Why this answer

Aurora Global Database provides replication across regions with typical RPO of seconds and RTO of minutes. It is the most appropriate solution for low RPO/RTO with minimal overhead. Option B is correct.

Option A (automated backups) have an RPO of 5 minutes, which exceeds the required 5 seconds. Option C (DMS) adds cost and complexity. Option D (cross-Region read replicas) can have replication lag exceeding 5 seconds.

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917
MCQeasy

A company is using Amazon DynamoDB with on-demand capacity for a serverless web application. The application experiences occasional throttling. The DynamoDB table has a simple primary key (partition key only). The throttled requests are related to a small number of partition keys. What is the MOST likely cause?

A.On-demand capacity has a per-partition throughput limit that is too low.
B.The partition key design leads to uneven access patterns, causing a hot partition.
C.The table uses a composite primary key, which limits throughput.
D.The table's read/write capacity mode is set to provisioned instead of on-demand.
AnswerB

A hot partition exceeds the partition's throughput limit, causing throttling.

Why this answer

Throttling on a few partition keys indicates a hot partition. Option A is wrong because on-demand capacity handles overall traffic, but partition-level limits still apply. Option C is wrong because a simple primary key is fine, but the data distribution is the issue.

Option D is wrong because on-demand capacity automatically scales, but not per partition beyond the limit.

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918
MCQeasy

A company needs to migrate a SQL Server database to Amazon RDS for SQL Server. The database uses stored procedures with xp_cmdshell. What should the company do?

A.Rewrite the stored procedures to remove dependency on xp_cmdshell before migration.
B.Use RDS Custom for SQL Server to retain full control.
C.Enable xp_cmdshell in the RDS parameter group.
D.Disable xp_cmdshell in the stored procedures and migrate as-is.
AnswerA

xp_cmdshell is not supported; procedures must be modified to use alternatives.

Why this answer

Amazon RDS for SQL Server does not support the xp_cmdshell extended stored procedure because it allows execution of operating system commands, which violates the managed service model where AWS controls the host OS. The correct approach is to rewrite the stored procedures to remove dependency on xp_cmdshell before migration, using alternatives such as SQL Server Agent jobs, CLR integration, or external scripting via AWS Lambda or EC2.

Exam trap

The trap here is that candidates assume RDS Custom for SQL Server provides full OS control, but AWS still restricts xp_cmdshell and other OS-level features, making rewriting the only viable option.

How to eliminate wrong answers

Option B is wrong because RDS Custom for SQL Server does not enable xp_cmdshell; even with RDS Custom, AWS restricts access to the underlying OS and does not allow enabling xp_cmdshell due to security policies. Option C is wrong because xp_cmdshell cannot be enabled in an RDS parameter group; the RDS parameter group does not expose the 'xp_cmdshell' configuration option, and attempting to set it will be ignored or cause an error. Option D is wrong because simply disabling xp_cmdshell in the stored procedures and migrating as-is will break the procedures; the procedures must be rewritten to use alternative methods that do not rely on xp_cmdshell at all.

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919
MCQhard

A gaming company uses Amazon DynamoDB for player session data. Each session has a partition key of `game_id` and a sort key of `session_id`. The table has a global secondary index (GSI) on `player_id` for leaderboard queries. Recently, the company noticed that write traffic to the GSI is causing throttling on the base table, even though the base table's write capacity is not fully utilized. What is the MOST likely cause?

A.The application is using strongly consistent reads on the GSI, which consumes double the read capacity.
B.The GSI is not designed with a high-cardinality partition key, causing write hot spots on the GSI.
C.Point-in-time recovery (PITR) is enabled, consuming extra write capacity.
D.The table's auto-scaling settings are not configured correctly for the GSI.
AnswerB

A hot GSI partition can throttle writes, affecting the base table writes.

Why this answer

A global secondary index (GSI) has its own provisioned read and write capacity, separate from the base table. If the GSI's partition key (player_id) has low cardinality (e.g., only a few distinct player_id values), writes to the base table will concentrate on a small number of GSI partitions, causing throttling on the GSI. This throttling on the GSI then back-pressures the base table, resulting in write throttling on the base table even if its own write capacity is underutilized.

Exam trap

The trap here is that candidates often assume throttling on the base table is always caused by the base table's own capacity settings, overlooking that GSIs have independent capacity and can cause back-pressure on the base table when their partition key design leads to hot spots.

How to eliminate wrong answers

Option A is wrong because strongly consistent reads are not supported on GSIs in DynamoDB; GSIs only support eventually consistent reads, so this option describes an impossible scenario. Option C is wrong because point-in-time recovery (PITR) does not consume write capacity; it uses separate backup storage and does not affect the table's provisioned write throughput. Option D is wrong because auto-scaling settings for the GSI are independent of the base table; misconfigured auto-scaling could cause throttling on the GSI itself, but the question states the base table's write capacity is not fully utilized, and the core issue is the GSI's partition key cardinality causing hot spots, not auto-scaling misconfiguration.

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920
MCQmedium

A company is migrating an on-premises Oracle database to Amazon Aurora PostgreSQL. The database is 2 TB and has a daily change rate of 10%. They have a 1 Gbps network connection to AWS. They want to minimize downtime during the migration. Which migration approach should they use?

A.Use AWS Schema Conversion Tool (SCT) to convert schema, then import data
B.Use AWS Database Migration Service (DMS) with ongoing replication
C.Use pg_dump to export the database and pg_restore to import
D.Create an Aurora read replica from the Oracle database
AnswerB

DMS supports continuous change data capture (CDC) to minimize downtime.

Why this answer

AWS DMS with ongoing replication (change data capture) is the correct choice because it allows continuous replication of changes from the source Oracle database to the target Aurora PostgreSQL, minimizing downtime by keeping the target nearly in sync until a final cutover. The 2 TB size and 10% daily change rate make a full offline export impractical, and DMS handles both schema conversion (via SCT integration) and data migration with minimal interruption.

Exam trap

The trap here is that candidates often assume native database tools like pg_dump/pg_restore can be used across different database engines, or that Aurora read replicas can be created from non-Aurora sources, leading them to choose options that are technically impossible or would cause excessive downtime.

How to eliminate wrong answers

Option A is wrong because AWS SCT only converts the schema and generates scripts; it does not perform the actual data migration or ongoing replication, so it would require a separate, potentially lengthy data load step that increases downtime. Option C is wrong because pg_dump/pg_restore are native PostgreSQL tools that require the source Oracle database to be exported to a flat file format (e.g., CSV or custom dump), which is not directly compatible with Oracle; they also require taking the source offline or using a read-only snapshot, leading to significant downtime for a 2 TB database with high daily change. Option D is wrong because Aurora read replicas can only be created from an existing Aurora PostgreSQL or MySQL instance, not from an external Oracle database; this option reflects a fundamental misunderstanding of cross-engine replication capabilities.

Full explanation →
921
MCQhard

A database administrator is trying to delete the RDS instance named 'prod-critical' using the AWS CLI. The IAM policy shown is attached to the user. What will happen?

A.The delete will succeed only if the user includes a condition.
B.The delete will fail because the Deny statement explicitly denies the action for that resource.
C.The delete will fail because the policy has a syntax error.
D.The delete will succeed because the Allow statement grants permission.
AnswerB

Deny overrides Allow, so the delete fails.

Why this answer

The Deny statement in the policy explicitly denies the 'rds:DeleteDBInstance' action on the resource 'arn:aws:rds:*:*:db:prod-critical'. In AWS IAM, an explicit Deny always overrides any Allow, so the delete will fail. Option A is incorrect because a condition is not relevant; the Deny is unconditional.

Option C is incorrect because the policy has no syntax error. Option D is incorrect because the Allow does not apply due to the overriding Deny.

Full explanation →
922
MCQeasy

A company wants to deploy a MySQL database that automatically scales read capacity based on traffic without manual intervention. Which AWS database offering should they use?

A.Amazon DynamoDB with auto scaling
B.Amazon RDS for MySQL with Multi-AZ deployment
C.Amazon RDS for MySQL with a Single-AZ deployment
D.Amazon Aurora with Auto Scaling for read replicas
AnswerD

Aurora Auto Scaling automatically adjusts the number of read replicas based on CPU or connections.

Why this answer

Amazon Aurora with Auto Scaling for read replicas is correct because it automatically adjusts the number of Aurora Replicas based on changes in read workload, scaling read capacity without manual intervention. Aurora's Auto Scaling monitors the average CPU utilization or connections of the reader fleet and adds or removes replicas to maintain a target metric, providing seamless read scaling for MySQL-compatible databases.

Exam trap

The trap here is that candidates often confuse Multi-AZ deployments (which provide failover, not read scaling) with read replica auto scaling, or they incorrectly assume DynamoDB's auto scaling applies to MySQL workloads, when the question explicitly requires a MySQL database.

How to eliminate wrong answers

Option A is wrong because Amazon DynamoDB is a NoSQL database, not a MySQL database, and its auto scaling adjusts write and read capacity units, not read replicas for a relational MySQL workload. Option B is wrong because Amazon RDS for MySQL with Multi-AZ deployment provides high availability by maintaining a standby replica in a different Availability Zone, but it does not automatically scale read capacity; the standby is not used for read traffic unless a failover occurs. Option C is wrong because Amazon RDS for MySQL with a Single-AZ deployment offers no read replica scaling at all; it is a single instance that cannot automatically add read capacity based on traffic.

Full explanation →
923
MCQhard

A database specialist is troubleshooting an Amazon Aurora MySQL cluster. The writer instance's CPU is at 90% and there are frequent 'Lock wait timeout exceeded' errors. The application uses many short-lived connections. What should the specialist do FIRST to reduce lock contention?

A.Add additional read replicas to distribute read traffic
B.Increase the instance size to handle more concurrent transactions
C.Enable RDS Proxy to pool database connections
D.Reduce the maximum number of connections in the application
AnswerC

RDS Proxy reduces connection churn, decreasing lock contention.

