DBS-C01 Practice Test 40 — 15 Questions

Question 1

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

Accepted Answer

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

Answer

Enable encryption at rest

Answer

Increase the write capacity of the DB instance

Answer

Enable Amazon ElastiCache in front of the database

Question 2

A company is running an Amazon RDS for MySQL DB instance. The database performance has degraded over time. The DBA suspects that the issue is due to a high number of connections that are in a 'sleep' state from a legacy application. What is the MOST effective solution to automatically terminate idle connections?

Accepted Answer

Modify the 'wait_timeout' parameter in the DB parameter group to a lower value.. The 'wait_timeout' parameter in MySQL automatically closes idle connections after the specified number of seconds. Reducing this value from the default (28800 seconds) will terminate sleeping connections sooner and free up resources. Option B is incorrect because increasing 'max_connections' does not terminate idle connections; it only allows more connections, which could worsen the problem. Option C is incorrect because Amazon RDS Proxy manages connection pooling and reduces connection overhead but does not automatically terminate idle connections; it reuses connections but does not kill them. Option D is incorrect because running 'SHOW PROCESSLIST' and manually killing connections is not automated and requires human intervention, so it is not the most effective solution.

Answer

Increase the 'max_connections' parameter to accommodate more idle connections.

Answer

Enable Amazon RDS Proxy to manage connection pooling and automatically close idle connections.

Answer

Use a script to run 'SHOW PROCESSLIST' and manually kill idle connections.

Question 3

A company uses Amazon DynamoDB with a table that stores sensitive customer data. The security team requires that all data at rest be encrypted using a customer-managed AWS KMS key (CMK). Additionally, the company needs to ensure that only specific IAM roles can access the table. Which solution meets these requirements with the least operational overhead?

Accepted Answer

Configure the DynamoDB table to use AWS KMS encryption with a CMK. Create an IAM role with a policy that grants access to the table and includes a condition that the encryption context matches the CMK.. It combines DynamoDB encryption at rest with a customer-managed KMS CMK and uses an IAM role policy with an encryption context condition. This ensures that only specific IAM roles can access the table, and the encryption context condition ties the KMS key usage to the table, providing fine-grained access control with minimal operational overhead. The encryption context is automatically set by DynamoDB to the table ARN, so the condition key `kms:EncryptionContext:aws:dynamodb:tableName` can be used to restrict decryption to that specific table.

Answer

Enable encryption at rest using AWS KMS with a CMK and use column-level encryption with AWS KMS to restrict access.

Answer

Attach a resource-based policy to the DynamoDB table that grants access only to the specific IAM roles.

Answer

Use a DynamoDB Accelerator (DAX) cluster with encryption at rest using a CMK, and attach a resource-based policy to the table.

Answer

Configure the DynamoDB table to use AWS KMS encryption with a CMK, and attach a key policy to the CMK that allows only the specific IAM roles.

Question 4

A company is using Amazon DynamoDB with Auto Scaling enabled. During a flash sale, write traffic spikes and the application experiences ProvisionedThroughputExceededException errors. The DynamoDB table has provisioned write capacity of 1000 WCU and Auto Scaling is set to scale between 1000 and 10000 WCU. What is the most likely cause of the throttling?

Accepted Answer

Auto Scaling cannot react quickly enough to sudden traffic spikes.. DynamoDB Auto Scaling uses a target tracking policy that adjusts capacity based on consumed WCU over a period (typically 5 minutes). During sudden traffic spikes, the consumption increases rapidly, but Auto Scaling cannot react immediately; there is a lag before the scaling policy triggers and additional capacity is provisioned. This results in ProvisionedThroughputExceededException errors until the new capacity takes effect. Option B is incorrect because throttling is due to insufficient write capacity, not partitioning. Option C is incorrect because burst capacity provides a short-term buffer but is quickly exhausted during sustained spikes. Option D is incorrect because the maximum WCU of 10000 is higher than the spike demand, so the limit is not the issue—the timing of the scaling action is the problem.

Answer

The DynamoDB table has insufficient partitions to handle the traffic.

Answer

The table's burst capacity is exhausted and Auto Scaling has not yet increased capacity.

