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AWS Certified Database Specialty DBS-C01 (DBS-C01) — Questions 526–600

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

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526
MCQhard

A company is using Amazon ElastiCache for Redis as a caching layer for a web application. The application's response time has increased, and the operations team suspects that cache evictions are occurring frequently. Which ElastiCache metric should be monitored to confirm cache evictions?

A.CacheHits
B.SwapUsage
C.Evictions
D.CurrItems
AnswerC

Evictions metric shows the number of keys evicted due to memory pressure, which directly indicates cache evictions.

Why this answer

The Evictions metric in Amazon ElastiCache for Redis shows the number of keys evicted due to memory pressure. Option A (CacheHits) is wrong because it indicates successful key retrievals, not evictions. Option B (SwapUsage) is wrong because it shows the amount of swap space used, not eviction count.

Option D (CurrItems) is wrong because it shows the current number of items in the cache, not evictions.

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527
MCQhard

A company is using Amazon DynamoDB for a gaming application. During a new game launch, write traffic spikes and some users receive 'ProvisionedThroughputExceededException' errors. The company wants to handle these spikes automatically and cost-effectively. What should be done?

A.Implement application-level retries with exponential backoff and a queue.
B.Switch the table to on-demand capacity mode.
C.Increase the provisioned write capacity to the expected peak.
D.Enable DynamoDB auto scaling with a target utilization of 70%.
AnswerD

Auto scaling automatically adjusts capacity to handle traffic spikes.

Why this answer

DynamoDB auto scaling with a target utilization of 70% allows the table to automatically adjust provisioned write capacity in response to traffic spikes, handling the increased load without manual intervention. This approach is cost-effective as it scales down during low traffic periods, avoiding over-provisioning. The target utilization of 70% provides a buffer to absorb sudden spikes while maintaining performance.

Exam trap

The trap here is that candidates often choose on-demand capacity mode (Option B) thinking it is always the best for unpredictable traffic, but the question emphasizes cost-effectiveness, and auto scaling provides a cheaper alternative for workloads with some predictability or sustained baseline traffic.

How to eliminate wrong answers

Option A is wrong because application-level retries with exponential backoff and a queue only mitigate the symptoms of throttling by retrying failed requests, but do not address the root cause of insufficient write capacity, leading to potential latency and queue buildup. Option B is wrong because switching to on-demand capacity mode can handle spikes automatically but is generally more expensive for predictable or sustained workloads, making it less cost-effective than auto scaling for this scenario. Option C is wrong because increasing provisioned write capacity to the expected peak results in over-provisioning during normal traffic, leading to unnecessary costs, and does not automatically adjust to actual demand.

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528
MCQhard

A company is using Amazon DynamoDB with provisioned capacity and Auto Scaling. They notice that during a marketing campaign, write traffic exceeded the provisioned WCU and caused throttling. Auto Scaling increased the WCU, but the throttling persisted for several minutes. Which additional measure can prevent throttling during such predictable spikes?

A.Switch the table to on-demand capacity mode.
B.Enable DynamoDB Accelerator (DAX) to cache write requests.
C.Increase the maximum provisioned WCU in the Auto Scaling policy.
D.Configure a scheduled scaling action in Application Auto Scaling to increase WCU before the campaign.
AnswerD

Scheduled scaling pre-provisions capacity for known traffic patterns.

Why this answer

Using Application Auto Scaling scheduled scaling allows pre-scaling capacity before the predictable spike, preventing throttling. Option A is incorrect because on-demand mode is for unpredictable traffic and may be costlier; for predictable spikes, scheduled scaling is more cost-effective. Option B is wrong because DAX is a caching layer for reads, not writes.

Option C is wrong because simply increasing the maximum WCU does not pre-scale capacity; it only raises the upper limit that Auto Scaling can reach after throttling occurs.

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529
MCQhard

A company is planning to migrate a 5 TB Oracle data warehouse to Amazon Redshift. They need to transform the data during migration. Which AWS service should they use to perform the transformation?

A.Amazon Kinesis Data Analytics for real-time transformation.
B.Amazon Athena to query the source data and write results to Redshift.
C.AWS Glue to perform extract, transform, and load (ETL) jobs.
D.AWS Database Migration Service (DMS) with transformation rules.
AnswerC

Glue is a fully managed ETL service suitable for transforming data before loading into Redshift.

Why this answer

AWS Glue is the correct choice because it is a fully managed ETL service designed for data transformation and loading into Amazon Redshift. It can handle the 5 TB Oracle data warehouse migration by running Spark-based ETL jobs that transform the data before writing it to Redshift, making it ideal for batch-oriented data warehouse migrations.

Exam trap

The trap here is that candidates often confuse AWS DMS's transformation rules (which handle simple schema mapping) with the full ETL capabilities needed for data warehouse migration, leading them to choose DMS instead of Glue.

How to eliminate wrong answers

Option A is wrong because Amazon Kinesis Data Analytics is for real-time stream processing, not for batch ETL transformations of a 5 TB data warehouse migration. Option B is wrong because Amazon Athena is an interactive query service for data in S3, not a tool for performing ETL transformations and loading data into Redshift; it cannot directly write transformed data to Redshift. Option D is wrong because AWS DMS with transformation rules is primarily for schema and data type conversions during live migration, not for complex data transformations required in a data warehouse migration; it lacks the ETL capabilities needed for significant data reshaping.

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530
MCQeasy

A company wants to migrate an on-premises MongoDB database to Amazon DocumentDB. The migration should be performed with minimal application changes. Which AWS service should be used?

A.Amazon S3 Transfer Acceleration
B.AWS DataSync
C.AWS Schema Conversion Tool (AWS SCT)
D.AWS Database Migration Service (AWS DMS)
AnswerD

AWS DMS can migrate data from MongoDB to DocumentDB with minimal application changes.

Why this answer

AWS DMS is the correct service because it supports homogeneous migrations from MongoDB to Amazon DocumentDB, including ongoing replication via change data capture (CDC) to minimize downtime. It handles schema conversion automatically for compatible data types, requiring minimal application changes since DocumentDB is MongoDB-compatible. AWS DMS can migrate data directly from a MongoDB source to a DocumentDB target without needing an intermediate schema transformation tool.

Exam trap

The trap here is that candidates may choose AWS SCT thinking schema conversion is always required, but for a homogeneous migration to a MongoDB-compatible target like DocumentDB, AWS DMS alone handles both schema and data migration without needing SCT.

How to eliminate wrong answers

Option A is wrong because Amazon S3 Transfer Acceleration is used to speed up uploads to S3 over long distances, not for database migration or replication. Option B is wrong because AWS DataSync is designed for moving large datasets between on-premises storage and AWS storage services (e.g., S3, EFS, FSx), not for database migration or ongoing replication. Option C is wrong because AWS Schema Conversion Tool (AWS SCT) is used for heterogeneous database migrations (e.g., Oracle to Aurora) to convert schema and code, but it is not needed for a homogeneous migration from MongoDB to DocumentDB, and it does not perform the actual data migration.

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531
MCQhard

A company stores financial data in an Amazon Aurora MySQL DB cluster. The security team requires that database audit logs be stored in Amazon CloudWatch Logs and encrypted at rest using a customer-managed KMS key. The database specialist enables audit log publishing to CloudWatch Logs and specifies a KMS key for log encryption. However, the audit logs are not appearing in CloudWatch Logs. What is the most likely cause?

A.The CloudWatch Logs log group does not exist and RDS cannot create it automatically.
B.The DB cluster is not configured to export error logs, only audit logs.
C.The IAM role used for publishing logs does not have the necessary permissions to use the KMS key for CloudWatch Logs.
D.CloudWatch Logs does not support encryption with customer-managed KMS keys for audit logs.
E.The audit log parameter is static and requires a DB cluster reboot after modification.
AnswerC

The IAM role must have kms:Encrypt permission on the KMS key to allow log delivery.

Why this answer

When publishing database audit logs to CloudWatch Logs with a customer-managed KMS key, the IAM role used by RDS must have explicit permissions for the `kms:Encrypt` and `kms:Decrypt` actions on the KMS key. Without these permissions, RDS cannot encrypt the log stream, and the logs will not appear. Option C correctly identifies this missing permission as the most likely cause.

Exam trap

The trap here is that candidates often assume the issue is a missing log group or a static parameter, but the exam tests the nuanced requirement that the IAM role must have explicit KMS key permissions for log encryption to work.

How to eliminate wrong answers

Option A is wrong because RDS can automatically create the CloudWatch Logs log group when publishing is enabled; the log group does not need to pre-exist. Option B is wrong because the question specifically states audit logs are enabled, and the issue is that no logs appear at all, not that only error logs are missing. Option D is wrong because CloudWatch Logs fully supports encryption with customer-managed KMS keys for audit logs; this is a supported feature.

Option E is wrong because the audit log parameter (`server_audit_logging`) is dynamic and does not require a reboot; it takes effect immediately after modification.

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532
MCQmedium

A database administrator is troubleshooting a backup failure for an Amazon RDS for SQL Server DB instance. The error message states 'Insufficient storage capacity for backup. Free storage space is 0 GB.' The DB instance has 200 GB allocated storage. What is the most likely cause?

A.The DB instance has insufficient free storage space to perform the backup.
B.The DB instance has reached the maximum number of manual snapshots.
C.The transaction logs are consuming all allocated storage.
D.The backup retention period is set to 0 days, disabling automated backups.
AnswerA

The DB instance has insufficient free storage space to perform the backup. This is correct because RDS uses allocated storage for backup operations. When free space is 0 GB, backups cannot proceed.

Why this answer

RDS requires free storage space to perform backups and manage transaction logs. With 0 GB free, the backup fails. Option B is incorrect because the number of manual snapshots does not affect the storage capacity of the DB instance; manual snapshots are stored separately in Amazon S3.

Option C is incorrect because although transaction logs consume allocated storage, the error specifically indicates insufficient free storage for the backup operation, and the most likely cause is that the instance lacks free storage. Option D is incorrect because setting the backup retention period to 0 days disables automated backups entirely; it does not cause a backup failure due to insufficient storage.

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533
MCQhard

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

A.Partition the table by time using PostgreSQL table partitioning
B.Migrate to Amazon Aurora PostgreSQL and enable parallel query
C.Add more indexes on the timestamp column
D.Create a read replica and direct queries to the replica
AnswerA

Partitioning by time allows partition pruning, significantly improving query performance on timestamp filters.

Why this answer

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

Exam trap

The trap here is that candidates often assume adding indexes or using read replicas will solve performance issues for large time-series tables, but they fail to recognize that partitioning directly reduces the data scanned per query, which is the root cause of slow filtering on timestamp columns.

How to eliminate wrong answers

Option B is wrong because migrating to Aurora PostgreSQL and enabling parallel query can improve performance for large scans, but it does not address the fundamental issue of scanning an entire 500 GB table for time-range queries; partitioning is a more targeted and cost-effective solution. Option C is wrong because adding more indexes on the timestamp column does not help when queries filter on a range of timestamps—the index can still lead to a large number of random I/Os and does not reduce the amount of data that must be read from the table. Option D is wrong because creating a read replica and directing queries to it distributes read load but does not reduce the amount of data scanned per query; the replica still has the same table structure and performance limitations.

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534
MCQeasy

A company notices that its Amazon DynamoDB table is consuming more read capacity than expected. The table has a global secondary index (GSI) with a different sort key. Which action would most likely reduce the read consumption?

A.Increase the write capacity of the table.
B.Enable DAX (DynamoDB Accelerator) to cache read results.
C.Change the sort key of the base table to match the GSI sort key.
D.Create a local secondary index (LSI) with the same sort key as the GSI.
AnswerB

DAX reduces the number of reads to the table, lowering read capacity consumption.

Why this answer

Enabling DynamoDB Accelerator (DAX) caches read results from the DynamoDB table, reducing the number of reads that consume read capacity units (RCUs). When the table has a GSI, reads that use the GSI also consume RCUs from the index. DAX can cache both base table and index reads, thereby lowering overall read consumption.

Option A is incorrect because increasing write capacity does not affect read consumption. Option C is incorrect because changing the sort key does not directly reduce read capacity usage. Option D is incorrect because a local secondary index (LSI) uses the same partition key and does not help reduce read consumption from a GSI.

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535
MCQhard

A company is migrating a 2 TB Oracle database running on an on-premises Linux server to Amazon RDS for Oracle. The migration must have minimal downtime and must be fully reversible if any issues arise. The DBA has configured AWS DMS with a full load and ongoing replication task. The full load completes successfully, and CDC is replicating changes. During the cutover window, the DBA stops the source database and promotes the target RDS instance. However, after cutover, the application team reports that some recent transactions are missing from the target database. The DBA confirms that the DMS task showed a 'healthy' status before stopping. Which action should the DBA take to resolve the issue and prevent recurrence?

A.Rebuild the entire migration using a native Oracle export/import tool.
B.Increase the replication instance size to improve throughput.
C.Restart the DMS task from the beginning to recapture the missing data.
D.Before stopping the source database, run the 'Stop task' command with the '--apply-immediately' option to ensure all cached changes are written to the target.
AnswerD

This ensures that all remaining CDC changes are applied before stopping.

Why this answer

In AWS DMS, after full load completes, ongoing replication (CDC) accumulates changes in a cache. To ensure no data loss during cutover, the DBA should stop the source database and then issue a 'Stop task' command with the '--apply-immediately' option (or use the 'Last Stop' job in the console) to flush all cached changes to the target before stopping the task. Option A (native export/import) would cause significant downtime and is unnecessary.