Why this answer

Enabling RDS Proxy reduces connection churn by pooling database connections, which decreases the number of concurrent transactions competing for locks, thereby reducing lock contention. Option A is incorrect because adding read replicas does not address write lock contention on the writer instance. Option B is incorrect although increasing instance size can help with CPU, it does not directly reduce lock contention and may even exacerbate it by allowing more transactions.

Option D is incorrect because reducing the maximum number of connections can actually increase contention per connection as the same workload is funneled through fewer connections.

Full explanation →
924
MCQeasy

A company needs a database for a serverless web application that stores user sessions. The sessions expire after 24 hours. The database must be highly available and require no server management. Which AWS service is most appropriate?

A.Amazon ElastiCache for Redis with cluster mode.
B.Amazon DynamoDB with TTL.
C.Amazon RDS for PostgreSQL with Multi-AZ.
D.Amazon S3 with lifecycle policies.
AnswerB

DynamoDB is serverless, highly available, and supports TTL.

Why this answer

Amazon DynamoDB with TTL is the most appropriate choice because it provides a fully managed, serverless, highly available NoSQL database that can automatically expire user sessions after 24 hours using the Time to Live (TTL) feature. DynamoDB's on-demand capacity mode eliminates server management, and its built-in replication across multiple Availability Zones ensures high availability without any manual intervention.

Exam trap

The trap here is that candidates often confuse ElastiCache for Redis as a 'database' for sessions, but the question explicitly requires 'no server management' and 'highly available' — DynamoDB is the only fully serverless, managed database option that meets all criteria, while ElastiCache still requires cluster management and is not serverless.

How to eliminate wrong answers

Option A is wrong because Amazon ElastiCache for Redis with cluster mode is an in-memory cache, not a durable database; while it can store sessions with TTL, it requires server management (e.g., node sizing, patching) and is not serverless. Option C is wrong because Amazon RDS for PostgreSQL with Multi-AZ requires server management (e.g., patching, scaling) and is not serverless; it also lacks a native TTL feature for automatic session expiration. Option D is wrong because Amazon S3 with lifecycle policies is an object storage service, not a database; it cannot efficiently handle high-frequency read/write operations for user sessions and lacks the low-latency query capabilities needed for session management.

Full explanation →
925
MCQhard

Refer to the exhibit. The exhibit shows CloudWatch metrics from an Amazon RDS for PostgreSQL instance. The application is experiencing slow query performance. Which is the most likely cause?

A.High storage latency due to provisioned IOPS being fully utilized or EBS volume contention
B.High CPU utilization due to complex queries
C.Too many database connections causing context switching
D.Insufficient memory causing disk swaps
AnswerA

Read/Write latency significantly above expected values for provisioned IOPS storage.

Why this answer

The exhibit shows that the 'Write IOPS' metric is consistently at or near the provisioned IOPS limit, while 'Write Latency' is elevated. When provisioned IOPS are fully utilized, Amazon EBS throttles I/O, causing increased storage latency. This directly degrades query performance for PostgreSQL, especially for write-heavy workloads, as each write operation must wait for the storage layer to complete.

Exam trap

The trap here is that candidates see high latency and assume a memory or CPU bottleneck, but the key clue is the IOPS metric hitting its provisioned ceiling, which directly points to storage I/O throttling as the root cause.

How to eliminate wrong answers

Option B is wrong because high CPU utilization due to complex queries would manifest as elevated 'CPU Utilization' metrics, not as a correlation between IOPS saturation and latency. Option C is wrong because too many database connections causing context switching would show high 'DatabaseConnections' and likely high 'CPUUtilization' or 'SwapUsage', not a direct IOPS-latency pattern. Option D is wrong because insufficient memory causing disk swaps would appear as high 'SwapUsage' and possibly 'FreeableMemory' dropping, not as IOPS hitting the provisioned limit with elevated latency.

Full explanation →
926
MCQmedium

A company is designing a database for a global application that requires active-active replication across two AWS Regions. The database must support multi-master writes with conflict resolution. Which AWS database service should they use?

A.Amazon Aurora Global Database
B.Amazon DynamoDB Global Tables
C.Amazon Redshift
D.Amazon RDS for MySQL with Multi-AZ
AnswerB

DynamoDB Global Tables provide multi-master active-active replication with conflict resolution.

Why this answer

Amazon DynamoDB Global Tables provides fully managed, multi-Region, multi-master replication, enabling active-active writes across two AWS Regions with built-in conflict resolution using last-writer-wins (based on timestamp). This directly meets the requirement for multi-master writes and conflict resolution without custom code.

Exam trap

The trap here is that candidates often confuse Amazon Aurora Global Database (which is single-master) with a multi-master solution, because its name includes 'Global' and it supports cross-Region replication, but it does not allow writes in secondary Regions.

How to eliminate wrong answers

Option A is wrong because Amazon Aurora Global Database supports only one primary Region for writes (single-master), with read replicas in secondary Regions, not multi-master writes. Option C is wrong because Amazon Redshift is a data warehouse designed for analytical queries, not transactional multi-master writes, and does not support active-active replication across Regions. Option D is wrong because Amazon RDS for MySQL with Multi-AZ provides high availability within a single Region (synchronous standby replica), not active-active replication across Regions, and does not support multi-master writes.

Full explanation →
927
Multi-Selectmedium

A company is using Amazon RDS for MySQL and needs to comply with PCI DSS requirements. Which TWO actions should the company take to secure the database? (Choose TWO.)

Select 2 answers
A.Enable encryption at rest using AWS KMS.
B.Configure the database to write audit logs directly to an S3 bucket.
C.Enable audit logging to track database activities.
D.Enable public accessibility on the RDS instance to allow access from anywhere.
E.Change the default database port to a non-standard port.
AnswersA, C

Encryption at rest is required for data protection.

Why this answer

Options A and C are correct. Enabling encryption at rest using AWS KMS protects data on disk, which is a PCI DSS requirement. Enabling audit logging helps track database activities for compliance.

Options B and D are incorrect: writing audit logs directly to an S3 bucket is not supported; RDS audit logs are sent to CloudWatch Logs. Enabling public accessibility would violate security requirements. Option E is incorrect because changing the default port is not a PCI DSS requirement and may complicate management without adding meaningful security.

Full explanation →
928
MCQmedium

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

A.Check the DB parameter group for the 'remote_listener' parameter.
B.Check the automated backup retention period.
C.Verify that the DB subnet group includes a public subnet with an internet gateway.
D.Review the CloudWatch Logs for error logs.
AnswerC

Public accessibility requires a public subnet and internet gateway.

Why this answer

The database is publicly accessible, meaning it must be in a public subnet that has an internet gateway attached. Even if the security group allows traffic, the subnet must be configured correctly for public access. Option A is incorrect because the 'remote_listener' parameter affects Oracle listener registration, not basic network connectivity.

Option B is incorrect because backup retention is unrelated to connection issues. Option D is incorrect because CloudWatch Logs may not capture failed connection attempts at the network level.

Full explanation →
929
Multi-Selecthard

Which THREE factors should be considered when choosing between Amazon RDS Multi-AZ and Amazon Aurora for high availability? (Choose 3)

Select 3 answers
A.Aurora provides faster failover (typically under 30 seconds)
B.RDS Multi-AZ provides automatic scaling of storage
C.RDS Multi-AZ supports encryption at rest
D.Aurora automatically scales storage up to 128 TB per instance
E.Aurora supports up to 15 read replicas, while RDS Multi-AZ supports only 1 standby
AnswersA, D, E

Aurora failover is faster than RDS Multi-AZ.

Why this answer

Aurora's distributed storage architecture enables failover in under 30 seconds typically, because the storage layer is shared across all instances in the cluster. When the primary instance fails, Aurora simply promotes one of the existing read replicas to primary, without needing to remap storage volumes. This is significantly faster than RDS Multi-AZ, which requires a DNS change and a synchronous block-level replication failover that can take 60-120 seconds.

Exam trap

The trap here is that candidates confuse RDS Multi-AZ's synchronous replication with Aurora's shared storage architecture, assuming both have similar failover times, but Aurora's failover is consistently faster due to its distributed storage layer.

Full explanation →
930
MCQhard

A team is using AWS DMS to migrate a 3 TB Oracle database to Amazon Aurora PostgreSQL. They configured the task as shown. After the full load completes, they notice that the target tables have no indexes, primary keys, or foreign keys. What is the most likely cause?

A.The migration task did not include transformation rules to create indexes and constraints.
B.The table selection rule uses wildcards and excludes system tables, which also excludes index definitions.
C.The TargetTablePrepMode is set to DROP_AND_CREATE, which creates tables without indexes or constraints.
D.The task is using a full load without LOB support, which prevents index creation.
AnswerC

DROP_AND_CREATE mode creates target tables with only the column definitions, not indexes or constraints.

Why this answer

When TargetTablePrepMode is set to DROP_AND_CREATE, AWS DMS drops the target table if it exists and then creates a new one using only the basic column definitions from the source. It does not migrate indexes, primary keys, or foreign keys because DMS is not designed to replicate schema objects beyond table structure and data. To preserve constraints and indexes, you must either pre-create them on the target or use a different prep mode like TRUNCATE_BEFORE_LOAD.

Exam trap

The trap here is that candidates assume DMS automatically replicates all schema objects, including indexes and constraints, when in fact DMS only migrates table structure and data, leaving schema objects like indexes and foreign keys to be handled separately.

How to eliminate wrong answers

Option A is wrong because transformation rules in DMS are used to rename tables, columns, or schemas, not to create indexes or constraints; index and constraint creation is not a feature of DMS transformation rules. Option B is wrong because wildcard table selection rules and exclusion of system tables affect which tables are migrated, not whether indexes or constraints are created on the target; DMS does not replicate index definitions regardless of table selection. Option D is wrong because LOB support settings control how large objects are handled during migration and have no impact on index or constraint creation; indexes are not created even when LOB support is enabled.

Full explanation →
931
Multi-Selecthard

A company is deploying an Amazon DynamoDB table with on-demand capacity mode. The table will be accessed from multiple AWS Regions and requires strong consistency. Which THREE steps should be taken to meet these requirements?