Answer

The Auto Scaling policy has a maximum WCU limit that is too low.

Question 5

A company uses Amazon DynamoDB to store IoT sensor data. Each sensor writes a record every second. The table has a partition key of 'sensor_id' and a sort key of 'timestamp'. Over time, the team notices that write performance degrades for certain sensors that generate more data. The table uses provisioned capacity with auto scaling enabled. The application uses eventual consistency. The team needs to ensure consistent write performance without throttling. Which action should be taken?

Accepted Answer

Modify the partition key to include a random number or prefix to distribute writes evenly.. Adding a random number or prefix to the partition key distributes writes across multiple partitions, preventing hot partitions that cause throttling. Option B (increasing WCU) does not resolve the underlying hot partition issue, as each partition still has a maximum write capacity. Option C (on-demand mode) still has per-partition throughput limits and does not fix uneven write distribution. Option D (creating a GSI) does not affect the base table's write distribution; GSIs are for read efficiency, not write distribution.

Answer

Increase the provisioned write capacity units (WCU) to a higher value.

Answer

Switch the table to on-demand capacity mode.

Answer

Create a global secondary index (GSI) with a different partition key to handle writes.

Question 6

A company runs a time-series application on Amazon RDS for PostgreSQL. The table 'events' has 500 million rows and is queried by event_time and event_type. Queries for the last hour are slow despite indexing. Which design change would most improve query performance?

Accepted Answer

Partition the table by month using PostgreSQL declarative partitioning. Partitioning the 'events' table by month using PostgreSQL declarative partitioning allows the query planner to prune partitions that do not contain data for the last hour. This dramatically reduces the number of rows scanned, even with a large table of 500 million rows, and directly addresses the slow query performance for time-range queries. Indexing alone cannot overcome the overhead of scanning a massive monolithic table for a narrow time window.

Answer

Add a composite index on (event_type, event_time)

Answer

Migrate to Amazon DynamoDB with TTL

Answer

Upgrade to a larger RDS instance

Question 7

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

Accepted Answer

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

Answer

Amazon DynamoDB with auto scaling

Answer

Amazon RDS for MySQL with Multi-AZ deployment

Answer

Amazon RDS for MySQL with a Single-AZ deployment

Question 8

A company runs a critical Oracle database on Amazon RDS. They need to implement a disaster recovery strategy that provides the lowest possible recovery point objective (RPO) and recovery time objective (RTO) across AWS Regions. Which TWO actions should they take? (Choose two.)

Accepted Answer

Configure cross-Region automated backups to copy backups to another Region.. Cross-Region automated backups copy RDS snapshots and transaction logs to a secondary Region, enabling point-in-time recovery with an RPO of minutes and an RTO of hours (depending on snapshot restore time). Option B is correct because AWS DMS with ongoing replication (change data capture) can continuously replicate Oracle data to a standby instance in another Region, achieving an RPO of seconds and an RTO of minutes by failing over to the replicated instance.

Answer

Enable Multi-AZ on the primary RDS instance.

Answer

Create a cross-Region read replica of the Oracle DB instance.

Answer

Use Amazon Aurora Global Database to replicate data across Regions.

Question 9

A company is migrating a 2 TB SQL Server database to Amazon RDS for SQL Server. They have a limited maintenance window and need to minimize downtime. Which TWO strategies should they combine?

Accepted Answer

Use AWS DMS to perform a full load and ongoing replication.. AWS DMS (Database Migration Service) is correct because it can perform a full load of the 2 TB SQL Server database to Amazon RDS for SQL Server, then continuously replicate ongoing changes using change data capture (CDC). This minimizes downtime by allowing the source database to remain operational during migration, and the target can be synchronized with minimal interruption before cutover.

Answer

Use AWS Direct Connect to increase bandwidth.

Answer

Use AWS CloudEndure Migration.

Answer

Use AWS Snowball to transfer a backup offline.

Question 10

A company is migrating a 3 TB Oracle database to Amazon RDS for Oracle. They want to use Oracle Data Pump to export the data and then import it into RDS. What is the most efficient way to transfer the dump files to AWS?