Option B (increasing instance size) improves throughput but does not capture remaining cached changes. Option C (restarting from the beginning) would lose already migrated data and is not a targeted fix. Only option D directly addresses the issue of missing transactions by ensuring all CDC cached changes are applied before the task is stopped.

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536
MCQeasy

A company is deploying a new application that requires a highly available DynamoDB table with eventual consistency. The table will be accessed from multiple AWS Regions. What is the most appropriate deployment strategy?

A.Use DynamoDB auto scaling to handle regional traffic
B.Use DynamoDB Accelerator (DAX) to cache data across regions
C.Use DynamoDB global tables
D.Use DynamoDB Streams to replicate data to another region
AnswerC

Global tables provide managed multi-region replication with eventual consistency.

Why this answer

DynamoDB global tables provide a fully managed, multi-Region, multi-active solution that automatically replicates data across selected AWS Regions, delivering eventual consistency and high availability for applications accessed from multiple Regions. This is the most appropriate strategy because it natively supports the requirement without custom code or additional infrastructure.

Exam trap

The trap here is that candidates may confuse DynamoDB Streams (a single-Region change capture mechanism) with global tables (which use Streams internally for multi-Region replication), leading them to choose Option D as a DIY approach instead of the managed global tables service.

How to eliminate wrong answers

Option A is wrong because DynamoDB auto scaling adjusts read/write capacity within a single Region based on traffic, but it does not replicate data across Regions or provide multi-Region access. Option B is wrong because DAX is an in-memory cache for a single Region; it does not replicate data across Regions and cannot provide multi-Region eventual consistency. Option D is wrong because DynamoDB Streams capture item-level changes in a single Region and require custom application logic to replicate data to another Region, which adds complexity and is not a managed multi-Region solution.

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537
MCQeasy

A developer is using AWS Database Migration Service (DMS) to migrate a database from on-premises to Amazon RDS. The migration task is failing with 'Insufficient memory' error. Which resource should be increased to resolve this?

A.Increase the size of the DMS replication instance.
B.Increase the memory on the source database.
C.Increase the Amazon S3 bucket size for staging.
D.Increase the memory on the target RDS instance.
AnswerA

Increasing the size of the DMS replication instance provides more memory for the migration task, resolving the 'Insufficient memory' error.

Why this answer

The DMS replication instance may have insufficient memory. Increasing its size provides more memory. Option B is wrong because source database memory is not controlled by DMS.

Option C is wrong because S3 is not involved in the DMS process by default. Option D is wrong because target RDS instance memory may not be the bottleneck.

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538
MCQeasy

A company runs an e-commerce application on AWS using an Aurora MySQL database cluster. The security team mandates that all database audit logs must be stored in Amazon S3 for at least one year for compliance. The database is currently configured to publish audit logs to Amazon CloudWatch Logs. The security team wants to use AWS Database Migration Service (DMS) to migrate the database to a new Aurora MySQL cluster, and during the migration, the audit logs must continue to be captured uninterrupted. Which solution meets these requirements with the LEAST operational overhead?

A.Enable Performance Insights on the source cluster and export the data to S3.
B.Create an Aurora MySQL read replica and enable audit logs on the replica, then migrate from the replica.
C.Use AWS CloudTrail to capture SQL queries and deliver them to S3.
D.Enable the Aurora MySQL advanced audit feature with file-based output, and configure the DMS task to use these log files as a source for ongoing replication.
AnswerD

The Aurora MySQL advanced audit feature can write audit logs to files by setting `aurora_audit_log_type` to `FILE`. These files can be uploaded to S3, and DMS can read them as a source for ongoing replication, ensuring uninterrupted audit log capture with minimal operational overhead.

Why this answer

Enabling the Aurora MySQL advanced audit feature allows audit logs to be output to files on the DB instance. These files can then be exported to Amazon S3 (e.g., via a scheduled script or Lambda function) for compliance. During the DMS migration, change data capture is handled by binary logs, not audit logs, so the audit logging remains uninterrupted.

This approach minimizes operational overhead by using the built-in audit feature and a simple export mechanism, without requiring additional services like CloudTrail or Performance Insights.

Exam trap

The trap here is that candidates may confuse CloudTrail (which logs AWS API calls) with database-level audit logging, or assume that a read replica can seamlessly inherit and forward audit logs from the source, when in fact it only logs its own activity.

How to eliminate wrong answers

Option A is wrong because Performance Insights provides performance metrics, not database audit logs, and cannot export SQL audit data to S3. Option B is wrong because creating a read replica and enabling audit logs on it would not capture audit logs from the source cluster during migration; the replica only logs its own activity, and the migration from the replica would still require uninterrupted audit capture from the source. Option C is wrong because CloudTrail captures AWS API calls (e.g., RDS management actions), not SQL queries or database-level audit logs, so it cannot fulfill the requirement to store database audit logs.

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539
MCQmedium

A company is migrating a 2 TB Oracle database from on-premises to Amazon RDS for Oracle. The database has a 4-hour maintenance window and the migration must have minimal downtime. Which AWS service should be used for the migration?

A.Use AWS Schema Conversion Tool (SCT) to convert the schema and then perform a full data load.
B.Create an RDS Cross-Region Read Replica and promote it to a standalone instance.
C.Use AWS Database Migration Service (DMS) with ongoing replication from the source database.
D.Take a full backup of the on-premises database, upload it to S3, and restore it to RDS.
AnswerC

AWS DMS can perform continuous replication, minimizing downtime.

Why this answer

AWS DMS with ongoing replication (change data capture) is the correct choice because it allows you to perform a full load of the 2 TB Oracle database and then continuously replicate incremental changes from the source to the target RDS instance. This minimizes downtime by enabling a cutover window of minutes rather than hours, which is critical given the 4-hour maintenance window constraint.

Exam trap

The trap here is that candidates often confuse AWS SCT with DMS, assuming SCT can handle ongoing replication, or they mistakenly think RDS Cross-Region Read Replicas can be created from an on-premises source, when in fact they only work between RDS instances.

How to eliminate wrong answers

Option A is wrong because AWS Schema Conversion Tool (SCT) is used for heterogeneous migrations (e.g., Oracle to Aurora PostgreSQL) and does not handle ongoing replication; it only converts schema and performs a one-time full load, which would cause extended downtime. Option B is wrong because RDS Cross-Region Read Replicas are only supported within RDS itself, not for on-premises databases; they cannot replicate from an external Oracle source. Option D is wrong because taking a full backup, uploading to S3, and restoring to RDS is a one-time bulk operation that does not capture ongoing changes, resulting in significant downtime during the backup and restore process.

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540
MCQmedium

A gaming company uses Amazon RDS for PostgreSQL to store player profiles and game state data. The database is currently 500 GB and grows by 10 GB per day. The company runs weekly reports that scan the entire database, causing high I/O and CPU usage. The application experiences read latency spikes during report generation. The team wants to minimize performance impact on the application while maintaining the ability to run reports. Which solution should the team implement?

A.Create a read replica and direct all report queries to the read replica.
B.Enable Multi-AZ deployment to provide a standby instance for failover and use it for reporting.
C.Scale up the RDS instance to a larger instance type to handle the additional load from reports.
D.Archive historical game state data to Amazon S3 and delete it from the database to reduce size.
AnswerA

A read replica offloads read-intensive workloads from the primary, reducing latency for the application.

Why this answer

Creating a read replica for Amazon RDS for PostgreSQL allows the team to offload all report queries to a separate read-only endpoint, eliminating the I/O and CPU contention on the primary database. This directly addresses the read latency spikes during report generation without requiring any application changes beyond redirecting the reporting queries. The read replica asynchronously replicates data from the primary instance, ensuring the reports see a near-real-time snapshot of the data while the primary remains dedicated to the application workload.

Exam trap

The trap here is that candidates often confuse Multi-AZ standby instances with read replicas, mistakenly believing the standby can be used for read traffic, but AWS explicitly prevents read access to the standby to maintain synchronous replication integrity.

How to eliminate wrong answers

Option B is wrong because a Multi-AZ standby instance is not accessible for read queries; it is a synchronous replica used solely for automatic failover and cannot serve traffic, so it would not offload the reporting workload. Option C is wrong because scaling up the RDS instance to a larger type only increases the capacity of the single instance, but the report queries would still compete with the application for the same I/O and CPU resources, failing to minimize the performance impact. Option D is wrong because archiving historical data to S3 reduces the database size but does not address the immediate I/O and CPU spikes caused by the weekly full-table scans; the reports would still scan the remaining data and cause latency issues.

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541
MCQhard

A company runs a data warehouse on Amazon Redshift. The workload has frequent DELETE and UPDATE operations on a large fact table. Over time, query performance degrades. Which maintenance operation should be scheduled regularly to optimize performance?

A.Run VACUUM FULL during maintenance windows
B.Run VACUUM and ANALYZE commands regularly
C.Alter the table to use a different DISTKEY
D.Drop and recreate the table periodically
AnswerB

Reclaims space and updates statistics for better query plans.

Why this answer

B is correct because frequent DELETE and UPDATE operations in Redshift create ghost rows and cause table bloat, degrading query performance. Running VACUUM reclaims space and re-sorts rows, while ANALYZE updates table statistics for the query optimizer; together they restore performance without requiring a full table rebuild.

Exam trap

The trap here is that candidates confuse VACUUM FULL (a PostgreSQL command) with Redshift's VACUUM options, or assume that changing the DISTKEY or recreating the table is a practical maintenance strategy instead of using the native VACUUM and ANALYZE commands.

How to eliminate wrong answers

Option A is wrong because VACUUM FULL is not a valid Redshift command; the correct commands are VACUUM (with optional FULL parameter) and VACUUM DELETE ONLY, but VACUUM FULL is a PostgreSQL command not applicable here. Option C is wrong because altering the DISTKEY is a schema design change that requires a table rebuild and does not address the immediate bloat and statistics issues caused by frequent DELETEs and UPDATEs. Option D is wrong because dropping and recreating the table is disruptive, causes downtime, and loses data unless carefully managed; it is not a regular maintenance operation and does not leverage Redshift's built-in VACUUM and ANALYZE capabilities.

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542
MCQeasy

A company wants to restrict access to an Amazon RDS for MySQL DB instance so that only applications running in a specific VPC can connect. Which solution should be implemented?

A.Use an IAM policy to restrict database connections based on source IP.
B.Configure the DB instance's security group to allow inbound traffic only from the application's security group.
C.Configure the subnet's network ACL to allow inbound traffic only from the application's IP range.
D.Attach a security group to the subnet that allows inbound traffic from the application's VPC.
AnswerB

Security groups can reference other security groups.

Why this answer

Security groups act as a virtual firewall for the DB instance, and by allowing inbound traffic only from the application's security group, access is restricted to resources in that VPC. Option A is incorrect because IAM policies control API-level permissions, not network-level access. Option C is incorrect because network ACLs are stateless and apply at the subnet level, not at the instance level.

Option D is incorrect because security groups are not attached to subnets; they are attached to network interfaces.

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543
MCQhard

A database administrator is troubleshooting connectivity to an Amazon RDS for MySQL DB instance. The application is running on an EC2 instance in the same VPC and security group. The application can connect using the endpoint shown in the exhibit. However, the security team requires that all connections be encrypted using SSL. The DBA has enabled SSL on the DB instance and modified the parameter group to set require_secure_transport to ON. The application is now failing to connect. What is the most likely cause?

A.The DB instance endpoint is not resolving to the correct IP address.
B.The database user account does not have the SSL privilege granted.
C.The application's JDBC connection string does not include SSL parameters such as useSSL=true.
D.The security group does not allow inbound traffic on port 3307, which is used for SSL connections.
AnswerC

The application must explicitly request SSL connections; otherwise, the server rejects the connection.

Why this answer

When require_secure_transport is set to ON, the database server rejects non-SSL connections. The application must be configured to use SSL by adding the useSSL=true and requireSSL=true parameters to the JDBC connection string. Option A is incorrect because the application could connect before SSL was enforced, indicating that DNS resolution was working.

Option B is incorrect because the server enforces SSL at the transport level, and no separate SSL privilege is needed for the user account. Option D is incorrect because SSL connections use the same port as non-SSL (3306 for MySQL), and the security group was already configured to allow traffic on that port.

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544
MCQeasy

A company needs to migrate a 2 TB PostgreSQL database from an on-premises data center to Amazon Aurora PostgreSQL. The network bandwidth is limited to 100 Mbps. The migration window is 5 days. Which approach is most cost-effective and likely to succeed?

A.Use AWS DMS with a VPN connection to the on-premises database
B.Set up AWS Direct Connect and use DMS
C.Use AWS Snowball Edge to transfer data offline
D.Export to Amazon S3 and import into Aurora
AnswerA

DMS can migrate online within bandwidth constraints.

Why this answer

AWS DMS with a VPN connection can handle the migration of 2 TB over 100 Mbps within 5 days. At 100 Mbps, the theoretical maximum transfer is about 1.08 TB per day (100 Mbps * 86400 seconds / 8 bits per byte / 1024^3), so 2 TB would take roughly 1.85 days of continuous transfer, well within the 5-day window. DMS supports ongoing replication to minimize downtime, and a VPN is cost-effective compared to Direct Connect for a one-time migration.