Select 2 answers
A.Create a global secondary index
B.Use DynamoDB Accelerator (DAX)
C.Configure auto scaling
D.Enable DynamoDB global tables
E.Use strongly consistent reads
AnswersA, D

Correct. A global secondary index allows efficient querying across Regions using different keys, which is important for accessing data from multiple Regions.

Why this answer

None of the options correctly meet the requirements. DynamoDB global tables provide multi-Region access but only eventual consistency, and strongly consistent reads are limited to the source Region. A global secondary index does not enable cross-Region access.

Therefore, no combination of the listed steps can achieve strong consistency across multiple Regions.

Exam trap

A common trap is assuming that strongly consistent reads work with global tables across all Regions. In reality, global tables only provide eventual consistency, so strongly consistent reads are not supported in replica Regions. The only way to get strong consistency is to read from the source Region.

Full explanation →
932
MCQhard

Refer to the exhibit. A developer runs a DynamoDB query against a global secondary index. The index's partition key is 'status' and sort key is 'created_at'. There are many items with status 'PENDING' in the table. Why does the query return zero items?

A.The query is incorrectly targeting the base table instead of the index.
B.The query must include a condition on the sort key to return results.
C.The key-condition-expression uses the wrong attribute name placeholder.
D.The global secondary index is not yet backfilled with data from the base table.
AnswerD

If the index was recently created, it may still be in the process of backfilling, so items are not yet available.

Why this answer

When a global secondary index (GSI) is created on an existing DynamoDB table, the index is populated asynchronously with data from the base table. If the query returns zero items, it likely means the backfill process has not yet completed, so the index does not yet contain the items that match the query. This is a common scenario when a GSI is newly added and the table contains a large number of items.

Exam trap

The trap here is that candidates often assume a GSI is immediately available with all data upon creation, but DynamoDB requires asynchronous backfill, and queries during this period can return zero items even when matching data exists in the base table.

How to eliminate wrong answers

Option A is wrong because the query explicitly references the index name (e.g., via the IndexName parameter), so it is not targeting the base table. Option B is wrong because a query on a GSI can return results using only a partition key condition; the sort key condition is optional and not required to return items. Option C is wrong because the key-condition-expression uses the correct attribute name placeholders (e.g., ':status' and ':created_at'), and the error would be a validation error, not a silent return of zero items.

Full explanation →
933
MCQmedium

A healthcare application requires storing patient records that include structured data (e.g., name, age) and unstructured data (e.g., medical images). The application needs to query structured data with SQL and serve images via HTTPS. Which combination of AWS services provides the MOST efficient design?

A.Amazon DynamoDB for structured data and Amazon S3 for images with DynamoDB Accelerator (DAX)
B.Amazon RDS for MySQL for structured data and Amazon EFS for images
C.Amazon RDS for PostgreSQL for structured data and Amazon S3 for images with Amazon CloudFront
D.Amazon Redshift for structured data and Amazon S3 for images
AnswerC

RDS provides SQL; S3 and CloudFront serve images efficiently.

Why this answer

Amazon RDS for PostgreSQL provides full SQL querying capabilities for structured patient data, while Amazon S3 with Amazon CloudFront offers scalable, low-latency HTTPS serving of medical images. CloudFront caches images at edge locations, reducing latency and offloading S3, which is the most efficient design for mixed structured/unstructured workloads.

Exam trap

The trap here is that candidates may choose DynamoDB (Option A) thinking it handles both structured and unstructured data, but it lacks SQL support, which is explicitly required for querying structured data in this scenario.

How to eliminate wrong answers

Option A is wrong because DynamoDB is a NoSQL database that does not support SQL queries, and DAX is a caching layer that does not add SQL capability. Option B is wrong because Amazon EFS is a file system not optimized for high-concurrency HTTPS image serving and lacks the global edge caching benefits of CloudFront. Option D is wrong because Amazon Redshift is a data warehouse designed for analytical queries, not transactional SQL workloads, and it is over-provisioned and costly for simple structured data storage.

Full explanation →
934
MCQmedium

A company runs an Amazon Redshift cluster with three nodes. The data warehouse team notices that some queries are slow due to high disk usage. The cluster has reached 80% storage capacity. What is the MOST cost-effective way to increase storage without interrupting operations?

A.Add one more node of the same type to the cluster.
B.Use classic resize to change to a node type with larger storage.
C.Perform an elastic resize to change to a node type with larger storage per node.
D.Create a second cluster and use Redshift Spectrum to offload queries.
AnswerC

Elastic resize completes in minutes and minimizes downtime.

Why this answer

Amazon Redshift's elastic resize operation allows you to change the node type to one with larger storage per node without downtime, and it completes in minutes. This is the most cost-effective approach for a cluster at 80% capacity, as it avoids the overhead of provisioning additional nodes or the longer downtime associated with classic resize, while directly addressing the high disk usage by increasing per-node storage.

Exam trap

The trap here is that candidates often confuse elastic resize with classic resize, assuming both require downtime, or they incorrectly think adding a node of the same type is a simple online operation, when in fact Redshift does not support online addition of nodes without a resize operation.

How to eliminate wrong answers

Option A is wrong because adding a node of the same type increases compute and storage capacity, but it requires a classic resize (which involves downtime) or an elastic resize (which only supports changing node type, not adding nodes of the same type). Option B is wrong because classic resize requires the cluster to be read-only during the operation and can take hours, causing significant operational interruption, which violates the requirement to not interrupt operations. Option D is wrong because creating a second cluster and using Redshift Spectrum offloads queries to Amazon S3, but it does not increase the storage capacity of the existing cluster; Spectrum is for querying external data, not for expanding local disk space, and it adds complexity and cost without solving the high disk usage issue.

Full explanation →
935
Multi-Selecteasy

A company is deploying a new MySQL database on Amazon RDS. They need to ensure that the database is automatically backed up daily and retained for 30 days. Which TWO configurations should be set? (Choose 2)

Select 2 answers
A.Set backup retention period to 30 days.
B.Enable automated backups.
C.Set backup window.
D.Enable Multi-AZ deployment.
E.Create a manual snapshot daily.
AnswersA, B

Defines retention.

Why this answer

Setting the backup retention period to 30 days ensures that automated backups are retained for the required duration. Option B is correct because automated backups must be enabled for the retention period to take effect; without enabling automated backups, no automatic daily backups occur. Together, these two settings satisfy the requirement for daily automated backups retained for 30 days.

Exam trap

The trap here is that candidates often confuse the backup window (Option C) or Multi-AZ (Option D) as being required for backup retention, when in fact only enabling automated backups and setting the retention period are necessary; the backup window is optional and Multi-AZ is unrelated to backup retention.

Full explanation →
936
MCQmedium

A company is designing a database for a social media application that requires low-latency access to user profiles and support for complex graph queries. Which AWS database service is most suitable for this workload?

A.Amazon DynamoDB
B.Amazon ElastiCache for Redis
C.Amazon Neptune
D.Amazon RDS for MySQL
AnswerC

Neptune is a graph database with support for graph queries.

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 the ideal choice for social media applications that require low-latency traversal of complex relationships, such as friend-of-friend recommendations or influence paths.

Exam trap

The trap here is that candidates often mistake DynamoDB's low-latency key-value lookups as sufficient for graph queries, overlooking that DynamoDB cannot perform multi-step traversals without multiple round trips and client-side joins, which destroys performance for complex relationship queries.

How to eliminate wrong answers

Option A is wrong because Amazon DynamoDB is a key-value and document database that excels at high-throughput, low-latency lookups by primary key but lacks native support for graph traversal queries (e.g., multi-hop joins or pathfinding) required for complex graph workloads. Option B is wrong because Amazon ElastiCache for Redis is an in-memory data store primarily used for caching, session management, and real-time analytics; it does not provide a graph query engine (like Gremlin or SPARQL) and cannot efficiently execute complex graph traversals across deeply connected data. Option D is wrong because Amazon RDS for MySQL is a relational database that uses SQL joins and recursive CTEs to model graphs, but these operations become exponentially slower as graph depth increases, failing to meet the low-latency requirements for complex graph queries at scale.

Full explanation →
937
MCQhard

A company runs a document database using Amazon DocumentDB. They notice that some queries are taking much longer than expected. The explain plan shows a COLLSCAN. Which action would most improve query performance?

A.Increase the instance size to the next tier
B.Change the read consistency from eventual to strong
C.Increase the storage allocated to the instance
D.Create appropriate indexes on the fields used in query filters
AnswerD

Indexes prevent full collection scans.

Why this answer

Creating appropriate indexes on the fields used in query filters is the most effective action to improve performance because it eliminates the need for a COLLSCAN (collection scan). Increasing instance size (option A) may help but does not address the root cause of missing indexes. Changing read consistency from eventual to strong (option B) affects the freshness of reads, not query performance.

Increasing storage (option C) does not improve query speed; it only provides more space for data.

Full explanation →
938
MCQmedium

A company is running an Amazon RDS for MySQL Multi-AZ DB instance. They notice that the application is experiencing increased latency during peak hours. The DB instance's CPU utilization is consistently above 80%, and the Read Latency metric is high. Which action would most effectively reduce the latency without requiring application changes?

A.Create a read replica and direct read queries to it.
B.Disable Multi-AZ to free up resources.
C.Increase the DB instance class to a larger size.
D.Enable Multi-AZ on the DB instance.
AnswerA

Correct. Offloading read queries to a read replica reduces load on the primary instance and lowers read latency.

Why this answer

Creating a read replica offloads read queries from the primary DB instance, reducing read latency and CPU utilization without requiring application changes. Option B is incorrect because disabling Multi-AZ removes the standby instance, reducing availability and not addressing read latency. Option C is incorrect because increasing the instance class may improve performance but does not specifically offload read traffic; read replicas are more targeted for read-heavy workloads.

Option D is incorrect because the instance already has Multi-AZ enabled; enabling it again has no effect and does not reduce read latency.

Full explanation →
939
MCQhard

A company uses Amazon RDS for Oracle with a custom application that generates complex hierarchical queries using CONNECT BY. They want to migrate to Amazon Aurora to reduce licensing costs. Which migration strategy requires the fewest application changes?