Accepted Answer

Upload the dump files to Amazon S3 and then import them into RDS for Oracle using Oracle Data Pump.. Uploading Oracle Data Pump dump files to Amazon S3 and then importing them into Amazon RDS for Oracle using the DBMS_DATAPUMP API is the most efficient method for transferring large dump files. This approach leverages S3 for scalable, durable storage and high-throughput transfer, avoiding network bottlenecks or physical shipping delays. The RDS for Oracle instance can directly access the S3 bucket via an IAM role, enabling a seamless import process.

Answer

Use AWS Snowball to physically ship the dump files to AWS.

Answer

Use AWS DMS to migrate the data directly from Oracle to RDS for Oracle.

Answer

Transfer the dump files over a VPN connection to an EC2 instance and then copy to RDS.

Question 11

Arrange the steps to enable automated backups for an Amazon RDS for PostgreSQL DB instance in the correct order.

Accepted Answer

Modify the DB instance → Set backup retention period to a value greater than 0 → Apply the changes immediately → Confirm the modification. Automated backups are enabled by adjusting the backup retention period in the instance settings and applying the change.

Answer

Set backup retention period to a value greater than 0 → Modify the DB instance → Apply the changes immediately → Confirm the modification

Answer

Modify the DB instance → Apply the changes immediately → Set backup retention period to a value greater than 0 → Confirm the modification

Answer

Confirm the modification → Modify the DB instance → Set backup retention period to a value greater than 0 → Apply the changes immediately

Question 12

A developer is configuring IAM permissions for a Lambda function that accesses a DynamoDB table named 'Orders'. The policy shown is attached to the Lambda execution role. The function needs to delete items but only if the item contains only 'order_id' and 'status' attributes. Which statement about this policy is correct?

Accepted Answer

The function can only delete items that contain exactly the attributes 'order_id' and 'status'. The IAM policy includes a condition key `dynamodb:Attributes` that restricts the `DeleteItem` action to items containing exactly the attributes 'order_id' and 'status'. This condition ensures the function can only delete items that match the specified attribute set, enforcing a fine-grained access control at the item attribute level.

Answer

The function can delete any item in the Orders table because the condition is on the resource

Answer

The function cannot delete any items because the DeleteItem action is not allowed

Answer

The function cannot call Query on the table because it is not listed in the actions

Question 13

Match each AWS database migration tool/service to its function.

Accepted Answer

AWS DMS: Migrates databases to AWS with minimal downtime. AWS DMS is for minimal-downtime database migration, AWS SCT for schema conversion, and AWS Snowball for physical data transfer. Common confusions involve swapping these functions.

Answer

AWS DMS: Converts database schema to target engine

Answer

AWS Snowball: Migrates databases to AWS with minimal downtime

Question 14

A healthcare company stores patient records in Amazon DynamoDB. Each record includes patient_id (partition key), visit_date (sort key), and a large JSON attribute for medical history. The application frequently queries recent visits for a patient and scans historical data for analytics. The scans on the medical history attribute cause high RCU consumption. The company wants to reduce costs and improve query performance. Which design should be implemented?

Accepted Answer

Move the medical history attribute to a separate table with patient_id as partition key and visit_date as sort key. Use DynamoDB Streams to keep both tables in sync.. It separates the large, infrequently accessed medical history attribute from the frequently queried core record, reducing the item size for common queries and thus lowering RCU consumption. By using DynamoDB Streams to synchronize the two tables, you maintain data consistency without adding complexity to the application, and queries against the main table become faster and cheaper since they no longer read the large JSON payload.

Answer

Compress the medical history attribute using gzip before storing in DynamoDB.

Answer

Enable DynamoDB Accelerator (DAX) for the table to cache frequent queries.

Answer

Use Amazon S3 to store the medical history as a separate object and reference it from DynamoDB.

Question 15

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?

Accepted Answer

Use AWS DMS with a VPN connection to the on-premises database. 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.

Answer

Set up AWS Direct Connect and use DMS

Answer

Use AWS Snowball Edge to transfer data offline

Answer

Export to Amazon S3 and import into Aurora