Exam trap

The trap here is that candidates often overestimate the need for offline transfer (Snowball) or expensive dedicated connections (Direct Connect) when the bandwidth and time window are actually sufficient for an online migration, leading them to ignore the cost-effectiveness of a VPN-based DMS approach.

How to eliminate wrong answers

Option B is wrong because AWS Direct Connect is more expensive to set up and provision than a VPN, and for a 2 TB migration over 100 Mbps, the bandwidth is sufficient without the additional cost and lead time of Direct Connect. Option C is wrong because AWS Snowball Edge is designed for offline transfer of large datasets (typically 10 TB+ or when bandwidth is extremely limited), but here the bandwidth is adequate and the migration window is long enough, making Snowball more costly and slower due to shipping and processing delays. Option D is wrong because exporting a 2 TB PostgreSQL database to Amazon S3 and then importing into Aurora is not a native or efficient method; it requires custom scripting, lacks built-in replication, and would likely exceed the 5-day window due to the overhead of export/import operations and lack of incremental sync.

Full explanation →
545
MCQhard

A gaming company uses Amazon DynamoDB to store player profiles. Each profile is about 5 KB and is accessed frequently. The access pattern is mostly point reads by player ID. The company wants to reduce read costs while maintaining low latency. Currently, the table uses provisioned capacity with 3000 RCU. Which change would be MOST effective?

A.Use strongly consistent reads instead of eventually consistent reads.
B.Switch from provisioned capacity to on-demand capacity mode.
C.Decrease the provisioned RCU to 2000 and rely on adaptive capacity.
D.Use DynamoDB Accelerator (DAX) to cache frequently accessed items.
AnswerD

DAX reduces reads from the table, lowering RCU consumption.

Why this answer

DynamoDB Accelerator (DAX) provides an in-memory cache that can serve frequently accessed items (like player profiles) with microsecond latency, reducing the number of read requests that hit the underlying table. Since each profile is 5 KB and accessed via point reads, DAX can absorb a significant portion of the read traffic, allowing you to lower provisioned RCU without sacrificing performance. This directly reduces read costs while maintaining low latency, making it the most effective solution.

Exam trap

The trap here is that candidates may think reducing provisioned capacity (Option C) is sufficient, but they overlook that adaptive capacity only handles short-term bursts and does not prevent throttling from sustained high read traffic, whereas DAX directly reduces the read load on the table.

How to eliminate wrong answers

Option A is wrong because strongly consistent reads consume twice the RCU of eventually consistent reads, increasing costs and latency, not reducing them. Option B is wrong because switching to on-demand capacity mode eliminates the need to manage provisioned capacity but does not inherently reduce read costs; on-demand can be more expensive for steady-state workloads with high read throughput. Option C is wrong because decreasing RCU to 2000 and relying on adaptive capacity risks throttling if the actual read demand exceeds the reduced capacity; adaptive capacity only helps with short-term bursts, not sustained reductions, and does not address the core goal of lowering costs while maintaining low latency.

Full explanation →
546
MCQmedium

Refer to the exhibit. A DBA sees the above log entries for an Amazon Aurora MySQL cluster. What is the most likely cause?

A.There is underlying storage corruption
B.The reader instance is lagging behind the writer
C.The DB instance has run out of connections
D.A recent backup restore operation failed
AnswerA

Page corruption errors indicate storage issues.

Why this answer

The logs show page corruption and checksum errors, which are classic symptoms of underlying storage corruption in Aurora MySQL. Storage corruption can occur due to hardware failures or software bugs, and Aurora's distributed storage layer can sometimes experience such issues that manifest as corrupted pages. Option B is incorrect because reader lag would show replication delay metrics and possibly 'seconds behind master' warnings, not corruption-related errors.

Option C is incorrect because connection exhaustion would generate errors like 'too many connections' or timeouts, not page corruption. Option D is incorrect because a failed backup restore would typically result in errors during the restore process, not ongoing page corruption after the instance is running.

Full explanation →
547
MCQmedium

A company is migrating a 500 GB Oracle database to Amazon RDS for Oracle. The migration must be completed within 24 hours with minimal downtime. The on-premises network bandwidth is 1 Gbps. The migration team plans to use AWS DMS with ongoing replication. During the initial load, the DMS task fails with an error indicating that the source database is experiencing high CPU utilization. The source database is a production system serving live traffic. The team needs to reduce the impact on the source database while still meeting the migration deadline. Which approach should the team take?

A.Change the DMS task to perform only full load and skip ongoing replication
B.Reduce the number of tables loaded in parallel in the DMS task and schedule the migration during off-peak hours
C.Increase the size of the DMS replication instance to speed up the migration
D.Use AWS Snowball to transfer the data offline and then use DMS for ongoing replication
AnswerB

This reduces source CPU load and still meets the deadline if off-peak window is sufficient.

Why this answer

The issue is that DMS is consuming too many resources on the source. Reducing the number of tables loaded in parallel and scheduling the migration during off-peak hours will reduce CPU impact. Option B is the most balanced.

Option A (increase DMS instance size) would increase source CPU usage. Option C (change to full load only) would require downtime. Option D (use Snowball) may not meet the 24-hour deadline due to shipping time.

Full explanation →
548
MCQeasy

A developer accidentally deleted a critical table from an Amazon RDS for MySQL DB instance. Automated backups are enabled with a retention period of 7 days. The deletion occurred 3 hours ago. What is the fastest way to restore the deleted table without affecting other tables?

A.Use the RDS Query Editor to run a flashback query that retrieves the deleted data.
B.Restore the DB instance from the latest manual snapshot and extract the table.
C.Restore the table from the automated backup using the AWS Management Console table-level restore feature.
D.Perform a point-in-time restore of the DB instance to a time just before the deletion, then export the table.
AnswerD

Point-in-time restore creates a new DB instance as it was at the specified time, allowing table extraction without affecting the original instance.

Why this answer

Point-in-time recovery (PITR) allows you to restore the entire DB instance to any second within the automated backup retention period (7 days). By restoring to a time just before the deletion, you can then extract the deleted table using mysqldump or SELECT INTO OUTFILE, and import it back into the original instance. This is the fastest method because it leverages existing automated backups without requiring a manual snapshot or waiting for a full restore of a large instance.

Exam trap

The trap here is that candidates confuse the table-level restore feature available in Amazon Aurora (via backtrack or cloning) with standard RDS MySQL, which lacks such granularity and requires a full instance restore for point-in-time recovery.

How to eliminate wrong answers

Option A is wrong because RDS for MySQL does not support flashback queries; that feature is specific to Oracle Database and Amazon Aurora with MySQL compatibility (using undo logs). Option B is wrong because restoring from a manual snapshot would require you to have taken one before the deletion, and the scenario only mentions automated backups, not manual snapshots; even if a manual snapshot existed, restoring the entire instance and then extracting the table is slower than PITR. Option C is wrong because RDS for MySQL does not offer a table-level restore feature from automated backups; that capability exists for Amazon Aurora (using backtrack or cloning) but not for standard RDS MySQL.

Full explanation →
549
MCQmedium

A company is migrating a 1 TB Amazon RDS for Oracle database to Amazon RDS for PostgreSQL. The migration must be completed within a 4-hour maintenance window. The database has a high volume of transactions. Which migration method minimizes risk of data loss?

A.Use AWS SCT to convert the schema, then use AWS DMS for full load and ongoing replication.
B.Set up Oracle log shipping to an EC2 instance, then restore to RDS PostgreSQL.
C.Use AWS DMS with a full load and change data capture (CDC) enabled, with task restart and validation.
D.Export the Oracle database using Data Pump, import to PostgreSQL using pgloader.
AnswerC

CDC captures ongoing changes, task restart ensures reliability.

Why this answer

AWS DMS with full load and CDC enables continuous replication of ongoing transactions after the initial load, minimizing data loss risk during the migration window. The task restart and validation features ensure resilience against transient failures and data integrity checks, critical for a high-volume transactional database. This approach meets the 4-hour window by allowing the full load to complete within the window while CDC captures any changes that occur during and after the load.

Exam trap

The trap here is that candidates often assume AWS SCT plus DMS full load (Option A) is sufficient for minimal data loss, overlooking that CDC is required to capture ongoing transactions during the migration window.

How to eliminate wrong answers

Option A is wrong because using AWS SCT for schema conversion followed by DMS full load and ongoing replication lacks the CDC component, meaning any transactions occurring during the full load would be lost, increasing data loss risk. Option B is wrong because Oracle log shipping to an EC2 instance and then restoring to RDS PostgreSQL is not supported; RDS PostgreSQL does not accept Oracle log-shipped files, and this method introduces unnecessary complexity and potential data loss without native CDC. Option D is wrong because exporting with Oracle Data Pump and importing with pgloader is a one-time, offline migration that cannot capture ongoing transactions, leading to significant data loss if the database remains active during the migration window.

Full explanation →
550
MCQeasy

A company is migrating a 100 GB SQL Server database to Amazon RDS for SQL Server. They need to minimize downtime and ensure that only the schema is converted. Which tool should they use?

A.AWS Database Migration Service (DMS)
B.AWS Schema Conversion Tool (SCT)
C.AWS DMS with schema conversion enabled
D.SQL Server Management Studio (SSMS)
AnswerB

SCT is designed to convert database schema from one engine to another.

Why this answer

AWS Schema Conversion Tool (SCT) is the correct choice because it is specifically designed to convert database schemas (including stored procedures, functions, and data types) from one engine to another, such as SQL Server to Amazon RDS for SQL Server. Since the requirement is to minimize downtime and only convert the schema (not migrate data), SCT can perform an offline schema conversion without impacting the source database, whereas DMS is a continuous data replication tool that would incur downtime for schema changes.

Exam trap

The trap here is that candidates often confuse AWS DMS (which handles data replication) with AWS SCT (which handles schema conversion), and assume DMS can perform schema-only migrations without realizing it always includes data transfer and potential downtime.

How to eliminate wrong answers

Option A is wrong because AWS DMS is primarily a data migration service that replicates ongoing changes and requires a full load of data, which would cause downtime and does not focus on schema-only conversion. Option C is wrong because DMS with schema conversion enabled still performs data migration and schema conversion together, leading to unnecessary downtime and data transfer when only schema conversion is needed. Option D is wrong because SQL Server Management Studio (SSMS) can generate schema scripts but does not automate conversion to RDS SQL Server's specific dialect or handle compatibility issues, and it would require manual intervention and potential downtime.

Full explanation →
551
MCQeasy

A company is using Amazon DynamoDB with auto scaling enabled. The table's read capacity is set to a minimum of 100 and maximum of 1000 read capacity units (RCUs). The actual consumed read capacity is consistently at 200 RCUs. What should the database specialist do to optimize costs without impacting performance?

A.Increase the minimum read capacity to 500 RCUs.
B.Lower the minimum read capacity to 200 RCUs.
C.Disable auto scaling and set the read capacity to 200 RCUs.
D.Decrease the maximum read capacity to 500 RCUs.
AnswerB

Matches the actual consumption, preventing over-provisioning.

Why this answer

Lowering the minimum read capacity to 200 RCUs matches the actual consumption, preventing over-provisioning when auto scaling scales down. This reduces costs without affecting performance, as the capacity remains sufficient for the consistent workload. Option A is incorrect because increasing the minimum would raise costs unnecessarily.

Option C is incorrect because disabling auto scaling removes the ability to handle traffic spikes automatically. Option D is incorrect because decreasing the maximum could cause throttling during unexpected demand spikes.

Full explanation →
552
Multi-Selecthard

A company is migrating a large Oracle data warehouse to AWS. The warehouse contains 50 TB of data and runs complex analytical queries. The solution must support concurrency of up to 100 users and provide high performance for queries. Which THREE design decisions should the company make? (Choose three.)

Select 3 answers
A.Use distribution keys based on frequently joined columns
B.Design tables with columnar storage
C.Use Amazon RDS for Oracle with Multi-AZ
D.Use Amazon DynamoDB with global tables
E.Use Amazon Redshift as the database engine
AnswersA, B, E

Distribution keys enable parallel processing and reduce data movement.

Why this answer

Distribution keys based on frequently joined columns ensure that related data is co-located on the same compute nodes, minimizing data movement across the network during joins. This is critical for complex analytical queries on large datasets in Amazon Redshift, as it reduces shuffle overhead and improves query performance.

Exam trap

The trap here is that candidates may confuse Amazon RDS for Oracle (an OLTP database) with a suitable data warehouse solution, overlooking that Redshift’s columnar storage and MPP architecture are specifically designed for large-scale analytical workloads.

Full explanation →
553
Matchingmedium

Match each AWS monitoring tool to its capability for databases.

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

Concepts
Matches

Collects metrics and logs, sets alarms

Visualizes database performance and identifies bottlenecks

Provides OS-level metrics for RDS instances

Records API calls for auditing and governance

SNS-based alerts for database events like failovers

Why these pairings

Correct matches: CloudWatch monitors metrics and logs, Performance Insights analyzes database load, Enhanced Monitoring provides OS-level metrics, and CloudTrail records API activity. Common confusions include mixing Enhanced Monitoring with CloudWatch and Performance Insights with CloudTrail.

Full explanation →
554
MCQhard

A financial services company needs to enforce row-level security on a MySQL database hosted on Amazon RDS. They want to restrict access so that each application user can only see their own data. Which approach should they take?