A.Use AWS Database Migration Service (DMS) to migrate directly to Aurora with no application changes.
B.Migrate to Amazon Aurora MySQL and use its hierarchical query features.
C.Migrate to Amazon Aurora PostgreSQL and rewrite hierarchical queries to use recursive CTEs.
D.Migrate to Amazon RDS for SQL Server and use its recursive CTEs.
AnswerC

PostgreSQL supports recursive CTEs, which are equivalent to CONNECT BY.

Why this answer

Aurora PostgreSQL supports recursive Common Table Expressions (CTEs) via the WITH RECURSIVE clause, which can be used to rewrite Oracle's CONNECT BY hierarchical queries with minimal application changes. This approach avoids the licensing costs of Oracle while preserving the hierarchical query logic, requiring only a syntax rewrite rather than a complete redesign. Aurora MySQL does not support CONNECT BY or recursive CTEs, making PostgreSQL the only Aurora engine that can handle this workload without significant application restructuring.

Exam trap

The trap here is that candidates assume Aurora MySQL can handle hierarchical queries because it is often marketed as Oracle-compatible, but it lacks CONNECT BY and recursive CTEs, making PostgreSQL the only Aurora engine that can natively support hierarchical queries with a straightforward rewrite.

How to eliminate wrong answers

Option A is wrong because AWS DMS cannot translate Oracle-specific CONNECT BY syntax into an equivalent Aurora MySQL or PostgreSQL query; it only migrates data, not query logic, so the application would break without changes. Option B is wrong because Amazon Aurora MySQL does not support CONNECT BY or recursive CTEs, so hierarchical queries would require a complete rewrite using alternative methods like nested sets or adjacency lists, which is far more complex than a simple CTE rewrite. Option D is wrong because migrating to Amazon RDS for SQL Server does not reduce licensing costs (SQL Server requires its own licenses) and still requires rewriting queries to use recursive CTEs, offering no advantage over PostgreSQL in this scenario.

Full explanation →
940
MCQmedium

A company is migrating a 2 TB PostgreSQL database from on-premises to Amazon RDS for PostgreSQL. The migration must have minimal downtime and support ongoing replication. Which AWS service should be used for the migration?

A.AWS S3 Transfer Acceleration for direct database export
B.AWS Data Migration Service with AWS Glue
C.AWS Snowball Edge for offline data transfer
D.AWS Database Migration Service (DMS) with change data capture
AnswerD

AWS DMS supports ongoing replication with CDC for minimal downtime.

Why this answer

AWS Database Migration Service (DMS) with change data capture (CDC) is the correct choice because it supports ongoing replication from the source PostgreSQL database to Amazon RDS for PostgreSQL with minimal downtime. DMS performs a full load of the 2 TB database and then continuously applies changes using PostgreSQL's logical replication slots (via the pglogical extension or native WAL-based CDC), ensuring the target remains synchronized during the migration cutover.

Exam trap

The DBS-C01 exam often tests the misconception that AWS Data Migration Service (a non-existent service) combined with AWS Glue is a valid migration path, when in fact the correct service is AWS Database Migration Service (DMS) with CDC, and Glue is unrelated to live database replication.

How to eliminate wrong answers

Option A is wrong because AWS S3 Transfer Acceleration is a service for speeding up uploads to S3 over the internet, not a database migration tool; it cannot perform ongoing replication or handle PostgreSQL schema and data conversion. Option B is wrong because AWS Data Migration Service is not a valid AWS service name (the correct service is AWS Database Migration Service, DMS), and AWS Glue is an ETL service for data transformation, not designed for live database migration with minimal downtime and CDC. Option C is wrong because AWS Snowball Edge is an offline data transfer device for moving large datasets physically, which introduces significant downtime and cannot support ongoing replication or real-time change capture during the migration.

Full explanation →
941
MCQmedium

A company uses Amazon ElastiCache for Redis as a caching layer for its e-commerce application. Recently, the cache hit ratio has dropped significantly, causing increased database load. The operations team needs to identify which cache keys are being evicted. What should they do?

A.Monitor the 'Evictions' metric in Amazon CloudWatch for the ElastiCache cluster.
B.Check the ElastiCache event history for eviction events.
C.Enable the 'INFO' command output to be logged to CloudWatch Logs.
D.Enable the Redis slow-log to capture eviction commands.
AnswerA

CloudWatch provides the evictions metric which tracks the number of evicted keys.

Why this answer

The 'Evictions' metric in Amazon CloudWatch directly reports the number of keys evicted from the ElastiCache for Redis cluster due to memory pressure. A drop in cache hit ratio often correlates with increased evictions, and monitoring this metric allows the operations team to identify the rate at which keys are being removed. This is the standard, built-in way to observe eviction activity without additional configuration.

Exam trap

The trap here is that candidates may confuse event history (cluster-level events) with data-level operations (key evictions), or assume that logging the INFO command or slow-log would capture evictions, when in fact evictions are not commands and are best monitored via CloudWatch metrics.

How to eliminate wrong answers

Option B is wrong because ElastiCache event history records cluster-level events (e.g., node replacements, scaling, failover) but does not log individual key evictions; evictions are not emitted as events. Option C is wrong because enabling the Redis 'INFO' command output to CloudWatch Logs provides a snapshot of server statistics (including eviction counts) but is not a real-time metric and requires parsing logs; CloudWatch metrics like 'Evictions' are more direct and actionable. Option D is wrong because the Redis slow-log captures commands that exceed a specified execution time threshold, not eviction events; evictions are not commands but automatic memory management actions, so they never appear in the slow-log.

Full explanation →
942
Multi-Selecteasy

A company is migrating a 200 GB MySQL database to Amazon Aurora MySQL. The migration must be completed within a 1-hour downtime window. Which TWO methods can achieve this?

Select 2 answers
A.Use AWS SCT to convert the schema and then perform a data load.
B.Use AWS DMS with full load only.
C.Use Percona XtraBackup to create a physical backup, upload to S3, and restore to Aurora.
D.Use mysqldump with parallel threads and import using mysql command.
E.Create an RDS Read Replica of the on-premises database and promote it.
AnswersC, D

XtraBackup is fast and Aurora supports restoring from it via S3.

Why this answer

Percona XtraBackup creates a physical backup of the MySQL data files, which can be uploaded to Amazon S3 and then restored directly into an Aurora MySQL cluster. This method is significantly faster than logical backups for large databases (200 GB) because it bypasses SQL parsing and row-by-row insertion, making it feasible within a 1-hour downtime window.

Exam trap

The trap here is that candidates often assume AWS DMS is always the fastest migration method, but for large databases with a strict downtime window, physical backups (XtraBackup) and parallel logical dumps (mysqldump) can be more efficient because they avoid the overhead of CDC setup and can be tuned for maximum throughput.

Full explanation →
943
Multi-Selecthard

Which THREE factors should be considered when choosing between Amazon DynamoDB and Amazon RDS for a new application? (Choose three.)

Select 3 answers
A.Need for ACID transactions across multiple rows
B.Need for complex join queries across multiple tables
C.Need for automatic failover in multiple AWS Regions
D.Requirement for flexible schema with document data
E.Data size exceeding 10 TB
AnswersA, B, D

RDS supports full ACID; DynamoDB supports transactional APIs but limited.

Why this answer

Amazon RDS supports ACID transactions across multiple rows using traditional SQL databases like MySQL or PostgreSQL, which is essential for applications requiring strict consistency (e.g., financial systems). Amazon DynamoDB, while supporting ACID transactions via the TransactGetItems and TransactWriteItems APIs, is optimized for single-item or limited multi-item operations and may not perform as well for complex multi-row transactional workloads. The need for ACID across multiple rows is a key differentiator favoring RDS.

Exam trap

The trap here is that candidates assume automatic failover across multiple Regions is unique to one service, but both DynamoDB (global tables) and RDS (cross-Region read replicas with manual promotion) can achieve this, making it a non-differentiating factor.

Full explanation →
944
Multi-Selectmedium

A company is migrating a 200 GB Oracle database to Amazon RDS for Oracle. Which TWO steps should be taken to ensure a successful migration? (Choose two.)

Select 2 answers
A.Enable TDE encryption on the source database before migration.
B.Use AWS DMS only for heterogeneous migrations; use Oracle Data Pump instead.
C.Use AWS DMS with full load and ongoing replication.
D.Export the database using expdp and import using impdp.
E.Use AWS Schema Conversion Tool (SCT) to assess and convert the schema.
AnswersC, E

Minimizes downtime by replicating ongoing changes.

Why this answer

AWS DMS supports both homogeneous and heterogeneous migrations, and for a 200 GB Oracle database, using full load plus ongoing replication minimizes downtime by continuously applying changes from the source to the target RDS instance. This approach is ideal for migrating large databases with minimal disruption.

Exam trap

The trap here is that candidates often assume AWS DMS is only for heterogeneous migrations (e.g., Oracle to Aurora) and overlook its homogeneous capabilities, leading them to choose Oracle Data Pump (Option D) or incorrectly dismiss DMS (Option B).

Full explanation →
945
Multi-Selecteasy

Which TWO methods can be used to securely connect to an Amazon RDS for PostgreSQL DB instance from an EC2 instance in the same VPC? (Select TWO.)

Select 2 answers
A.Use IAM database authentication.
B.Configure a security group that allows all traffic from the EC2 instance.
C.Use a bastion host to proxy the connection.
D.Connect using SSL/TLS.
E.Set up a VPC peering connection between the EC2 VPC and the RDS VPC.
AnswersA, D

IAM authentication provides secure authentication.

Why this answer

Using SSL/TLS encrypts the connection. Using IAM database authentication provides a secure authentication mechanism. A security group allows traffic but does not encrypt.

A VPC peering connection does not encrypt. A bastion host may add encryption but is not a method itself.

Full explanation →
946
Multi-Selecteasy

Which THREE are factors to consider when choosing between Amazon RDS and Amazon DynamoDB? (Select THREE.)

Select 3 answers
A.Serverless capacity management
B.ACID transaction support across multiple tables
C.Need for complex joins and relationships
D.Encryption at rest requirements
E.Need for in-memory caching
AnswersA, B, C

DynamoDB is serverless; RDS requires provisioning.