A.Place each user's data in a separate database and use VPC endpoints to isolate access
B.Create separate database views for each user
C.Use a MySQL proxy that injects session context variables and enable row-level security in the application queries
D.Use IAM database authentication and define fine-grained access policies
AnswerC

Allows dynamic row filtering per user.

Why this answer

MySQL does not natively support row-level security (RLS) like PostgreSQL or SQL Server. Instead, you can implement RLS by using a MySQL proxy (e.g., ProxySQL, Amazon RDS Proxy) that injects session context variables (e.g., user_id) at connection time. Application queries then reference these variables in WHERE clauses (e.g., WHERE user_id = @@session.user_id), effectively restricting each user to their own data without modifying the underlying schema.

Exam trap

The trap here is that candidates often confuse IAM database authentication (which only handles authentication) with authorization (row-level access), leading them to incorrectly select Option D, while overlooking the need for a proxy-based solution to inject session context for row filtering.

How to eliminate wrong answers

Option A is wrong because placing each user's data in a separate database does not enforce row-level security; it creates administrative overhead and VPC endpoints control network access, not row-level filtering. Option B is wrong because creating separate views for each user is not scalable for many users and requires manual maintenance; views do not dynamically filter rows based on the current user without additional context. Option D is wrong because IAM database authentication only controls who can connect to the database, not which rows they can see; fine-grained IAM policies cannot restrict access to specific rows in MySQL RDS.

Full explanation →
555
MCQeasy

A database specialist is troubleshooting an Amazon RDS for MySQL DB instance that is running out of storage. The instance has automated backups enabled. The specialist needs to free up storage space immediately without losing backup capability. Which action should the specialist take?

A.Modify the DB instance to reduce the backup retention period to 0 days.
B.Delete older automated backups that are no longer needed.
C.Delete manual snapshots from the RDS console.
D.Disable automated backups to stop storage consumption.
AnswerB

Deleting older automated backups also removes them from S3, not instance storage, so it does not help.

Why this answer

None of the provided options will free up DB instance storage. Automated backups are stored in Amazon S3, not in the instance's allocated storage. To free up instance storage, you would need to increase the allocated storage or delete actual data from the database.

Reducing backup retention deletes backups from S3, not instance storage. Manual snapshots are also in S3. Disabling backups stops future backups but does not free existing storage.

Full explanation →
556
MCQeasy

A company is using Amazon RDS for MySQL and notices that the Read IOPS metric is consistently high during business hours. The application is read-heavy. Which configuration change would most likely reduce Read IOPS?

A.Add a Multi-AZ standby instance.
B.Create one or more read replicas and redirect read traffic to them.
C.Increase the DB instance size to a larger instance type.
D.Enable storage Auto Scaling on the RDS instance.
AnswerB

Read replicas handle read queries from the primary, reducing read IOPS on the source instance.

Why this answer

Creating read replicas offloads read queries from the primary DB instance to replica instances, directly reducing the number of read I/O operations on the primary. Since the application is read-heavy and Read IOPS is high during business hours, distributing read traffic to replicas alleviates the I/O bottleneck on the primary instance without requiring a larger instance or storage changes.

Exam trap

The trap here is that candidates often confuse Multi-AZ with read replicas, assuming a standby instance can handle read traffic, but in RDS for MySQL, Multi-AZ standby is passive and does not serve reads.

How to eliminate wrong answers

Option A is wrong because a Multi-AZ standby instance is for high availability and failover, not for read scaling; it does not serve read traffic and thus does not reduce Read IOPS on the primary. Option C is wrong because increasing the DB instance size may improve throughput but does not reduce the number of read I/O operations; it only provides more capacity to handle the same I/O load, potentially leaving Read IOPS high. Option D is wrong because enabling storage Auto Scaling automatically increases storage when needed but does not reduce read I/O operations; it addresses storage capacity, not read workload distribution.

Full explanation →
557
MCQmedium

A company needs to store and query time-series data from IoT sensors. The data is written continuously and queried by time range for dashboards. Which AWS database service is most cost-effective and scalable for this workload?

A.Amazon ElastiCache for Redis with time-series data structures.
B.Amazon DynamoDB with time-based partition keys.
C.Amazon Timestream.
D.Amazon RDS for PostgreSQL with time-based indexing.
AnswerC

Managed time-series database with built-in analytics.

Why this answer

Amazon Timestream is purpose-built for time-series data, offering automatic tiering between in-memory and magnetic stores for cost efficiency, and built-in functions for time-based aggregations and windowed queries. It is serverless and scales automatically to handle continuous writes from IoT sensors and low-latency dashboard queries by time range, making it the most cost-effective and scalable choice.

Exam trap

The trap here is that candidates often choose DynamoDB for its scalability, overlooking that time-series workloads with sequential timestamps cause hot partitions and lack native time-series query capabilities, while Timestream is the only service specifically designed for this use case with automatic tiering and cost optimization.

How to eliminate wrong answers

Option A is wrong because ElastiCache for Redis is an in-memory cache, not a durable, scalable database for continuous time-series ingestion; it requires manual management of data eviction and lacks built-in time-series query optimization for large historical datasets. Option B is wrong because DynamoDB with time-based partition keys can lead to hot partitions due to sequential writes, and it lacks native time-series functions like interpolation or smoothing, requiring complex application logic for dashboard queries. Option D is wrong because RDS for PostgreSQL with time-based indexing incurs high storage and compute costs for continuous writes, requires manual scaling and partitioning, and lacks the automatic data tiering and query optimization that Timestream provides for time-series workloads.

Full explanation →
558
MCQmedium

A DBA runs the IAM policy simulation above. The DBA can describe the DB instance but cannot modify it. What is the most likely cause?

A.The resource ARN in the simulation is incorrect.
B.An IAM policy explicitly denies the rds:ModifyDBInstance action.
C.The DBA's IAM policy does not include an allow for rds:ModifyDBInstance.
D.The DBA is not within the VPC where the DB instance resides.
AnswerB

The evaluation result shows 'explicitDeny' for ModifyDBInstance.

Why this answer

The simulation shows that the DBA can describe the DB instance but cannot modify it. Since the simulation evaluates all applicable IAM policies, an explicit deny for rds:ModifyDBInstance in any attached policy will override any allow and cause the modify action to fail. This is the most likely cause because explicit denies are evaluated first and cannot be overridden by other policies.

Exam trap

The trap here is that candidates often assume a missing allow is the cause, but AWS IAM simulation results clearly distinguish between implicit deny (no policy allows) and explicit deny (a policy specifically denies), and the question's phrasing 'cannot modify' combined with the simulation context points to an explicit deny as the most likely cause.

How to eliminate wrong answers

Option A is wrong because if the resource ARN were incorrect, the simulation would likely fail for all actions, not just the modify action; the DBA can still describe the instance, so the ARN is valid. Option C is wrong because even if the DBA's policy does not include an allow for rds:ModifyDBInstance, the default IAM behavior is to deny implicitly, but the question states the DBA cannot modify, which could be due to an implicit deny; however, the most likely cause given the simulation context is an explicit deny, as implicit denies would also prevent the action but are less specific to the simulation output. Option D is wrong because VPC membership does not affect IAM authorization; IAM policies control API access regardless of network location, and the DBA is already able to describe the instance, proving network connectivity is not the issue.

Full explanation →
559
MCQeasy

A company is running an Amazon RDS for MySQL DB instance. The database performance has degraded over time, and the company suspects that slow queries are the cause. Which AWS service should the company use to identify the slow queries and analyze the database performance?

A.AWS Trusted Advisor
B.Amazon RDS Enhanced Monitoring
C.Amazon CloudWatch Logs
D.Amazon RDS Performance Insights
AnswerD

Amazon RDS Performance Insights provides a dashboard to analyze database performance and identify slow queries.

Why this answer

Performance Insights provides a dashboard to analyze database performance and identify slow queries. AWS Trusted Advisor (A) provides best-practice checks, not query analysis. Amazon RDS Enhanced Monitoring (B) provides OS metrics, not query details.

Amazon CloudWatch Logs (C) is for logs, not query analysis.

Full explanation →
560
MCQeasy

A company is running an Amazon RDS for MySQL instance as shown in the exhibit. The application is experiencing high write latency. The instance has a high number of write operations and the storage queue depth is consistently above 100. Which change would most effectively reduce write latency?

A.Modify the storage type to Provisioned IOPS (io1) with 3000 IOPS.
B.Change the instance class to db.m5.xlarge.
C.Enable Multi-AZ to offload writes to a standby.
D.Increase the allocated storage to 200 GB.
AnswerA

Provisioned IOPS provides consistent, low-latency performance.

Why this answer

The instance is experiencing high write latency with a consistently high storage queue depth (above 100), which indicates that the current storage (likely gp2 or magnetic) cannot keep up with the write IOPS demand. Provisioned IOPS (io1) with 3000 IOPS guarantees a dedicated level of IOPS, reducing queue depth and write latency by ensuring the storage subsystem can handle the write workload without throttling.

Exam trap

The trap here is that candidates often confuse Multi-AZ replication as a way to distribute write load, but in reality, Multi-AZ only handles failover and read replicas for reads, not writes, and increasing storage or instance size without addressing the IOPS bottleneck will not resolve high queue depth.

How to eliminate wrong answers

Option B is wrong because changing the instance class to db.m5.xlarge improves compute and memory resources but does not address the storage-level bottleneck causing high queue depth and write latency. Option C is wrong because enabling Multi-AZ provides synchronous replication to a standby for high availability and failover, but it does not offload writes; writes must still be committed to the primary instance's storage, so it does not reduce write latency. Option D is wrong because increasing allocated storage to 200 GB may improve baseline IOPS for gp2 (since gp2 IOPS scale with size) but does not guarantee the consistent, high IOPS needed to reduce queue depth; the queue depth above 100 indicates a need for Provisioned IOPS, not just more storage.

Full explanation →
561
MCQmedium

A company uses Amazon RDS for PostgreSQL and needs to ensure that only specific IP addresses can connect to the database. Which configuration should be used?

A.Configure the DB subnet group to allow only specific IP addresses.
B.Set the rds.force_ssl parameter in the DB parameter group.
C.Create an IAM policy that restricts access to the RDS API based on source IP.
D.Modify the VPC security group associated with the DB instance to allow inbound traffic only from specific IP addresses.
AnswerD

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

Why this answer

VPC security groups act as a virtual firewall that controls inbound and outbound traffic to the DB instance. By modifying the security group to allow inbound traffic only from specific IP addresses, you restrict database access to those IPs. Option A is incorrect because DB subnet groups define which subnets the RDS instance can reside in, not IP filtering.

Option B is incorrect because the rds.force_ssl parameter enforces SSL connections, not IP restrictions. Option C is incorrect because IAM policies control permissions to AWS API actions, not network-level access to the database.

Full explanation →
562
MCQmedium

A social media startup is using Amazon ElastiCache for Redis to cache user profiles. The cache currently has a 24-hour TTL. The application experiences a sudden spike in traffic after a celebrity mentions the service, causing the cache to be flooded with requests for uncached profiles. This results in high latency and database load. Which design pattern should the company implement to prevent this in the future?

A.Use a read-through cache with a longer TTL (e.g., 48 hours).
B.Implement a local cache in each application instance to reduce load on the centralized Redis cluster.
C.Use a write-through cache with a longer TTL (e.g., 48 hours).
D.Use a write-through cache with a shorter TTL (e.g., 1 hour).
AnswerB

Local caching reduces the number of requests to Redis and the database, helping to mitigate cache stampedes.

Why this answer

Implementing a local cache (e.g., using a library like Caffeine or Guava) in each application instance reduces the number of requests hitting the centralized Redis cluster during a traffic spike. This pattern, often called a multi-tier or near-cache, absorbs repeated reads for the same uncached profiles locally, preventing cache flooding and database overload without relying solely on Redis TTL adjustments.

Exam trap

The trap here is that candidates often assume extending TTL or changing cache write strategies (write-through vs. read-through) will solve a cache-miss storm, when in fact the core issue is the volume of concurrent misses, which only a local cache or similar request-reduction pattern can mitigate.

How to eliminate wrong answers

Option A is wrong because simply extending the TTL to 48 hours does not prevent the initial flood of requests for uncached profiles; it only keeps cached data longer once it is loaded, but the spike still causes a cache-miss storm. Option C is wrong because a write-through cache with a longer TTL focuses on write consistency and does not address read-side cache misses during a traffic spike; it would also increase write latency unnecessarily. Option D is wrong because a write-through cache with a shorter TTL would evict data faster, exacerbating cache misses and making the flood problem worse, not better.

Full explanation →
563
MCQhard

A company uses Amazon DynamoDB for a session management system. They need to store session data with a TTL of 24 hours. However, they notice that expired items are not being deleted promptly, causing storage costs to increase. What is the most likely cause?

A.The table has insufficient write capacity
B.TTL is not enabled on the table
C.DynamoDB typically deletes expired items within 48 hours
D.The TTL attribute is set as a string instead of a number
AnswerC

TTL deletions are eventually consistent and can take up to 48 hours.

Why this answer

DynamoDB's TTL mechanism typically deletes expired items within 48 hours, not immediately. The service processes TTL deletions as a background process, and while items are marked as expired at the TTL time, actual deletion can be delayed up to 48 hours. This explains why expired session data persists and increases storage costs despite TTL being properly configured.

Exam trap

The trap here is that candidates assume TTL deletions are instantaneous or happen within minutes, but AWS explicitly documents a 48-hour window, making delayed deletion the expected behavior rather than a misconfiguration.

How to eliminate wrong answers

Option A is wrong because write capacity affects throughput for writes, not the timing of TTL-based deletions; TTL deletions consume no write capacity units. Option B is wrong because the question states the company 'needs to store session data with a TTL of 24 hours,' implying TTL is enabled; if TTL were not enabled, no expired items would be deleted at all, not just delayed. Option D is wrong because DynamoDB TTL supports both Number and String data types for the TTL attribute, as long as the value is a Unix epoch timestamp; setting it as a string does not prevent deletion, though it must be a valid epoch value.

Full explanation →
564
MCQeasy

A startup is building a social media analytics application that ingests high-velocity streaming data from multiple sources. The data consists of JSON objects with varying schemas. The application needs to store this data for real-time querying and later batch processing. Which AWS database solution is most cost-effective and scalable for this workload?

A.Amazon ElastiCache for Redis with persistence enabled.
B.Amazon DynamoDB with on-demand capacity.
C.Amazon RDS for MySQL with multiple read replicas.
D.Amazon Redshift with auto-ingest from Kinesis.
AnswerB

DynamoDB handles high-velocity writes and varying schemas, and is cost-effective for unpredictable workloads.

Why this answer

Amazon DynamoDB with on-demand capacity is the most cost-effective and scalable solution for this workload because it is a fully managed NoSQL database that can handle high-velocity streaming data with varying JSON schemas without requiring schema definition or provisioning. Its on-demand capacity mode automatically scales to accommodate unpredictable traffic spikes, making it ideal for real-time querying and batch processing via features like DynamoDB Streams and integration with AWS Glue or EMR.

Exam trap

The trap here is that candidates often choose Amazon Redshift (Option D) because they associate streaming data with data warehousing, but Redshift is optimized for batch analytics on structured data, not for real-time ingestion and querying of schema-less JSON, making DynamoDB the correct choice for this specific workload.

How to eliminate wrong answers

Option A is wrong because Amazon ElastiCache for Redis is an in-memory cache, not a durable primary data store; while persistence can be enabled, it is not designed for long-term storage of high-velocity streaming data with varying schemas and would be cost-prohibitive for large datasets. Option C is wrong because Amazon RDS for MySQL requires a fixed schema, which cannot handle JSON objects with varying schemas efficiently, and its read replicas do not address the write scalability needed for high-velocity ingestion. Option D is wrong because Amazon Redshift is a columnar data warehouse optimized for analytical queries on structured data, not for real-time querying of raw JSON with varying schemas; auto-ingest from Kinesis adds latency and cost, and Redshift is not designed for high-frequency point lookups or schema-less data.

Full explanation →
565
Multi-Selecthard

A company is using Amazon Redshift for analytics. The security team wants to audit all SQL queries executed against the database, including the actual query text, for compliance. They also want to ensure that the audit logs are stored in a secure, immutable location. Which THREE services or features should they use together to meet these requirements?

Select 3 answers
A.Redshift Audit Logging
B.VPC Flow Logs
C.Amazon CloudWatch Logs
D.Redshift Spectrum
E.S3 Object Lock
AnswersA, C, E

Captures SQL query logs.

Why this answer

Options A, C, and E are correct. Redshift Audit Logging captures SQL queries. CloudWatch Logs can be used as a destination for audit logs (via streaming).

Then, CloudWatch Logs can export logs to S3, and S3 Object Lock provides immutability. Option B is for performance, not auditing. Option D is for network monitoring.

Full explanation →
566
MCQeasy

A startup wants to store session data for a web application. Each session is small (under 1 KB) and accessed frequently with low latency. The data can be ephemeral and does not require complex queries. Which AWS database service is most suitable?

A.Amazon DynamoDB
B.Amazon RDS for PostgreSQL
C.Amazon Neptune
D.Amazon ElastiCache for Redis
AnswerD

In-memory key-value store with sub-millisecond latency, ideal for session management.

Why this answer

Amazon ElastiCache for Redis is the most suitable choice because it is an in-memory data store that provides sub-millisecond latency for frequent reads and writes, ideal for ephemeral session data under 1 KB. Redis supports key-value storage with built-in time-to-live (TTL) expiration, which automatically removes stale sessions without additional application logic, and its simple data model avoids the overhead of complex queries.

Exam trap

The trap here is that candidates often choose DynamoDB (Option A) because they associate it with 'NoSQL' and 'low latency,' but they overlook that ElastiCache for Redis is purpose-built for ephemeral, in-memory caching with even lower latency and automatic eviction, which is the optimal fit for session data.

How to eliminate wrong answers

Option A is wrong because Amazon DynamoDB, while fast and scalable, is a fully managed NoSQL database that persists data to disk and incurs higher latency than an in-memory cache for sub-1 KB session data; it also requires provisioned throughput and lacks native TTL-based eviction for ephemeral use cases. Option B is wrong because Amazon RDS for PostgreSQL is a relational database with ACID compliance and disk-based storage, introducing unnecessary overhead, higher latency, and complex query capabilities that are not needed for simple session key-value lookups. Option C is wrong because Amazon Neptune is a graph database designed for highly connected data and complex graph queries (e.g., social networks, recommendation engines), which is overkill and misaligned for storing small, ephemeral session blobs.

Full explanation →
567
MCQhard

A financial services company is migrating a 500 GB Oracle database to Amazon Aurora MySQL-Compatible Edition. The migration must have minimal downtime and support bidirectional replication for a test-and-cutover approach. The company currently uses Oracle GoldenGate for replication. Which migration strategy should be used?

A.Use AWS DMS with a full load and ongoing replication from Oracle to Aurora MySQL, then cut over.
B.Use AWS Schema Conversion Tool (SCT) to convert the schema and then use AWS DMS for a one-time full load.
C.Use AWS DMS with Oracle GoldenGate as a source for continuous replication to Aurora MySQL.
D.Use Oracle GoldenGate to replicate directly to Aurora MySQL, then cut over.
AnswerA

DMS supports ongoing replication for cross-engine migrations, enabling minimal downtime and test-and-cutover.

Why this answer

AWS DMS with full load and ongoing replication is the correct strategy because it supports minimal-downtime migration from Oracle to Aurora MySQL and can handle bidirectional replication for a test-and-cutover approach. DMS uses change data capture (CDC) from Oracle's redo logs to keep the target in sync, allowing you to validate the target before cutting over. This meets the requirement for minimal downtime and a test-and-cutover workflow without relying on Oracle GoldenGate.

Exam trap

The trap here is that candidates assume Oracle GoldenGate, which the company already uses, must be part of the migration strategy, but AWS DMS can directly handle the replication without GoldenGate, and GoldenGate does not support Aurora MySQL as a target.

How to eliminate wrong answers

Option B is wrong because AWS Schema Conversion Tool (SCT) only converts schema and a one-time full load does not provide ongoing replication or bidirectional support, so it cannot achieve minimal downtime. Option C is wrong because AWS DMS cannot use Oracle GoldenGate as a source for continuous replication; DMS uses its own CDC engine based on Oracle redo logs, not GoldenGate. Option D is wrong because Oracle GoldenGate does not natively support direct replication to Amazon Aurora MySQL-Compatible Edition as a target; GoldenGate requires a supported target database, and Aurora MySQL is not a certified GoldenGate target.

Full explanation →
568
MCQeasy

A company wants to migrate an on-premises MongoDB database to Amazon DocumentDB. The migration must be performed with minimal downtime and should support live data synchronization. Which AWS service should be used?

A.AWS Glue with streaming ETL jobs.
B.AWS Data Pipeline to schedule periodic data loads.
C.AWS S3 with AWS Lambda functions to sync data.
D.AWS Database Migration Service (DMS) with ongoing replication.
AnswerD

DMS can connect to MongoDB as source and DocumentDB as target with change data capture for minimal downtime.

Why this answer

AWS Database Migration Service (DMS) with ongoing replication is the correct choice because it supports continuous change data capture (CDC) from a source MongoDB database to Amazon DocumentDB, enabling live data synchronization with minimal downtime. DMS can perform a full load followed by ongoing replication using the MongoDB oplog to capture and apply changes in near real-time, which meets the requirement for minimal downtime during migration.

Exam trap

The trap here is that candidates may confuse AWS DMS's ongoing replication with batch-oriented services like Glue or Data Pipeline, assuming any 'sync' tool works, but only DMS provides native CDC from MongoDB's oplog for live migration to DocumentDB.

How to eliminate wrong answers

Option A is wrong because AWS Glue with streaming ETL jobs is designed for batch and stream processing of data for analytics, not for live database migration with minimal downtime; it lacks native support for capturing ongoing changes from a MongoDB oplog and applying them to DocumentDB. Option B is wrong because AWS Data Pipeline schedules periodic data loads, which introduces downtime between loads and cannot provide live synchronization or capture real-time changes. Option C is wrong because AWS S3 with AWS Lambda functions to sync data would require custom code to poll for changes and handle conflict resolution, and it cannot reliably replicate MongoDB's oplog-based CDC, leading to data inconsistency and higher latency.

Full explanation →
569
Multi-Selecthard

A company is migrating a 5 TB MySQL database to Amazon Aurora MySQL. The migration must have minimal downtime and support ongoing replication. The source database is in a corporate data center with a 500 Mbps internet connection. Which THREE steps should the database specialist take?

Select 3 answers
A.Establish a VPN connection to AWS and use mysqldump.
B.Use AWS Schema Conversion Tool (AWS SCT) to convert the schema.
C.Create an Aurora read replica from the source database.
D.Set up AWS Database Migration Service (AWS DMS) with change data capture (CDC).
E.Use an AWS Snowball Edge device to transfer the initial data load.
AnswersB, D, E

SCT helps with schema conversion if needed.

Why this answer

Options B, D, and E are correct. Option B: The AWS Schema Conversion Tool (AWS SCT) converts the source MySQL schema to be compatible with Aurora MySQL, which is often necessary due to differences in storage engines or features. Option D: AWS Database Migration Service (AWS DMS) with change data capture (CDC) enables ongoing replication and minimizes downtime by continuously syncing changes from the source database to the target Aurora database.

Option E: An AWS Snowball Edge device is used for the initial data load, which transfers the 5 TB database over a fast local connection to AWS, reducing the time required compared to the 500 Mbps internet connection. Option A is incorrect because using a VPN and mysqldump would result in significant downtime and slower data transfer; it does not support ongoing replication. Option C is incorrect because creating an Aurora read replica is not a migration method; read replicas are used for read scaling within a managed Aurora environment, not for migrating data from an external source.

Full explanation →
570
MCQhard

Refer to the exhibit. A DynamoDB table has a primary key of pk (partition key) and sk (sort key). An application needs to perform GetItem and Query operations but should only be allowed to retrieve the pk and sk attributes. The IAM policy above is applied to the application's IAM role. Why does the policy fail to achieve the goal?

A.The Deny statement uses the wrong condition key; it should use 'dynamodb:Select' instead of 'dynamodb:Attributes'.
B.The policy should use 'dynamodb:ReturnValues' condition key.
C.The Deny statement does not prevent retrieval of all attributes when no ProjectionExpression is specified.
D.The Allow statement should include 'dynamodb:Scan' to allow Query operations.
AnswerC

If the request does not specify attributes, the condition has no values to compare, so the Deny is not applied, allowing full access.

Why this answer

The Deny statement only denies access when the request includes a ProjectionExpression that specifies attributes other than pk and sk. However, if the application performs a GetItem or Query without specifying any ProjectionExpression, DynamoDB returns all attributes by default, which violates the requirement to restrict retrieval to only pk and sk. The policy does not block this default behavior, so it fails to achieve the goal.

Exam trap

The trap here is that candidates assume a Deny on specific attributes will block all access to those attributes, but they overlook that DynamoDB returns all attributes by default when no ProjectionExpression is specified, making the Deny ineffective unless a ProjectionExpression is always provided.

How to eliminate wrong answers

Option A is wrong because 'dynamodb:Attributes' is a valid condition key for restricting attribute access in DynamoDB policies, and 'dynamodb:Select' is used for Select parameters in Scan/Query, not for attribute-level restrictions. Option B is wrong because 'dynamodb:ReturnValues' is a condition key that controls the return values of write operations like PutItem or UpdateItem, not read operations like GetItem or Query. Option D is wrong because Query operations do not require the 'dynamodb:Scan' action; Query is a separate API action that must be allowed explicitly via 'dynamodb:Query', and the Allow statement already includes 'dynamodb:Query'.

Full explanation →
571
MCQhard

A company uses Amazon DynamoDB with auto scaling enabled. They notice that a table's write capacity is frequently throttled during a specific hour each day. The access pattern is uniform across partitions. Which action would resolve the throttling without manual intervention?

A.Enable DynamoDB Accelerator (DAX) to cache writes.
B.Disable auto scaling and set a fixed higher capacity.
C.Create a larger number of partitions by splitting the table.
D.Increase the minimum provisioned capacity in auto scaling.
AnswerD

Correct. Setting a higher minimum ensures enough capacity during the spike, and auto scaling can scale up further if needed.

Why this answer

When DynamoDB auto scaling is enabled, throttling can occur if the table's write capacity demand spikes faster than auto scaling can increase capacity. The 'minimum provisioned capacity' in the auto scaling policy sets a floor for the capacity units. By increasing this minimum, the table starts with a higher base capacity, reducing the likelihood of throttling during predictable peak hours.