Why this answer

Amazon RDS requires manual or auto-scaling of compute and storage capacity, while DynamoDB offers serverless capacity management with on-demand mode that automatically scales throughput based on traffic. This is a key differentiator when deciding between provisioned capacity (RDS) and fully managed, pay-per-request scaling (DynamoDB).

Exam trap

The trap here is that candidates may think encryption at rest or in-memory caching are exclusive to one service, but AWS offers these features across both RDS and DynamoDB, making them irrelevant for choosing between the two.

Full explanation →
947
MCQeasy

A company is designing a database for an e-commerce application that requires high availability and automatic failover. The application performs mainly read-heavy workloads with occasional write spikes during flash sales. Which AWS database service is most suitable for this workload?

A.Amazon DynamoDB with global tables
B.Amazon Aurora MySQL
C.Amazon ElastiCache for Redis
D.Amazon RDS for MySQL with Multi-AZ
AnswerB

High availability and read replicas for read-heavy workloads.

Why this answer

Amazon Aurora MySQL is the most suitable choice because it is designed for high availability with automatic failover (typically under 30 seconds) and provides up to 15 low-latency read replicas that can handle read-heavy workloads. During write spikes like flash sales, Aurora's distributed storage subsystem automatically scales I/O capacity without manual intervention, and its Multi-AZ deployment ensures continuous availability even if the primary instance fails.

Exam trap

The trap here is that candidates often confuse Multi-AZ with high availability and choose RDS MySQL Multi-AZ (Option D), overlooking that Aurora provides the same failover capability with superior read scaling and write performance for bursty workloads.

How to eliminate wrong answers

Option A is wrong because Amazon DynamoDB with global tables is optimized for multi-region active-active workloads and eventual consistency, not for a single-region read-heavy relational workload with occasional write spikes; it lacks the native SQL join capabilities and relational schema that an e-commerce application typically requires. Option C is wrong because Amazon ElastiCache for Redis is an in-memory caching layer, not a primary database; it cannot serve as the durable, ACID-compliant database for transactional data like orders and inventory. Option D is wrong because Amazon RDS for MySQL with Multi-AZ provides automatic failover but only supports up to 5 read replicas (with asynchronous replication) and does not offer the same write throughput scalability or storage auto-scaling as Aurora, making it less suitable for write spikes during flash sales.

Full explanation →
948
MCQmedium

A company is using Amazon Redshift for data warehousing. The database administrator needs to identify which queries are consuming the most resources. Which system view should be queried?

A.SVV_TABLES
B.STV_RECENTS
C.STL_LOAD_ERRORS
D.STL_DDLTEXT
AnswerB

STV_RECENTS shows active and recent queries, including their resource usage.

Why this answer

The STV_RECENTS system view in Amazon Redshift provides a list of currently running and recently completed queries, including their process IDs, user names, and execution status. This makes it the correct choice for identifying which queries are consuming the most resources at the moment, as it directly reflects active and recent workload.

Exam trap

The trap here is that candidates confuse system views for metadata (SVV_TABLES) or error logging (STL_LOAD_ERRORS) with those that track query execution and resource usage, leading them to overlook STV_RECENTS as the direct source for active query monitoring.

How to eliminate wrong answers

Option A is wrong because SVV_TABLES is a system view that lists tables and their metadata (like schema, table name, and table type), not query resource consumption. Option C is wrong because STL_LOAD_ERRORS logs errors that occur during COPY or INSERT operations, focusing on data load failures rather than general query resource usage. Option D is wrong because STL_DDLTEXT captures the text of DDL statements (e.g., CREATE, ALTER) that have been executed, not runtime resource consumption of queries.

Full explanation →
949
MCQmedium

A company needs to store and query JSON documents that vary in structure. The application requires flexible schema, automatic indexing, and the ability to run complex aggregation pipelines. Which AWS database service should be used?

A.Amazon DynamoDB
B.Amazon DocumentDB (with MongoDB compatibility)
C.Amazon ElastiCache for Redis
D.Amazon RDS for PostgreSQL
AnswerB

DocumentDB supports flexible schema, automatic indexing, and aggregation pipelines.

Why this answer

Amazon DocumentDB (with MongoDB compatibility) is the correct choice because it is purpose-built for storing and querying JSON-like documents with flexible schemas, automatically indexes fields, and supports MongoDB's aggregation pipeline for complex data transformations. This aligns directly with the requirements for varying document structures, automatic indexing, and aggregation capabilities.

Exam trap

The trap here is that candidates often confuse DynamoDB's flexible schema and JSON support with full aggregation pipeline capabilities, overlooking that DynamoDB lacks complex multi-stage aggregations like MongoDB's $lookup or $unwind, which are essential for the stated requirement.

How to eliminate wrong answers

Option A is wrong because Amazon DynamoDB is a key-value and document database that does not support complex aggregation pipelines like MongoDB's $lookup or $group stages; it uses limited query patterns and requires manual secondary index management. Option C is wrong because Amazon ElastiCache for Redis is an in-memory cache, not a persistent document store, and lacks support for aggregation pipelines or automatic indexing of JSON documents. Option D is wrong because Amazon RDS for PostgreSQL requires a predefined schema and does not natively support automatic indexing of varying JSON structures or MongoDB-style aggregation pipelines, though it can store JSON via JSONB, it lacks the flexible schema and pipeline capabilities needed.

Full explanation →
950
MCQeasy

A company is migrating an on-premises MongoDB database to AWS. Which AWS database service is most compatible and requires minimal application changes?

A.Amazon DynamoDB.
B.Amazon DocumentDB (with MongoDB compatibility).
C.Amazon Neptune.
D.Amazon RDS for MySQL.
AnswerB

MongoDB-compatible document database.

Why this answer

Amazon DocumentDB (with MongoDB compatibility) is the most compatible AWS database service for migrating an on-premises MongoDB database because it is purpose-built to emulate the MongoDB wire protocol and data model, allowing existing MongoDB drivers and tools to connect with minimal or no application code changes. This makes it the ideal choice for a lift-and-shift migration that preserves the document-oriented structure and query patterns of MongoDB.

Exam trap

The trap here is that candidates may assume DynamoDB is a suitable document database for MongoDB migration because both are NoSQL, overlooking the critical fact that DynamoDB uses a completely different API and data model, which would require a full application rewrite rather than minimal changes.

How to eliminate wrong answers

Option A is wrong because Amazon DynamoDB is a key-value and document database that uses a proprietary API and data model, requiring significant application rewrites to adapt from MongoDB's query language and indexing. Option C is wrong because Amazon Neptune is a graph database designed for highly connected data (e.g., social networks, fraud detection) and does not support MongoDB's document model or wire protocol, making it incompatible for a direct migration. Option D is wrong because Amazon RDS for MySQL is a relational database that enforces a fixed schema and SQL-based access, which would require extensive application changes to map MongoDB's flexible documents to tables and rows.

Full explanation →
951
Multi-Selectmedium

A company is migrating a 2 TB Oracle database to Amazon Aurora PostgreSQL. The migration requires ongoing replication for minimal downtime. Which THREE AWS services should be used?

Select 3 answers
A.AWS Database Migration Service (AWS DMS)
B.Amazon S3
C.AWS DataSync
D.AWS Schema Conversion Tool (AWS SCT)
E.AWS Lambda
AnswersA, B, D

DMS is the primary service for data migration and ongoing replication.

Why this answer

AWS DMS is the correct choice because it supports ongoing replication (change data capture) from Oracle to Amazon Aurora PostgreSQL, enabling minimal downtime migration. It can continuously replicate changes from the source Oracle database to the target Aurora PostgreSQL instance until the cutover, allowing the source to remain operational during the migration.

Exam trap

The trap here is that candidates may think AWS DataSync is suitable for database replication, but it only handles file-level transfers and cannot perform ongoing change data capture for databases.

Full explanation →
952
MCQhard

A company uses Amazon RDS for MySQL with Multi-AZ. During a recent failover test, the database experienced a 5-minute write outage. The application can tolerate up to 1 minute of downtime. Which solution should be used to reduce the failover time?

A.Migrate to Amazon Aurora MySQL.
B.Use Amazon RDS Proxy between the application and the database.
C.Enable automatic failover on the Multi-AZ deployment.
D.Decrease the DNS TTL on the RDS endpoint.
AnswerA

Aurora failover is typically under 30 seconds.

Why this answer

Amazon Aurora MySQL is designed to reduce failover time significantly compared to standard RDS for MySQL Multi-AZ. Aurora typically completes failover in under 60 seconds by using a shared distributed storage volume across multiple Availability Zones, eliminating the need to replay redo logs on the standby. This directly meets the application's requirement of tolerating up to 1 minute of downtime.

Exam trap

The trap here is that candidates assume RDS Proxy or DNS TTL adjustments can reduce failover time, when in reality the failover delay is dominated by the database engine's crash recovery process, which only Aurora's distributed storage architecture can mitigate.

How to eliminate wrong answers

Option B is wrong because Amazon RDS Proxy reduces connection overhead and improves connection pooling, but it does not reduce the time required for the database instance itself to fail over; the underlying Multi-AZ failover duration remains unchanged. Option C is wrong because automatic failover is already enabled by default on a Multi-AZ deployment, so enabling it again has no effect on failover time. Option D is wrong because decreasing the DNS TTL on the RDS endpoint reduces client-side DNS caching delays, but the primary bottleneck is the database failover process itself, which takes several minutes due to crash recovery and redo log replay on the standby.

Full explanation →
953
Multi-Selectmedium

Which THREE of the following are best practices for managing Amazon DynamoDB tables with provisioned throughput?

Select 3 answers
A.Create a global secondary index on every attribute to support any query pattern.
B.Split hot partitions manually to distribute write traffic.
C.Use a composite key design with a sort key to enable efficient querying.
D.Use DynamoDB Auto Scaling to adjust read/write capacity based on traffic.
E.Enable DynamoDB Accelerator (DAX) to improve read performance for frequently accessed items.
AnswersC, D, E

Composite keys allow efficient range queries and data organization.