Option A is incorrect because DAX is a caching layer for reads, not writes. Option B is incorrect because disabling auto scaling requires manual intervention. Option C is incorrect because table partitioning in DynamoDB is managed automatically based on provisioned capacity; you cannot manually split partitions.

Full explanation →
572
MCQhard

Refer to the exhibit. A company is creating an IAM policy for a migration engineer who needs to perform a database migration from an on-premises SQL Server to Amazon RDS for SQL Server using AWS DMS. The policy is attached to the engineer's IAM user. Which additional permission is required for the engineer to create a DMS replication instance?

A.kms:CreateKey
B.dms:CreateEndpoint
C.s3:PutObject
D.ec2:CreateInstance (or equivalent)
AnswerD

DMS replication instances require EC2 permissions to be created.

Why this answer

To create a DMS replication instance, AWS DMS launches an Amazon EC2 instance in the customer's VPC to run the replication engine. The IAM user must have permission to call ec2:RunInstances (or the equivalent ec2:CreateInstance action) to provision this underlying compute resource. Without this EC2 permission, the DMS service cannot spin up the replication instance, and the migration engineer will receive an authorization error.

Exam trap

The trap here is that candidates assume DMS replication instances are fully managed by AWS and require no EC2 permissions, but in reality DMS provisions EC2 instances in the customer's account, making ec2:RunInstances a mandatory permission.

How to eliminate wrong answers

Option A is wrong because kms:CreateKey is used to create a new AWS KMS key for encryption, but DMS can use an existing KMS key or the default AWS managed key; the engineer does not need to create a new key. Option B is wrong because dms:CreateEndpoint is a separate permission for creating source or target endpoints, not for creating the replication instance itself. Option C is wrong because s3:PutObject is required only if the migration involves storing task logs or using S3 as a target, but it is not needed to provision the replication instance.

Full explanation →
573
MCQmedium

A company uses Amazon ElastiCache for Redis and needs to encrypt data in transit between the application and the cache cluster. Which feature should be enabled?

A.Enable encryption in transit on the replication group.
B.Enable encryption at rest.
C.Use AWS KMS customer master keys.
D.Configure the VPC security group to allow only HTTPS traffic.
AnswerA

ElastiCache for Redis supports TLS encryption for data in transit.

Why this answer

Amazon ElastiCache for Redis supports encryption in transit using TLS (Transport Layer Security) when enabled on the replication group. This ensures data is encrypted between the application and the cache cluster. Option B is incorrect because encryption at rest protects data stored on disk, not data in transit.

Option C is incorrect because AWS KMS customer master keys are used for encryption at rest, not for transit encryption. Option D is incorrect because VPC security groups control network access at the IP/port level, not encryption; allowing HTTPS traffic does not enable encryption for ElastiCache communication.

Full explanation →
574
MCQhard

A company runs a time-series application that collects sensor data from millions of IoT devices. The data is written in batches every minute and queried to generate hourly, daily, and monthly aggregates. The database must support high ingestion rates and efficient storage. Which database service is most appropriate?

A.Amazon DynamoDB with TTL
B.Amazon Redshift
C.Amazon RDS for PostgreSQL
D.Amazon Timestream
AnswerD

Timestream is purpose-built for time-series data, with automatic storage tiering and aggregate functions.

Why this answer

Amazon Timestream is purpose-built for time-series data, offering a serverless architecture that automatically scales to handle high ingestion rates from millions of IoT devices. It optimizes storage by separating recent data (in memory) from historical data (in a magnetic store), and its built-in aggregation functions (e.g., `BIN`, `DATE_BIN`) efficiently compute hourly, daily, and monthly aggregates without manual partitioning or indexing.

Exam trap

The trap here is that candidates often choose Amazon DynamoDB for high ingestion rates, overlooking that time-series workloads require efficient time-based aggregation and storage optimization, which DynamoDB lacks, while Timestream is the only AWS service purpose-built for this exact use case.

How to eliminate wrong answers

Option A is wrong because Amazon DynamoDB with TTL is a key-value and document database optimized for low-latency lookups, not for time-series analytics; TTL only expires old data but does not provide native time-based aggregation or efficient range scans over time intervals. Option B is wrong because Amazon Redshift is a columnar data warehouse designed for complex analytical queries on structured data, but its high overhead for small, frequent batch writes (every minute) and lack of native time-series optimization make it unsuitable for high-ingestion IoT workloads. Option C is wrong because Amazon RDS for PostgreSQL is a relational database that requires manual schema design, indexing, and partitioning to handle time-series data, and it cannot match the ingestion throughput or storage efficiency of a purpose-built time-series engine.

Full explanation →
575
MCQhard

A company is running an Oracle database on Amazon RDS with the configuration shown in the exhibit. The application is experiencing high latency for write operations. The storage is consistently showing high queue depth and write latency. Which change will most improve write performance?

A.Increase allocated storage to 1,000 GB to get higher gp2 baseline IOPS.
B.Enable storage auto scaling and increase storage throughput to 500 MB/s.
C.Change the DB instance class to db.r5.2xlarge.
D.Migrate to io1 or io2 storage with higher provisioned IOPS.
AnswerD

Provisioned IOPS storage provides consistent low-latency performance for write-intensive workloads.

Why this answer

Migrating to io1 or io2 block storage with higher provisioned IOPS directly addresses the root cause of high queue depth and write latency. Unlike gp2, which has a baseline IOPS of 3 per GB (up to 16,000 IOPS at 5,334 GB) and a burst bucket that depletes under sustained load, io1/io2 provide consistent, provisioned IOPS independent of volume size. This ensures the storage subsystem can keep up with the write workload, reducing queue depth and latency.

Exam trap

The trap here is that candidates often assume increasing storage size (Option A) or instance class (Option C) will fix I/O bottlenecks, but the real constraint is the gp2 burst model and insufficient provisioned IOPS for sustained write-heavy workloads.

How to eliminate wrong answers

Option A is wrong because increasing gp2 storage to 1,000 GB only raises baseline IOPS to 3,000 (3 IOPS/GB), which may still be insufficient for the workload, and does not address the burst bucket exhaustion that causes high latency under sustained writes. Option B is wrong because storage auto scaling adjusts volume size automatically, but it does not increase throughput beyond gp2 limits (250 MB/s for volumes up to 1,000 GB), and the problem is IOPS-bound, not throughput-bound; 500 MB/s throughput is not achievable on gp2 without exceeding its maximum of 250 MB/s. Option C is wrong because changing the DB instance class to db.r5.2xlarge improves CPU and memory but does not affect the storage layer's IOPS or queue depth; the bottleneck is at the EBS volume, not the compute instance.

Full explanation →
576
MCQeasy

A company is migrating an on-premises MySQL database to Amazon RDS for MySQL. The database has a table with 100 million rows. During the migration using AWS DMS, the full load phase is taking too long. What change can speed up the full load?

A.Increase the number of DMS task mappings (parallel threads)
B.Reduce the target RDS instance storage to minimize write latency
C.Increase the size of the source on-premises database instance
D.Disable LOB columns in the task settings
AnswerA

More threads can process data in parallel, speeding up full load.

Why this answer

Increasing the number of DMS task mappings (parallel threads) allows DMS to partition the source table and load multiple segments concurrently, significantly reducing the full load duration. By default, DMS uses a single thread per table; adding parallel threads leverages the source database's read capacity and the target RDS instance's write throughput more efficiently.

Exam trap

The DBS-C01 exam often tests the misconception that increasing source or target instance size is the primary lever for migration speed, when in fact DMS's parallelism settings directly control concurrency and are the most impactful change for full load performance.

How to eliminate wrong answers

Option B is wrong because reducing the target RDS instance storage does not minimize write latency; in fact, smaller storage volumes often have lower IOPS baselines and throughput limits, which can increase write latency and slow the load. Option C is wrong because increasing the size of the source on-premises database instance does not directly affect DMS's extraction speed; DMS reads from the source using SQL queries, and the bottleneck is typically DMS's own parallelism, not the source instance size. Option D is wrong because disabling LOB columns is not a valid DMS task setting; LOB handling can be configured (e.g., limited LOB mode), but disabling them entirely would omit data and is not a recommended method to speed up full load.

Full explanation →
577
MCQhard

A company is using Amazon DynamoDB with global tables (multi-region) for a gaming application. The application writes to a table in us-east-1 and reads from a table in eu-west-1. The team notices that writes in us-east-1 are taking longer than expected to propagate to eu-west-1. What is the most likely cause?

A.Network latency between the regions is causing replication delay
B.The write capacity in eu-west-1 is insufficient
C.Conflicts are being resolved due to concurrent writes
D.DynamoDB Streams is disabled on the table
AnswerA

Global tables use asynchronous replication; inter-region latency adds propagation time.

Why this answer

DynamoDB global tables use asynchronous replication across regions, and network latency between regions is the primary factor causing delays in propagation. Option B is incorrect because read and write capacity is independent per region; insufficient capacity in eu-west-1 does not affect replication speed from us-east-1. Option C is incorrect while conflict resolution can occur with concurrent writes, it does not inherently cause delays; replication delay is due to network latency.

Option D is incorrect because DynamoDB Streams are required for global tables to function; if they were disabled, replication would not occur at all, not just be delayed.

Full explanation →
578
MCQeasy

A company wants to migrate an on-premises Oracle database to Amazon RDS for Oracle with minimal downtime. Which AWS service should be used for the initial data load and ongoing replication?

A.AWS Database Migration Service (DMS)
B.AWS Snowball
C.AWS Direct Connect
D.AWS Schema Conversion Tool (SCT)
AnswerA

DMS supports full load and ongoing replication for minimal downtime.

Why this answer

AWS Database Migration Service (DMS) is the correct choice because it supports both full-load migration and ongoing change data capture (CDC) replication from Oracle to Amazon RDS for Oracle. DMS uses Oracle LogMiner or binary reader to capture incremental changes from the source database's redo logs, enabling near-zero downtime migration without requiring application modifications.

Exam trap

The trap here is that candidates often confuse AWS Snowball (a bulk data transfer appliance) with a migration tool suitable for minimal-downtime scenarios, failing to recognize that Snowball cannot handle ongoing replication or CDC from a live database.

How to eliminate wrong answers

Option B (AWS Snowball) is wrong because Snowball is a physical data transfer device for large-scale offline data movement, not a tool for ongoing replication or minimal-downtime migration; it cannot capture and apply continuous changes from an active Oracle database. Option C (AWS Direct Connect) is wrong because Direct Connect establishes a dedicated network connection between on-premises and AWS, but it does not perform data migration or replication itself; it only provides the network transport layer. Option D (AWS Schema Conversion Tool) is wrong because SCT is used to convert database schema and code from one engine to another (e.g., Oracle to PostgreSQL), not to migrate data or perform ongoing replication; it does not handle data movement or CDC.

Full explanation →
579
MCQmedium

Refer to the exhibit. A DBA deploys this CloudFormation stack. After creation, the DBA needs to enable deletion protection for the DB instance. Which modification to the template would accomplish this?

A.Add 'DeletionProtection': true to the Properties.
B.Add 'DeletionPolicy: Retain' to the resource.
C.Add 'PreventRDSDeletion: true' to the Properties.
D.Add 'DeletionProtection': 'Enabled' to the Properties.
AnswerA

Correct property to enable deletion protection.

Why this answer

Adding 'DeletionProtection': true to the Properties of the AWS::RDS::DBInstance resource enables deletion protection for that DB instance. Option B is incorrect because 'DeletionPolicy: Retain' is a CloudFormation resource-level attribute that retains the resource when the stack is deleted, but it does not prevent deletion of the resource outside of CloudFormation. Option C is incorrect because 'PreventRDSDeletion' is not a valid property for RDS.

Option D is incorrect because the correct syntax is 'DeletionProtection': true, not 'Enabled'.

Full explanation →
580
Multi-Selectmedium

A company is migrating a large Oracle data warehouse to Amazon Redshift. Which THREE design considerations are important for performance optimization?

Select 3 answers
A.Choose appropriate distribution styles (KEY, ALL, EVEN).
B.Use compression encodings for columns.
C.Define sort keys for commonly filtered columns.
D.Enable cross-Region replication for data locality.
E.Use row-based storage for fact tables.
AnswersA, B, C

Affects data distribution across nodes.

Why this answer

Distribution styles (KEY, ALL, EVEN) control how data is distributed across compute nodes in Amazon Redshift. Choosing the right distribution style minimizes data movement during joins and aggregations, which is critical for performance in a large data warehouse migration from Oracle. For example, using KEY distribution on a frequently joined column keeps related rows on the same node, reducing network traffic.

Exam trap

The trap here is that candidates may confuse disaster recovery features (like cross-Region replication) with performance design choices, or mistakenly think row-based storage applies to Redshift because of their Oracle background, where row storage is the norm.

Full explanation →
581
MCQhard

A financial services company is using Amazon DynamoDB to store customer transaction data. The compliance team requires that all data be encrypted at rest using a customer-managed AWS KMS key, and that access to the key be auditable via AWS CloudTrail. Additionally, the security team wants to ensure that DynamoDB can only be accessed from within the VPC using a VPC endpoint. Which combination of steps should the company take to meet these requirements?