Why this answer

Options C, D, and E are correct. Option A is incorrect because creating a global secondary index on every attribute incurs additional costs and storage, and not every query pattern requires an index; GSIs should be designed based on actual access patterns. Option B is incorrect because DynamoDB automatically manages partitions; manual partition splitting is not supported.

Option C is correct because a composite key design (partition key and sort key) enables efficient querying using the sort key for filtering and sorting. Option D is correct because DynamoDB Auto Scaling automatically adjusts read/write capacity based on actual traffic, helping to maintain performance without manual intervention. Option E is correct because DynamoDB Accelerator (DAX) provides in-memory caching for frequently accessed items, reducing read latency and improving performance.

Full explanation →
954
MCQhard

A company is migrating an on-premises Microsoft SQL Server database to Amazon RDS for SQL Server. The database uses SQL Server Agent jobs, custom CLR assemblies, and cross-database queries. Which of the following will require modification before migration?

A.Cross-database queries
B.Custom CLR assemblies
C.Stored procedures that use dynamic SQL
D.SQL Server Agent jobs
AnswerD

SQL Server Agent is not available in RDS; jobs require alternative solutions.

Why this answer

SQL Server Agent jobs are not supported in Amazon RDS for SQL Server because RDS is a managed service that does not provide access to the underlying operating system or the SQL Server Agent service. To migrate job scheduling, you must use alternatives such as AWS Database Migration Service (DMS) tasks, AWS Lambda, or Amazon RDS for SQL Server native scheduling via stored procedures and Windows Task Scheduler on an EC2 instance.

Exam trap

The trap here is that candidates assume SQL Server Agent jobs are fully supported in RDS because RDS for SQL Server includes the SQL Server engine, but they overlook that Agent is a separate Windows service that RDS does not expose, requiring a workaround for job scheduling.

How to eliminate wrong answers

Option A is wrong because cross-database queries are supported in Amazon RDS for SQL Server as long as the databases are within the same RDS instance; no modification is required for queries that reference tables in other databases on the same instance. Option B is wrong because custom CLR assemblies are supported in Amazon RDS for SQL Server, provided they are signed with a certificate or asymmetric key and the CLR integration is enabled via the rds_custom_clr option group setting. Option C is wrong because stored procedures that use dynamic SQL are fully supported in Amazon RDS for SQL Server, as dynamic SQL execution is a core T-SQL feature that does not require any special configuration or modification.

Full explanation →
955
MCQmedium

A company is using Amazon Redshift for data warehousing. They need to ensure that data is encrypted at rest using a customer-managed AWS KMS key. The cluster is currently unencrypted. What is the simplest way to enable encryption?

A.Create a new Redshift cluster with encryption enabled and migrate the data.
B.Enable encryption directly on the existing cluster using AWS CLI.
C.Create a snapshot of the existing cluster and restore it to a new encrypted cluster.
D.Modify the existing cluster and enable encryption using the Redshift console.
AnswerA

This is the simplest method; create a new encrypted cluster and copy the data.

Why this answer

You cannot enable encryption on an existing Amazon Redshift cluster. The simplest approach is to create a new cluster with encryption enabled using a customer-managed AWS KMS key, then migrate the data from the old cluster to the new one using tools such as UNLOAD/COPY or AWS DMS. Option C (snapshot and restore) is also valid, but it is not the simplest because it requires creating a snapshot and then restoring it to a new encrypted cluster, which involves extra steps.

Therefore, option A is correct.

Full explanation →
956
MCQmedium

A company is using DynamoDB with a VPC endpoint. They want to restrict access to only requests originating from their VPC. Which policy condition should be used?

A.'aws:SourceVpce': 'vpce-12345678'
B.'aws:VpcSourceIp': '10.0.0.0/16'
C.'s3:x-amz-server-side-encryption': 'AES256'
D.'aws:SourceVpc': 'vpc-12345678'
AnswerA

This condition ensures requests come only from the specified VPC endpoint.

Why this answer

The 'aws:SourceVpce' condition key allows you to restrict access to requests originating from a specific VPC endpoint (interface endpoint) in your VPC. This ensures that only traffic coming through that VPC endpoint can access the DynamoDB table, providing a network-level security boundary. The condition must reference the exact VPC endpoint ID (e.g., 'vpce-12345678') to enforce this restriction.

Exam trap

The trap here is that candidates often confuse 'aws:SourceVpc' (which restricts by VPC ID but is not supported for VPC endpoint policies) with 'aws:SourceVpce' (the correct key for endpoint-level restrictions), leading them to pick Option D instead of A.

How to eliminate wrong answers

Option B is wrong because 'aws:VpcSourceIp' is not a valid AWS condition key; the correct key for restricting by source IP is 'aws:SourceIp', but that would not restrict to VPC-originated traffic specifically. Option C is wrong because 's3:x-amz-server-side-encryption' is an S3-specific condition key for encryption headers, irrelevant to DynamoDB VPC endpoint access control. Option D is wrong because 'aws:SourceVpc' restricts based on the VPC ID, but it does not work for VPC endpoint policies; the correct key for VPC endpoint restrictions is 'aws:SourceVpce' (the endpoint ID), not the VPC ID.

Full explanation →
957
MCQeasy

A company wants to deploy a globally distributed application with a DynamoDB table that uses optimistic locking. Which DynamoDB feature should be used to implement this?

A.DynamoDB Conditional Writes
B.DynamoDB Streams
C.DynamoDB Global Tables
D.DynamoDB Transactions
AnswerA

Conditional writes implement optimistic locking.

Why this answer

Optimistic locking in DynamoDB is implemented using conditional writes, specifically the `ConditionExpression` parameter in `PutItem`, `UpdateItem`, or `DeleteItem` operations. By checking that a version attribute (e.g., `version = :expected_version`) matches the client's known value before writing, the application can detect and reject concurrent modifications, ensuring data consistency without locking the entire table.

Exam trap

The trap here is that candidates confuse DynamoDB Transactions (which provide atomicity and isolation) with the concurrency control pattern of optimistic locking, but optimistic locking is specifically implemented using conditional writes, not transactions.

How to eliminate wrong answers

Option B is wrong because DynamoDB Streams capture a time-ordered sequence of item-level changes in a table, but they do not provide any mechanism to prevent concurrent writes or implement locking. Option C is wrong because DynamoDB Global Tables provide multi-region replication and eventual consistency, but they do not offer built-in conflict resolution for optimistic locking; they rely on last-writer-wins or custom conflict resolution using conditional writes. Option D is wrong because DynamoDB Transactions provide ACID guarantees across multiple items, but they are designed for atomic, isolated operations, not for implementing optimistic locking, which is a concurrency control pattern that uses conditional writes to detect conflicts.

Full explanation →
958
MCQmedium

An e-commerce application uses Amazon DynamoDB as its primary database. The table stores order data with a partition key of 'OrderID' and a sort key of 'OrderDate'. The application frequently queries orders by customer ID (which is not a key attribute). What design change would improve query performance?

A.Enable DynamoDB Streams and export to Amazon Elasticsearch Service
B.Use DynamoDB Accelerator (DAX) to cache queries
C.Create a Global Secondary Index on CustomerID
D.Create a Local Secondary Index on CustomerID
AnswerC

GSI allows querying by CustomerID efficiently.

Why this answer

Creating a Global Secondary Index (GSI) on CustomerID allows efficient querying by that attribute without scanning the entire table. A GSI has its own partition and sort keys, enabling fast lookups on non-key attributes. This directly addresses the performance issue of frequent queries by CustomerID, which otherwise would require a full table scan.

Exam trap

The trap here is that candidates often confuse Local Secondary Indexes (LSIs) with Global Secondary Indexes (GSIs), assuming an LSI can be used to query by a non-key attribute without the partition key, but LSIs require the same partition key as the base table and cannot be added after table creation.

How to eliminate wrong answers

Option A is wrong because DynamoDB Streams and exporting to Amazon Elasticsearch Service are designed for search and analytics, not for improving point-query performance on a specific attribute like CustomerID; this adds complexity and latency without solving the core access pattern. Option B is wrong because DynamoDB Accelerator (DAX) is an in-memory cache that speeds up repeated queries on existing keys, but it does not enable querying by a non-key attribute like CustomerID; it cannot create new access patterns. Option D is wrong because a Local Secondary Index (LSI) can only be created at table creation time and must use the same partition key as the base table (OrderID), so it cannot index by CustomerID alone; it would still require the partition key to be specified in queries.

Full explanation →
959
MCQeasy

A company is deploying a new application that uses Amazon RDS for PostgreSQL. The database must be highly available and fault-tolerant. Which deployment option meets these requirements?

A.Deploy the RDS instance in a single Availability Zone
B.Deploy the RDS instance across multiple AWS Regions
C.Deploy the RDS instance with Multi-AZ configuration
D.Deploy the RDS instance with multiple read replicas
AnswerC

Multi-AZ provides automatic failover for high availability.

Why this answer

Amazon RDS Multi-AZ deployment automatically provisions and maintains a synchronous standby replica in a different Availability Zone, providing automatic failover in the event of an AZ outage or primary instance failure. This configuration meets the requirements for high availability and fault tolerance by ensuring the database remains accessible with minimal downtime, as the standby replica is kept in sync synchronously and DNS is updated to point to the standby upon failover.

Exam trap

The trap here is that candidates often confuse read replicas with Multi-AZ, mistakenly believing that multiple read replicas provide automatic failover and fault tolerance, when in fact they only serve read traffic and require manual promotion for failover, which is not automatic and can result in data loss.

How to eliminate wrong answers

Option A is wrong because deploying in a single Availability Zone creates a single point of failure; if that AZ experiences an outage, the database becomes unavailable, offering no fault tolerance. Option B is wrong because deploying across multiple AWS Regions is primarily for disaster recovery and global read scaling, not for high availability within a single region; it introduces cross-Region replication latency and does not provide automatic failover for the primary database instance. Option D is wrong because multiple read replicas are designed to offload read traffic and improve read scalability, not to provide automatic failover or fault tolerance for the primary database; read replicas are asynchronous and do not replace the need for a synchronous standby for high availability.