A.Use a CloudHSM key for encryption and create a VPC peering connection to DynamoDB.
B.Enable server-side encryption with a customer-managed KMS key and route all traffic through a NAT gateway.
C.Enable server-side encryption with a customer-managed KMS key and create an interface VPC endpoint for DynamoDB.
D.Use AWS managed KMS key and a gateway VPC endpoint for DynamoDB.
AnswerC

Correct. Customer-managed KMS key meets encryption requirement; interface VPC endpoint ensures private VPC-only access.

Why this answer

Option C. To meet the encryption requirement, the company must use a customer-managed KMS key, not an AWS managed key. For VPC-only access, DynamoDB supports interface VPC endpoints (via AWS PrivateLink), which provide private connectivity.

Option C correctly uses a customer-managed KMS key and an interface VPC endpoint. Option D is incorrect because it uses an AWS managed KMS key. Option A is incorrect because CloudHSM is unnecessary and VPC peering is not the correct method.

Option B is incorrect because a NAT gateway still routes traffic over the internet.

Exam trap

A common trap is assuming that DynamoDB only supports gateway VPC endpoints. DynamoDB also supports interface VPC endpoints, providing private connectivity within the VPC. Also, ensure the KMS key is customer-managed for full control and auditing.

Full explanation →
582
MCQmedium

A company wants to run a MongoDB-compatible database on AWS with automated patching and backups. Which service should they choose?

A.Amazon DocumentDB (with MongoDB compatibility)
B.Self-managed MongoDB on Amazon EC2
C.Amazon DynamoDB
D.Amazon RDS for MySQL
AnswerA

Fully managed MongoDB-compatible database.

Why this answer

Amazon DocumentDB with MongoDB compatibility is the correct choice because it is a fully managed, MongoDB-compatible database service that provides automated patching and backups. It supports the MongoDB wire protocol and drivers, allowing existing MongoDB applications to migrate with minimal changes while offloading administrative tasks like patching, backup, and replication to AWS.

Exam trap

The trap here is that candidates may confuse Amazon DocumentDB's MongoDB compatibility with full feature parity, but DocumentDB does not support all MongoDB features (e.g., some aggregation pipeline stages or change streams), so the exam expects you to recognize it as the only managed MongoDB-compatible option with automated patching and backups.

How to eliminate wrong answers

Option B is wrong because self-managed MongoDB on Amazon EC2 requires the company to manually handle patching, backups, and operational overhead, which contradicts the requirement for automated patching and backups. Option C is wrong because Amazon DynamoDB is a key-value and document database that is not MongoDB-compatible; it uses a different API and data model, so it cannot run MongoDB workloads. Option D is wrong because Amazon RDS for MySQL is a relational database that does not support the MongoDB wire protocol or document model, making it incompatible with MongoDB-based applications.

Full explanation →
583
MCQhard

A company is migrating a 3 TB SQL Server database to Amazon RDS for SQL Server. The migration must be completed with less than 15 minutes of downtime. The company has set up AWS DMS with ongoing replication. During the final cutover, the DMS task fails with a 'Target error: Cannot insert duplicate key' error. What is the most likely cause?

A.The target table has an identity column that is not set to allow insert of explicit values.
B.The DMS task is not set to truncate the target before loading.
C.The source database transaction log is full.
D.The source database has a primary key violation.
AnswerA

If identity insert is off, DMS cannot insert source values, causing duplicate key errors.

Why this answer

The error 'Cannot insert duplicate key' during DMS full load or ongoing replication indicates that the target table has an identity column that does not allow explicit value insertion. By default, SQL Server identity columns are set to NOT FOR REPLICATION = OFF, meaning DMS cannot insert rows with explicit identity values, causing duplicate key violations when the source has identity values that conflict with the target's auto-generated sequence. To resolve this, you must set the identity column to allow explicit inserts (e.g., SET IDENTITY_INSERT ON) or configure DMS to use 'Do nothing' for identity handling.

Exam trap

The trap here is that candidates often confuse a target-side duplicate key error with a source-side primary key violation or think that truncating the target will fix all replication issues, but the real cause is the identity column's explicit insert restriction specific to SQL Server.

How to eliminate wrong answers

Option B is wrong because truncating the target before loading would not prevent duplicate key errors during ongoing replication; it only affects the initial full load, and the error occurs during the final cutover when replication is already running. Option C is wrong because a full transaction log on the source would cause a different error (e.g., 'Log full' or 'Cannot continue because log is full'), not a duplicate key violation on the target. Option D is wrong because a primary key violation on the source would be a source-side error, not a target-side 'Cannot insert duplicate key' error; DMS would report a source error, not a target constraint violation.

Full explanation →
584
Multi-Selecthard

A company is deploying a new Amazon RDS for Oracle instance and needs to ensure high availability and automatic failover. Which configuration should be used?

Select 1 answer
A.Read Replicas in a different Region
B.Multi-AZ deployment
C.Enhanced Monitoring
D.Automated backups with point-in-time recovery
E.Deletion protection
AnswersB

Correct. Multi-AZ creates a standby replica in a different AZ for automatic failover.

Why this answer

(Multi-AZ deployment) is correct because it provisions a synchronous standby replica in a different Availability Zone, enabling automatic failover without data loss when the primary instance fails. This meets the high availability and automatic failover requirements for Amazon RDS for Oracle. Option D (Automated backups with point-in-time recovery) provides backup and recovery capabilities but does not ensure high availability or automatic failover; it is not a configuration for automatic failover.

Exam trap

The trap here is that candidates often confuse Read Replicas with Multi-AZ, assuming read replicas provide automatic failover, but they require manual promotion and do not offer synchronous replication or automatic failover.

Full explanation →
585
Multi-Selectmedium

Which THREE considerations are important when designing a database for a global, multi-Region application using Amazon DynamoDB Global Tables? (Select THREE.)

Select 3 answers
A.Using a single write region to avoid conflicts
B.Strongly consistent reads across regions
C.Application tolerance for eventually consistent reads
D.Provisioning sufficient write capacity in each region
E.Conflict resolution using last writer wins
AnswersC, D, E

Cross-region replication is asynchronous; reads may be eventually consistent.

Why this answer

Amazon DynamoDB Global Tables use an eventually consistent model for cross-region replication. Applications must tolerate eventual consistency, as updates made in one region are propagated to other regions asynchronously, typically within one second or less. This design trade-off enables high availability and low-latency writes across multiple regions.

Exam trap

The DBS-C01 exam often tests the misconception that Global Tables require a single write region or support cross-region strongly consistent reads, when in fact they are designed for multi-region writes with eventual consistency and automatic conflict resolution via last writer wins.

Full explanation →
586
MCQeasy

A company needs to store and query JSON documents that have varying attributes. The workload is read-heavy, and the team wants to use SQL-like queries. Which service is most appropriate?

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

DocumentDB is a document database that natively stores JSON and supports MongoDB queries.

Why this answer

Amazon DocumentDB (with MongoDB compatibility) is the most appropriate service because it is purpose-built for storing and querying JSON documents with varying attributes, and it supports SQL-like queries via MongoDB's query language, which is familiar to developers. As a read-heavy workload, DocumentDB's architecture with distributed storage and read replicas provides high throughput and low latency for such patterns. The flexible schema of DocumentDB naturally handles documents with differing attributes without requiring schema migrations.

Exam trap

The trap here is that candidates often choose DynamoDB with PartiQL because it supports SQL-like queries and JSON, but they overlook that DocumentDB is specifically designed for document workloads with flexible schemas and richer query capabilities, while DynamoDB is optimized for key-value access patterns and requires careful design for varying attributes.

How to eliminate wrong answers

Option A is wrong because Amazon Neptune is a graph database designed for highly connected data (e.g., social networks, recommendation engines), not for storing and querying JSON documents with varying attributes using SQL-like queries. Option C is wrong because Amazon DynamoDB with PartiQL is a NoSQL key-value and document database that supports SQL-compatible queries, but it is optimized for high-scale, low-latency workloads with a fixed primary key schema; while it can store JSON, it does not natively support rich querying on varying attributes without secondary indexes and is less suited for complex SQL-like joins or aggregations compared to a document database. Option D is wrong because Amazon RDS for MySQL with JSON data type requires a fixed relational schema for the table structure, and while it can store JSON in a column, it does not provide the flexible schema or native document querying capabilities that DocumentDB offers; querying varying attributes often involves complex JSON functions and can lead to performance issues in read-heavy workloads.

Full explanation →
587
Multi-Selectmedium

A company is using Amazon Redshift and needs to encrypt data at rest with a customer-managed key. Which TWO steps are required to enable encryption with a customer-managed AWS KMS key?

Select 2 answers
A.Create a customer-managed key in AWS KMS.
B.Specify the KMS key when creating the Redshift cluster.
C.Use the AWS CLI to modify the cluster to enable encryption.
D.Configure the Redshift cluster to use an HSM.
E.Request AWS Support to enable encryption.
AnswersA, B

Correct. A customer-managed KMS key must be created to control access to the encryption key.

Why this answer

Options A and B are correct. To enable encryption with a customer-managed AWS KMS key, you must first create a customer-managed KMS key (A) and then specify that KMS key when creating a new Redshift cluster (B). Option C is incorrect because you cannot modify an existing unencrypted Redshift cluster to enable encryption; encryption must be set at cluster creation or by restoring from an encrypted snapshot.

Option D is incorrect because Redshift encryption with KMS does not require an HSM. Option E is incorrect because AWS Support cannot enable encryption; it must be performed by the customer via the AWS Management Console, CLI, or API.

Exam trap

Encryption must be enabled at cluster creation or by restoring from an encrypted snapshot; it cannot be added to an existing unencrypted cluster.

Full explanation →
588
MCQmedium

A company runs a MySQL database on Amazon RDS. They need to export a subset of data to Amazon S3 for analysis using Amazon Athena. The data is stored in multiple tables with complex joins. What is the MOST efficient way to export the data?

A.Use the COPY command from Amazon Redshift to pull data from RDS.
B.Use the SELECT INTO OUTFILE S3 extension to export the query results directly to S3.
C.Use the AWS Schema Conversion Tool (SCT) to extract the data to S3.
D.Use mysqldump to export the tables and then upload the files to S3.
AnswerB

This RDS feature allows exporting arbitrary SQL query results to S3.

Why this answer

Amazon RDS for MySQL supports the `SELECT INTO OUTFILE S3` extension, which allows you to export the results of complex queries (including joins) directly to Amazon S3 in a single, efficient operation. This avoids the overhead of intermediate staging or manual file transfers, making it the most efficient method for exporting a subset of data to S3 for Athena analysis.

Exam trap

The trap here is that candidates often confuse the AWS Schema Conversion Tool (SCT) as a data extraction tool, when it is actually designed for schema assessment and conversion, not for efficient query-based data export to S3.

How to eliminate wrong answers

Option A is wrong because the COPY command is an Amazon Redshift feature for loading data into Redshift, not for exporting from RDS; it cannot pull data from RDS directly. Option C is wrong because the AWS Schema Conversion Tool (SCT) is designed for schema conversion and heterogeneous database migrations, not for efficient ad-hoc data export of query results to S3. Option D is wrong because mysqldump exports entire tables or databases in a serialized format (SQL or delimited text) that requires manual upload to S3, and it cannot handle complex joins or subsetting efficiently, making it far less efficient than the native S3 export extension.

Full explanation →
589
MCQeasy

A company is migrating an on-premises Oracle database to Amazon RDS for Oracle. The database is 500 GB and has a 4-hour downtime window. Which AWS service should be used to perform the migration with minimal downtime?

A.AWS Database Migration Service (DMS)
B.Oracle Data Pump using Amazon RDS option groups
C.AWS Snowball Edge
D.Amazon S3 Transfer Acceleration
AnswerA

DMS can perform a one-time full load and then ongoing replication to minimize downtime.

Why this answer

AWS Database Migration Service (DMS) is the correct choice because it supports ongoing replication (change data capture) to minimize downtime during the migration. Oracle Data Pump is an export/import tool that requires downtime and cannot perform continuous replication. AWS Snowball Edge is for offline bulk data transfer, not suitable for a 4-hour window with minimal downtime.

Amazon S3 Transfer Acceleration only speeds up uploads to S3, not a database migration tool. Therefore, DMS is the recommended service for minimal downtime migration.

Full explanation →
590
MCQmedium

A company is migrating a 1 TB Microsoft SQL Server database to Amazon RDS for SQL Server using native backup and restore. The DBA has taken a full backup of the source database and uploaded it to an S3 bucket. The DBA then uses the 'aws rds restore-db-instance-from-db-snapshot' CLI command to restore the backup to an RDS instance. The restore fails with an error: 'Could not find the specified backup file in the S3 bucket.' The DBA verifies that the backup file exists in the correct S3 bucket and that the IAM role used by RDS has permissions to access the bucket. What is the most likely cause of the failure?

A.The backup file has an incorrect file extension, such as .trn instead of .bak.
B.The backup file is not encrypted with AWS KMS.
C.The RDS instance is not configured to use native restore.
D.The backup file is not placed in the correct S3 bucket path that includes the account ID prefix.
AnswerD

RDS expects the backup file to be in a path like 'bucket-name/account-id/filename'. If missing, it cannot find the file.

Why this answer

When restoring a native backup from S3 to RDS, the backup file must be stored in a path that includes the AWS account ID as a prefix (e.g., /123456789012/backup.bak). RDS for SQL Server expects this specific path structure to locate the file. Even if the file exists in the bucket and permissions are correct, a wrong path causes the 'Could not find the specified backup file' error.