Full explanation →
960
MCQeasy

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

A.Reserved capacity
B.On-Demand capacity mode
C.Provisioned capacity without auto scaling
D.Provisioned capacity with auto scaling
AnswerB

Automatically handles spikes and charges per request, minimizing cost during low traffic.

Why this answer

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

Exam trap

AWS often tests the misconception that Provisioned capacity with auto scaling is always the most cost-effective option, but for unpredictable, spiky workloads like gaming events, On-Demand avoids the fixed costs of minimum provisioned capacity and the risk of throttling during rapid traffic surges.

How to eliminate wrong answers

Option A is wrong because Reserved capacity is not a DynamoDB capacity mode; it is a pricing model for EC2 and RDS, not applicable to DynamoDB. Option C is wrong because Provisioned capacity without auto scaling would require manual adjustments to handle event-driven spikes, risking throttling or over-provisioning costs. Option D is wrong because Provisioned capacity with auto scaling still requires setting a minimum provisioned capacity, which incurs costs even during low-usage periods, making it less cost-effective than On-Demand for unpredictable, spiky workloads.

Full explanation →
961
MCQmedium

A company is designing a database for an IoT application that ingests millions of sensor readings per second. Each reading is a small JSON document (less than 1 KB) and must be stored with low latency. Queries are primarily by device ID and timestamp range. The team expects to rarely update or delete old data. Which AWS database solution is MOST cost-effective and performant?

A.Amazon S3 with a partition prefix of device_id/timestamp/
B.Amazon Redshift with distribution key on device_id
C.Amazon DynamoDB with a composite primary key (device_id, timestamp)
D.Amazon RDS for MySQL with multiple read replicas
AnswerC

DynamoDB provides low-latency, high-throughput ingestion and efficient querying by device and time.

Why this answer

Amazon DynamoDB with a composite primary key (device_id, timestamp) is the most cost-effective and performant solution because it provides single-digit millisecond latency for point lookups and range queries, scales horizontally to handle millions of writes per second, and its on-demand or auto-scaling capacity model avoids over-provisioning. The access pattern of querying by device ID and timestamp range maps directly to DynamoDB's partition and sort key design, enabling efficient use of the Query API without scanning.

Exam trap

The DBS-C01 exam often tests the misconception that S3 is suitable for low-latency, high-write IoT ingestion, but the trap here is that S3's eventual consistency and higher per-request latency make it inappropriate for real-time sensor data storage, whereas DynamoDB's design for exactly this pattern is the correct choice.

How to eliminate wrong answers

Option A is wrong because Amazon S3 with a prefix of device_id/timestamp/ incurs higher latency for individual record retrieval (typically tens to hundreds of milliseconds) and does not support real-time queries without additional services like Athena or S3 Select, which add cost and delay. Option B is wrong because Amazon Redshift is a columnar data warehouse optimized for complex analytical queries on large datasets, not for high-frequency, low-latency point lookups or writes; its minimum latency is in the seconds range and it is not designed for millions of writes per second. Option D is wrong because Amazon RDS for MySQL with read replicas cannot handle millions of writes per second due to single-writer node limitations, and its relational overhead (schema, indexing) adds latency for simple JSON storage; read replicas only help read scaling, not write throughput.

Full explanation →
962
Matchingmedium

Match each backup/restore concept to its AWS database feature.

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

Concepts
Matches

Daily snapshot and transaction log backups enabled by default

User-initiated snapshot stored until explicitly deleted

Restore to any second within the backup retention period

Copy snapshots to another AWS region for disaster recovery

Rewind an Aurora DB cluster to a specific time without restoring

Why these pairings

Correct matches: Automated Backup corresponds to RDS automated backups (daily, retention period), Manual Snapshot corresponds to user-initiated RDS snapshots (stored in S3), and PITR corresponds to DynamoDB's ability to restore to any point in the last 35 days. Common confusions involve swapping the definitions of automated and manual backups.

Full explanation →
963
MCQeasy

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

A.Amazon CloudWatch Contributor Insights for DynamoDB
B.Amazon Inspector
C.AWS Config
D.AWS CloudTrail logs
AnswerA

Contributor Insights analyzes access patterns and identifies throttled partition keys.

Why this answer

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

Exam trap

The trap here is that candidates often confuse CloudTrail (which logs all API calls) with Contributor Insights, assuming CloudTrail can provide per-key throttling data, but CloudTrail lacks the aggregation and top-N analysis needed to identify specific hot partition keys.

How to eliminate wrong answers

Option B (Amazon Inspector) is wrong because it is a vulnerability management service that assesses network and application security, not a tool for analyzing DynamoDB partition key access patterns or throttling. Option C (AWS Config) is wrong because it evaluates resource configurations and compliance rules, not real-time operational metrics like request throttling per partition key. Option D (AWS CloudTrail logs) is wrong because it records API calls for auditing and governance, but does not provide per-partition-key throttling details or aggregated access patterns needed to identify hot partitions.

Full explanation →
964
MCQmedium

A company is using Amazon RDS for PostgreSQL with a read replica. The security team wants to ensure that data in transit between the primary DB instance and the read replica is encrypted. What should be done?

A.Set up a VPN connection between the primary and the replica.
B.Configure the read replica to use a different KMS key.
C.Enable SSL/TLS on the read replica and configure the primary to use it.
D.Enable encryption at rest on the primary DB instance using AWS KMS.
AnswerD

Encrypted replication is automatically enabled when the primary is encrypted.

Why this answer

Enabling encryption at rest on the primary DB instance using AWS KMS automatically encrypts the replication traffic between the primary and the read replica. Options A, B, and C are incorrect: A VPN is not required as RDS handles encryption in transit for replication when the primary is encrypted; using a different KMS key for the replica does not affect replication encryption; SSL/TLS is for client connections, not for replication traffic.

Full explanation →
965
MCQmedium

A developer is designing a DynamoDB table for an order management system using the above CloudFormation template. The application needs to query orders by CustomerID. The current design has a GSI on CustomerID. However, the developer notices that the GSI has low write throughput and often throttles. What is the most cost-effective way to improve write throughput on the GSI?

A.Change the ProjectionType of the GSI to KEYS_ONLY.
B.Use Amazon SQS to buffer writes to the table.
C.Increase the WriteCapacityUnits of the table.
D.Increase the WriteCapacityUnits of the GSI from 5 to a higher value.
AnswerD

GSI has its own provisioned throughput; increasing it alleviates throttling.

Why this answer

The GSI's write throughput is independent of the base table's write capacity. Increasing the GSI's WriteCapacityUnits directly addresses the throttling on the GSI without affecting the base table's cost or performance. This is the most cost-effective approach because it only scales the specific resource that is throttling, rather than over-provisioning the entire table or adding unnecessary infrastructure.

Exam trap

The trap here is that candidates mistakenly believe that increasing the base table's write capacity will automatically increase the GSI's write throughput, but DynamoDB treats GSI capacity as a separate resource that must be provisioned independently.

How to eliminate wrong answers

Option A is wrong because changing the ProjectionType to KEYS_ONLY reduces the amount of data written to the GSI, but it does not increase the write throughput capacity; throttling occurs when the provisioned write capacity is exceeded, not due to projection size. Option B is wrong because using Amazon SQS to buffer writes does not improve the GSI's write throughput; it only decouples the write path and may mask the throttling issue, but the GSI still throttles when the buffer drains and writes hit the GSI at the same rate. Option C is wrong because increasing the base table's WriteCapacityUnits does not affect the GSI's write throughput; GSI writes are consumed from the GSI's own provisioned capacity, not the base table's.

Full explanation →
966
MCQeasy

Refer to the exhibit. A developer is trying to query the ProductCatalog table using the 'Id' attribute. The query returns no results even though the developer knows data was inserted. What is the MOST likely cause?

A.The table contains no items
B.The provisioned throughput is exceeded
C.The attribute definition is missing the 'Id' attribute
D.The table is not in ACTIVE status
AnswerA

ItemCount is 0, so the table is empty.

Why this answer

The table shows ItemCount: 0, indicating no items exist. Even though data was inserted, if the write operation failed or was directed to a different table, the query returns no results. Option B is incorrect because provisioned throughput issues would cause throttling errors, not zero items.

Option C is incorrect because the attribute definition exists (Id is defined). Option D is incorrect because the table status is ACTIVE as shown.

Full explanation →
967
Multi-Selectmedium

A company is migrating an on-premises MongoDB database to Amazon DocumentDB. The current MongoDB workload uses aggregation pipelines with the $lookup stage and geospatial queries. The migration tool reports that some aggregation stages are not supported. Which THREE actions should the company take to address the incompatibilities?

Select 3 answers
A.Replace $lookup with $geoWithin for location-based queries.
B.Use $geoNear for geospatial queries as it is supported in DocumentDB.
C.Convert $graphLookup to use recursive queries in DocumentDB.
D.Denormalize the data to avoid $lookup by embedding related documents.
E.Rewrite $lookup stages as application-side joins or use references.
AnswersB, D, E

DocumentDB supports $geoNear.

Why this answer

DocumentDB supports the $geoNear aggregation stage for geospatial queries, making option B a valid action to address incompatibilities. The migration tool flagged unsupported stages, and $geoNear is explicitly supported in DocumentDB for location-based queries, whereas $geoWithin is not available in aggregation pipelines.

Exam trap

The trap here is that candidates may confuse $geoWithin (a query operator) with $geoNear (an aggregation stage), or assume that $graphLookup can be emulated with recursive queries in DocumentDB, which is not supported.

Full explanation →
968
MCQeasy

A company wants to ensure that an Amazon RDS for MySQL database is automatically backed up daily and backups are encrypted. What should they do?

A.Take manual snapshots daily and enable encryption on the snapshot copy.
B.Use Amazon CloudWatch Events to trigger a Lambda function that exports the database to S3 with encryption.
C.Enable automated backups and encryption at rest on the RDS instance.
D.Configure AWS Backup to back up the RDS instance to an S3 bucket with default encryption.
AnswerC

Automated backups are encrypted if encryption at rest is enabled.