Option A is wrong because the file extension is not the issue; RDS accepts .bak and .trn files. Option B is wrong because encryption is not required for native restore. Option C is wrong because native restore is a supported feature on RDS for SQL Server; the error is about file location, not configuration.

Full explanation →
591
MCQmedium

A gaming company uses Amazon DynamoDB with provisioned capacity. During a new game launch, the read activity spikes and some requests receive 'ProvisionedThroughputExceededException' errors. The operations team needs to monitor read throttling in real-time. Which CloudWatch metric should they create an alarm for?

A.ReadLatency
B.ConsumedReadCapacityUnits
C.ReadThrottleEvents
D.ThrottledRequests
AnswerC

This metric specifically counts throttled read requests.

Why this answer

(ReadThrottleEvents) because this metric directly counts the number of read requests that are throttled due to exceeding provisioned read capacity. Option B (ConsumedReadCapacityUnits) shows the amount of read capacity used, not throttled events. Option D (ThrottledRequests) includes both read and write throttles, so it is not specific to reads.

Option A (ReadLatency) measures response time, not throttling.

Full explanation →
592
Multi-Selecteasy

A company is migrating an on-premises MySQL database to Amazon RDS for MySQL. The database is 500 GB and the migration must have minimal downtime. Which TWO AWS services should be used together to accomplish this?

Select 2 answers
A.AWS App2Container
B.AWS CloudFormation
C.AWS DataSync
D.AWS Database Migration Service (AWS DMS)
E.AWS Schema Conversion Tool (AWS SCT)
AnswersD, E

AWS DMS handles data migration with minimal downtime.

Why this answer

AWS DMS (Option D) is correct because it is purpose-built for migrating databases with minimal downtime by continuously replicating changes from the source to the target using change data capture (CDC). AWS SCT (Option E) is correct because it assesses the source MySQL schema and automatically converts any incompatible objects (e.g., storage engines, functions) to be compatible with Amazon RDS for MySQL, ensuring a smooth migration. Together, they enable a near-zero-downtime migration by first performing a full load via DMS and then applying ongoing CDC replication until cutover.

Exam trap

The trap here is that candidates may confuse AWS DataSync (a file-transfer service) with a database migration tool, or assume CloudFormation can handle data migration because it can deploy RDS instances, but neither supports live database replication or schema conversion.

Full explanation →
593
MCQeasy

A company runs a production Amazon DynamoDB table with on-demand capacity. The table stores session data for a web application. Recently, users have reported occasional slow response times. The operations team notices that the table's ConsumedWriteCapacityUnits metric shows occasional spikes that exceed the provisioned throughput (though on-demand auto-scales), and ThrottledWriteEvents metrics show occasional throttling. The application uses the AWS SDK with default retry logic. The database specialist is asked to investigate. Upon reviewing the table configuration, the specialist finds that the table has a simple primary key (partition key only) and the data access pattern is heavily skewed toward a small number of partition keys. The application writes in batches of 25 items using the BatchWriteItem API. What should the specialist recommend to reduce throttling and improve performance?

A.Implement write sharding by adding a random suffix to the partition key to distribute writes more evenly.
B.Increase the provisioned read capacity units to handle the load.
C.Switch the table to provisioned capacity mode and increase write capacity.
D.Enable DynamoDB Accelerator (DAX) to cache write operations.
AnswerA

Write sharding spreads writes across multiple partitions, reducing throttling.

Why this answer

The throttling is caused by a hot partition: the table uses a single partition key, and writes are heavily skewed toward a few keys. By adding a random suffix to the partition key (write sharding), the writes are distributed evenly across all partitions, eliminating hot spots and reducing throttling. The on-demand capacity mode already handles overall throughput, but it cannot prevent throttling on individual partitions when access is skewed.

Exam trap

The trap here is that candidates assume on-demand capacity mode eliminates all throttling, but it only manages total table throughput, not per-partition limits, so hot keys still cause throttling.

How to eliminate wrong answers

Option B is wrong because increasing read capacity units does not address write throttling; the issue is with write operations, not reads. Option C is wrong because switching to provisioned capacity and increasing write capacity does not solve the hot partition problem; even with higher provisioned capacity, a single partition can still be throttled if writes are concentrated on it. Option D is wrong because DynamoDB Accelerator (DAX) is an in-memory cache for read operations only; it does not cache or accelerate write operations, so it cannot reduce write throttling.

Full explanation →
594
MCQmedium

A company is using Amazon Redshift for data warehousing. The security team requires that all data be encrypted at rest using a customer-managed AWS KMS key. Additionally, the team wants to ensure that the key must be used only for this specific Redshift cluster. Which configuration should be applied?

A.Create a new KMS key and associate it with the Redshift cluster's subnet group.
B.Create a new KMS key with a key policy that grants access only to the Redshift cluster's IAM role.
C.Use the default AWS managed KMS key for Redshift.
D.Use an HSM-backed key from CloudHSM.
AnswerB

KMS key can be restricted to a specific role used by Redshift.

Why this answer

To enforce encryption at rest with a customer-managed AWS KMS key and restrict key usage to a specific Redshift cluster, you create a new KMS key and configure its key policy to grant encrypt/decrypt permissions only to the IAM role associated with the Redshift cluster. This ensures the key is only used by that cluster. Option A is incorrect because associating a key with a subnet group does not limit usage to the cluster; key policies control access.

Option C is incorrect because the default AWS managed key is not customer-managed. Option D is incorrect because CloudHSM keys are not directly used with Redshift; KMS is the required service for integration.

Full explanation →
595
MCQhard

A company uses Amazon DynamoDB to store session data for a web application. The application is experiencing occasional throttling (ProvisionedThroughputExceededException) during peak hours. The DynamoDB table has on-demand capacity mode. Which combination of steps should be taken to resolve the issue?

A.Review the partition key design and consider using a write sharding pattern to distribute traffic evenly.
B.Enable DynamoDB Accelerator (DAX) to cache the frequently accessed items.
C.Enable auto scaling for the table and set minimum and maximum capacity.
D.Switch to provisioned capacity and increase the read and write capacity units.
AnswerA

A hot partition causes throttling even in on-demand mode; distributing the writes across partitions resolves the issue.

Why this answer

Even with on-demand capacity, DynamoDB applies a per-partition throughput limit (3,000 RCU or 1,000 WCU). Throttling occurs when traffic is unevenly distributed across partitions due to a suboptimal partition key. Reviewing the partition key and applying a write sharding pattern (e.g., adding a random suffix) distributes writes evenly, preventing throttling.

Option B (DAX) only reduces read load and does not fix write throttling. Option C (auto scaling) is for provisioned mode, not on-demand. Option D (switching to provisioned) does not resolve uneven partition access; proper key design is still needed.

Full explanation →
596
Drag & Dropmedium

Arrange the steps to troubleshoot a connection timeout issue from an EC2 instance to an Amazon RDS for SQL Server DB instance in the correct order.

Drag steps to the numbered slots on the right, or tap a step then tap a slot.

Steps
Order
1Step 1
2Step 2
3Step 3
4Step 4

Why this order

Troubleshooting connectivity involves checking security groups, network ACLs, network configuration, and testing with telnet.

Full explanation →
597
MCQhard

A company is migrating a 10 TB PostgreSQL database from on-premises to Amazon RDS for PostgreSQL. They need to minimize downtime and have limited network bandwidth. Which approach is most appropriate?

A.Use AWS Database Migration Service (DMS) with a full load and ongoing replication to keep the target in sync.
B.Use pg_dump to export the database and pg_restore to import to RDS, then use DMS for replication.
C.Use AWS Snowball to transfer a physical backup to AWS, then use DMS for ongoing replication.
D.Use AWS Direct Connect to increase bandwidth and then use DMS for full load and replication.
AnswerC

Snowball handles the large initial load offline; DMS handles ongoing changes with minimal bandwidth.

Why this answer

The 10 TB database size combined with limited network bandwidth makes a direct online migration impractical due to prolonged transfer times. AWS Snowball allows you to physically ship the database backup to AWS, bypassing bandwidth constraints. After loading the backup into Amazon RDS, AWS DMS can perform ongoing change data capture (CDC) replication to synchronize any changes made during the transfer, minimizing downtime.

Exam trap

The trap here is that candidates often assume DMS can handle any data volume over any network, ignoring the practical reality that large datasets (e.g., 10 TB) over limited bandwidth require a physical transfer mechanism like Snowball to avoid days or weeks of migration time.

How to eliminate wrong answers

Option A is wrong because a full load via DMS over limited bandwidth would take an excessively long time for 10 TB, likely exceeding acceptable downtime windows and risking timeout or failure. Option B is wrong because pg_dump and pg_restore over a limited network would also be extremely slow for 10 TB, and using DMS afterward for replication does not solve the initial transfer bottleneck. Option D is wrong because AWS Direct Connect requires weeks to provision and may not be feasible for a one-time migration; it also does not address the immediate bandwidth limitation if the on-premises network itself is constrained.

Full explanation →
598
MCQmedium

A company is migrating a 5 TB MySQL database to Amazon RDS for MySQL. They need to minimize downtime. Which approach should they use?

A.Use Percona XtraBackup to take a backup and restore to RDS
B.Use mysqldump to export and import data
C.Use AWS DMS with full load and change data capture (CDC)
D.Use AWS DataSync to copy database files
AnswerC

Minimizes downtime with CDC.

Why this answer

AWS DMS with full load and change data capture (CDC) minimizes downtime by performing an initial full load of the 5 TB database while continuously replicating ongoing changes from the source. Once the full load completes and the target is nearly synchronized, you can cut over with minimal downtime, typically seconds to minutes. This approach is specifically designed for large-scale migrations with low downtime requirements.

Exam trap

The trap here is that candidates often choose mysqldump (Option B) because it is familiar, but they overlook its inability to handle CDC and the impracticality of exporting 5 TB with minimal downtime.

How to eliminate wrong answers

Option A is wrong because Percona XtraBackup creates a physical backup of the MySQL data files, but restoring to RDS requires additional steps like uploading to S3 and using the mysql_restore tool, which does not support CDC and thus cannot minimize downtime for a live database. Option B is wrong because mysqldump is a logical backup tool that exports data as SQL statements, which is extremely slow for 5 TB and requires the source database to be read-locked or offline during the export, causing significant downtime. Option D is wrong because AWS DataSync is designed for copying files over NFS or SMB, not for direct database migration; it cannot handle MySQL's internal file structure or ongoing transactional changes, and would require the database to be stopped to ensure consistency.

Full explanation →
599
MCQhard

A database specialist observes the CloudWatch metric for a production RDS for MySQL instance. The max_connections parameter is set to 150. The Sum of DatabaseConnections over 1-minute periods is shown. The application reports intermittent connection failures. What is the most likely cause?

A.The number of connections exceeded the max_connections limit.
B.The average number of connections is below the limit.
C.The DatabaseConnections metric is unreliable.
D.The application is not using connection pooling.
AnswerA

The Sum metric shows up to 200 connections per minute, exceeding the limit.

Why this answer

The Sum metric over 1-minute period represents the total number of connections during that minute. A Sum of 200 indicates that at some point during the minute, connections exceeded the max_connections of 150, causing failures. Option B is wrong because the metric shows Sum, not average.

Option C is wrong because connection pooling reduces connections, not increases. Option D is wrong because the metric clearly shows high connection counts.

Full explanation →
600
MCQmedium

A company is migrating a 1 TB SQL Server database from on-premises to Amazon RDS for SQL Server. The database has a large table with a LOB column. The network bandwidth between the on-premises data center and AWS is limited to 50 Mbps. The migration must be completed within a week. Which approach should the company use?

A.Use BCP to export the LOB table to flat files and then import to RDS.
B.Use SQL Server native backup and restore to RDS.
C.Use AWS DMS with compression enabled.
D.Create a read replica of the on-premises database and promote it.
AnswerC

DMS supports compression to reduce data transfer time.

Why this answer

AWS DMS with compression enabled is the correct approach because it can migrate the 1 TB database within the limited 50 Mbps bandwidth by compressing data during transfer, reducing the total data volume. DMS supports ongoing replication and handles LOB columns efficiently, making it suitable for a time-constrained migration. The 50 Mbps link can transfer approximately 1.5 TB in a week (50 Mbps * 604,800 seconds / 8 = ~3.78 TB raw capacity), but compression and overhead make DMS the most reliable choice for this scenario.

Exam trap

The trap here is that candidates often assume native backup and restore is universally supported for RDS migrations, but RDS for SQL Server restricts this to specific versions and requires the backup file to be uploaded to S3 first, which still faces bandwidth limitations without compression.

How to eliminate wrong answers

Option A is wrong because BCP exports LOB data to flat files, which are uncompressed and require additional storage and manual import steps, and the transfer over 50 Mbps would likely exceed the one-week window due to the large data volume without compression. Option B is wrong because SQL Server native backup and restore to RDS is not supported for on-premises to RDS migrations; RDS for SQL Server does not allow restoring native backups from external sources unless using the native backup/restore feature with S3, which still requires uploading the backup file and does not inherently compress data for transfer. Option D is wrong because creating a read replica of the on-premises database and promoting it is not feasible; SQL Server does not support cross-environment read replicas between on-premises and RDS, and this approach would require complex log shipping or replication setup that is not natively available.

Full explanation →
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