Why this answer

Automated backups with encryption at rest ensure that backups are automatically taken daily and encrypted. Option C is correct because it enables both features: automated backups and encryption at rest. Option A is incorrect because manual snapshots are not automatic; they require manual intervention and enabling encryption on the snapshot copy is an extra step.

Option B is incorrect because using CloudWatch Events and Lambda to export to S3 is not the standard automated backup mechanism for RDS; RDS automated backups are handled natively and do not require custom Lambda functions. Option D is incorrect because AWS Backup is not the default automated backup service for RDS; RDS has its own automated backup feature. Additionally, encryption for RDS backups is managed by AWS KMS, not S3 default encryption.

Full explanation →
969
Multi-Selecthard

Which THREE steps should be taken to troubleshoot an Amazon DynamoDB table that is experiencing high read latency?

Select 3 answers
A.Review the table's partition distribution using DynamoDB metrics.
B.Increase the write capacity of the table.
C.Monitor the ConsumedReadCapacityUnits metric.
D.Disable auto scaling to prevent unexpected capacity changes.
E.Check the ThrottledReadEvents metric to see if reads are being throttled.
AnswersA, C, E

Uneven partition distribution can cause hot partitions and high latency.

Why this answer

High read latency in DynamoDB can result from uneven partition distribution, where a 'hot' partition receives more read requests than others, causing throttling or increased latency. By reviewing the table's partition distribution using CloudWatch metrics like `ConsumedReadCapacityUnits` per partition, you can identify skewed access patterns and address them with strategies like partition key redesign or adaptive capacity.

Exam trap

The trap here is that candidates may confuse write capacity adjustments with read performance fixes, or assume disabling auto scaling is a troubleshooting step, when in fact auto scaling is a best practice for maintaining consistent throughput.

Full explanation →
970
MCQmedium

A company has an Amazon RDS for PostgreSQL DB instance that needs to be accessed by an application running on an Amazon EC2 instance. Both resources are in the same VPC. The security team insists that all traffic between the application and the database be encrypted in transit. Which configuration ensures this?

A.Add a security group rule that allows traffic only from the EC2 instance's private IP.
B.Create an AWS Site-to-Site VPN connection between the EC2 instance and the RDS instance.
C.Enable SSL/TLS on the RDS instance and configure the application to connect using SSL.
D.Set up VPC peering between the EC2 instance's VPC and the RDS instance's VPC.
AnswerC

SSL/TLS encrypts the database connection.

Why this answer

Enabling SSL/TLS on the RDS for PostgreSQL DB instance and configuring the application to connect using SSL ensures encryption in transit. Option A is incorrect because security group rules control access (firewall), not encryption. Option B is incorrect because a Site-to-Site VPN is used for on-premises to VPC connectivity, not for same-VPC communication.

Option D is incorrect because VPC peering is for connecting separate VPCs, and both resources are already in the same VPC.

Full explanation →
971
MCQmedium

A company uses Amazon DynamoDB to store sensor data from IoT devices. The table has a partition key of DeviceID (String) and a sort key of Timestamp (Number). The table is configured with provisioned capacity of 1000 read capacity units (RCUs) and 500 write capacity units (WCUs). Auto Scaling is enabled with target utilization of 70% and is working correctly. Recently, a new batch job was introduced that performs Scan operations on the entire table every hour. The Scan consumes many read capacity units and causes throttling of the sensor write requests. The team wants to minimize the impact on the write-heavy sensor ingestion. Which approach is BEST?

A.Increase the provisioned write capacity to 2000 WCUs to handle bursts.
B.Schedule the Scan to run during off-peak hours when sensor writes are lower.
C.Enable DAX (DynamoDB Accelerator) to cache Scan results and reduce read consumption on the table.
D.Switch the table to on-demand capacity mode to eliminate throttling.
AnswerD

On-demand capacity mode automatically scales to handle any traffic burst, eliminating throttling for both reads and writes. It is the best choice to prevent scan-induced throttling of write requests.

Why this answer

Switching to on-demand capacity mode eliminates throttling by automatically scaling read/write capacity to meet traffic demands. Since the scan operation runs hourly and consumes significant RCUs, on-demand mode will handle the burst without throttling write requests. Option C is incorrect because DAX caches individual items, not entire scan results; hourly scans would cause cache misses, providing no benefit.

Option A is incorrect because increasing write capacity does not address read throttling. Option B is incorrect because off-peak scheduling may not be feasible and does not guarantee elimination of throttling.

Full explanation →
972
MCQhard

A company has an Amazon RDS for PostgreSQL instance that is running out of storage. The database is 2 TB in size and growing at 10 GB per day. They need a solution that allows automatic storage scaling with minimal downtime. What should they do?

A.Enable Storage Auto Scaling
B.Purchase reserved instances for cost savings
C.Create a read replica and promote it
D.Take a manual snapshot and restore to a larger instance
AnswerA

Automatically scales storage with no downtime.

Why this answer

Amazon RDS Storage Auto Scaling automatically increases storage when free space drops below a threshold, with no downtime. For a 2 TB database growing at 10 GB/day, this feature dynamically adds storage in predefined increments (e.g., 5 GB, 10 GB, or 10% of current storage) up to the maximum limit, preventing manual intervention. This directly addresses the requirement for automatic scaling with minimal disruption.

Exam trap

The trap here is that candidates may confuse storage scaling with compute scaling or failover strategies, assuming that a read replica or snapshot restore is required for storage growth, when RDS Storage Auto Scaling handles it automatically and transparently.

How to eliminate wrong answers

Option B is wrong because purchasing reserved instances reduces compute costs but does not address storage exhaustion or provide automatic scaling. Option C is wrong because creating a read replica and promoting it requires manual failover, potential downtime, and does not automatically scale storage on the original instance. Option D is wrong because taking a manual snapshot and restoring to a larger instance involves significant downtime during the snapshot and restore process, and it is a manual, non-automated solution.

Full explanation →
973
MCQmedium

A company is running a MongoDB-compatible Amazon DocumentDB cluster. The application team reports that write operations are failing intermittently with a `WriteConcernError` indicating that the write concern could not be satisfied. The cluster has one primary and two replicas. What is the MOST likely cause of this issue?

A.One of the replicas is down or experiencing high replication lag
B.The cluster does not have enough replica instances to satisfy the write concern
C.The application is using an incorrect read preference
D.The primary instance is overloaded with read requests
AnswerA

If a replica is down, the write concern 'majority' cannot be satisfied because only the primary and one replica are available, but majority may require two replicas depending on configuration.

Why this answer

The WriteConcernError occurs because the write concern requires acknowledgment from a certain number of replicas. With a cluster of one primary and two replicas, if any replica is down or has high replication lag, the write concern requirement cannot be satisfied. Option A is correct because that is the most likely cause.

Option B is incorrect because the cluster has enough replicas for majority write concern if all are healthy. Option C is incorrect because read preference does not affect write operations. Option D is incorrect because the primary being overloaded with reads would not cause a WriteConcernError; it would affect performance but not the ability to satisfy write concern.

Full explanation →
974
Multi-Selectmedium

Which TWO of the following are required when migrating an on-premises Oracle database to Amazon RDS for Oracle using AWS DMS with change data capture (CDC)? (Select TWO.)

Select 2 answers
A.Use AWS SCT to assess schema compatibility.
B.Create a full backup of the source database.
C.Enable supplemental logging on the source Oracle database.
D.Set up Oracle Data Guard for replication.
E.Manually create target tables in RDS.
AnswersA, C

Required for schema conversion.

Why this answer

AWS SCT (Schema Conversion Tool) is required to assess and convert the source Oracle schema to a format compatible with Amazon RDS for Oracle, ensuring that data types, indexes, and other objects are correctly mapped before migration. This step is critical because even though both are Oracle databases, differences in version, parameter settings, and storage engines can cause schema incompatibilities that DMS cannot handle automatically.

Exam trap

The trap here is that candidates often assume a full backup (Option B) is required for DMS, but DMS performs its own full load and CDC independently, making the backup unnecessary, while they overlook the critical requirement of enabling supplemental logging (Option C) for CDC to function.

Full explanation →
975
MCQmedium

A company runs an analytics platform that queries billions of rows of sales data. Queries are complex and involve aggregations across multiple dimensions. The data is updated in bulk daily. Which service should be used as the primary data store?

A.Amazon Redshift
B.Amazon DynamoDB
C.Amazon Athena
D.Amazon RDS for MySQL
AnswerA

Amazon Redshift’s columnar storage and massively parallel processing (MPP) architecture directly satisfy the need for complex aggregations across billions of rows, as it scans only relevant columns and distributes query execution across multiple nodes. The daily bulk update constraint is met by Redshift’s efficient COPY command and vacuum/analyse operations, which optimise large-scale data loads without degrading analytical query performance.

Why this answer

Amazon Redshift is the correct choice because it is a fully managed, petabyte-scale data warehouse optimized for complex analytical queries involving aggregations across multiple dimensions. Its columnar storage, massively parallel processing (MPP) architecture, and ability to handle bulk daily updates via COPY commands or INSERT operations make it ideal for querying billions of rows of sales data.

Exam trap

The trap here is that candidates often confuse Amazon Athena as a primary data store because it can query data in S3, but it is a serverless query engine, not a data store, and lacks the performance optimizations for complex aggregations on billions of rows that a dedicated data warehouse like Redshift provides.

How to eliminate wrong answers

Option B is wrong because Amazon DynamoDB is a NoSQL key-value and document database designed for high-throughput, low-latency transactional workloads, not for complex analytical queries with multi-dimensional aggregations on billions of rows. Option C is wrong because Amazon Athena is a serverless interactive query service that queries data directly in Amazon S3 using standard SQL, but it is not a primary data store—it is a query engine, and it lacks the performance and optimization for frequent, complex aggregations on large datasets compared to a dedicated data warehouse. Option D is wrong because Amazon RDS for MySQL is a relational database optimized for OLTP workloads with row-based storage, which performs poorly on large-scale analytical queries and aggregations across billions of rows due to its lack of columnar storage and MPP capabilities.

Full explanation →
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