DBS-C01 Practice Questions 2 — 50 Questions

Question 1

A database specialist is analyzing an Aurora MySQL error log and finds the above deadlock error. The application performs an update on the orders table and then updates the inventory table within the same transaction. The deadlock occurs when two concurrent transactions try to update orders and inventory in different orders. Which design change should the database specialist recommend to reduce deadlocks?

Accepted Answer

Ensure all transactions update tables in the same order (e.g., always update inventory first, then orders). Option B is correct because deadlocks in Aurora MySQL often occur when concurrent transactions acquire row-level locks on tables in different orders. By enforcing a consistent lock order (e.g., always updating inventory first, then orders), the database can avoid circular wait conditions, which are a necessary condition for deadlocks. This is a standard best practice for reducing deadlocks in InnoDB, which uses row-level locking and two-phase locking.

Answer

Combine the orders and inventory tables into a single table to avoid multiple table locks

Answer

Use SELECT ... FOR UPDATE on both tables before updating

Answer

Change the transaction isolation level to READ UNCOMMITTED

Question 2

A company is using Amazon DynamoDB for a shopping cart application. The table has a partition key of `user_id` and a sort key of `item_id`. The application performs frequent updates to the `quantity` attribute. The company notices that write requests are being throttled during peak hours. Which TWO actions would help reduce throttling? (Choose two.)

Accepted Answer

Increase the provisioned write capacity for the table.. Option A is correct because increasing the provisioned write capacity directly raises the number of write capacity units (WCUs) available per second, allowing more write requests to succeed without being throttled. Since the application performs frequent updates to the `quantity` attribute, which consumes write capacity, adding more capacity alleviates throttling during peak hours.

Answer

Use conditional writes to prevent overwrites.

Answer

Enable DynamoDB Streams to process writes asynchronously.

Answer

Enable DynamoDB Accelerator (DAX) for the table.

Question 3

A company is running a MySQL database on Amazon RDS and needs to store JSON documents that are frequently queried by fields within the JSON. The company wants to reduce development complexity and improve query performance. Which RDS MySQL feature should the database specialist recommend?

Accepted Answer

Use the JSON data type in MySQL 8.0 and utilize JSON path expressions in queries. Option B is correct because MySQL 8.0's native JSON data type stores JSON documents in an optimized binary format, enabling efficient indexing and querying via JSON path expressions (e.g., `JSON_EXTRACT`, `->`, `->>`). This reduces development complexity by allowing direct SQL access to JSON fields without application-level parsing, and improves query performance through generated columns and virtual indexes.

Answer

Migrate the JSON data to Amazon DynamoDB and use DynamoDB's document model

Answer

Store JSON documents in a VARCHAR(MAX) column and use LIKE operations for queries

Answer

Store JSON documents as BLOBs and parse them in application code

Question 4

A company is migrating a MySQL database to Amazon Aurora MySQL. The current database uses multi-statement transactions with read committed isolation level. The application frequently encounters deadlocks on the source database. Which Aurora MySQL feature can help reduce deadlocks without application changes?

Accepted Answer

Use Amazon RDS Proxy to pool and share database connections.. RDS Proxy helps reduce deadlocks by pooling and reusing database connections, which minimizes the overhead of establishing new connections and reduces contention on database resources. In MySQL, deadlocks often occur when multiple transactions compete for the same resources under high connection churn; by maintaining a stable pool of connections, RDS Proxy lowers the probability of concurrent conflicting locks. Since the proxy is transparent to the application, no code changes are required to benefit from this behavior.

Answer

Use Amazon Aurora Auto Scaling to automatically adjust the number of replicas.

Answer

Use Amazon Aurora Global Database to replicate data to multiple regions.

Answer

Use Amazon Aurora Backtrack to quickly revert transactions.

Question 5

A company is migrating an on-premises MongoDB database to Amazon DocumentDB. The database stores IoT sensor data with time-series characteristics. The application performs range queries on timestamp fields and updates recent documents frequently. Which THREE aspects of DocumentDB should the company consider to optimize performance for this workload? (Choose three.)

Accepted Answer

Implement sharding to distribute write load across multiple instances.. Option B is correct because DocumentDB does not support native sharding like MongoDB; however, for workloads with high write throughput, you can distribute writes by using multiple DocumentDB instances and routing writes based on a shard key in the application layer. This helps avoid write bottlenecks on a single instance and scales write capacity horizontally.

Answer

Use global secondary indexes to speed up queries on the timestamp field.

Answer

Use a single large instance class to avoid sharding complexity.

Question 6

A company runs a customer relationship management (CRM) application on Amazon RDS for PostgreSQL. The application has a table 'customers' with columns: id (primary key), name, email, signup_date, and status. The table has 10 million rows. The application frequently queries by email address. The company also needs to run analytical queries that aggregate customers by signup_date and status. The DBA notices that the index on email is heavily used and causes high write latency. The company wants to improve write performance without sacrificing read performance for email queries. Which THREE actions should the database specialist take? (Choose THREE.)

Accepted Answer

Create a BRIN index on signup_date.. Option A is correct because a BRIN (Block Range INdex) index on signup_date is ideal for large tables where data is naturally ordered by insertion time. BRIN indexes are much smaller and have lower write overhead than B-tree indexes, making them suitable for analytical queries on signup_date without significantly impacting write performance.

Answer

Remove the index on email to reduce write latency.

Answer

Create a GIN index on email.

Question 7

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?

Accepted Answer

Implement a local cache in each application instance to reduce load on the centralized Redis cluster.. Option B is correct because 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.

Answer

Use a read-through cache with a longer TTL (e.g., 48 hours).

Answer

Use a write-through cache with a longer TTL (e.g., 48 hours).

Answer

Use a write-through cache with a shorter TTL (e.g., 1 hour).

Question 8

A company is implementing fine-grained access control for a DynamoDB table named UserSessions. The table has a partition key of 'user_id'. The above IAM policy is attached to an IAM role assumed by the application. What does this policy achieve?

Accepted Answer

Restricts the application to access only items where the partition key matches the user's AWS user ID. The IAM policy uses a condition key `dynamodb:LeadingKeys` with a value of `${aws:userid}`. This restricts access to items in the DynamoDB table where the partition key (`user_id`) matches the unique identifier of the IAM user or role that is making the request. This implements fine-grained access control, ensuring the application can only read or write items belonging to the authenticated user.

Answer

Allows the application to perform all operations on the UserSessions table without restrictions

Answer

Allows the application to read but not write items in the UserSessions table

Answer

Allows the application to access only the UserSessions table but not other tables

Question 9

A financial services company is designing a ledger system using Amazon QLDB. The application records transactions that must never be modified or deleted. The company expects high write throughput and needs to ensure that the ledger can handle the load without throttling. Which design consideration is MOST important to achieve this?

Accepted Answer

Batch multiple document inserts into a single transaction to reduce the number of transactions.. Option D is correct because Amazon QLDB charges per transaction (document insert, update, or delete) and has a maximum throughput limit of 1,000 transactions per second per ledger. By batching multiple document inserts into a single transaction, you reduce the number of transactions, thereby staying within the throughput limit while still achieving high write throughput. This approach directly addresses the need to avoid throttling without sacrificing the immutability requirements of the ledger system.

Answer

Partition the ledger table by transaction date to distribute write load.

Answer

Create multiple ledgers and distribute writes across them.

Answer

Enable auto-scaling on the ledger to handle bursts of traffic.

Question 10

A social media company uses Amazon DynamoDB to store user posts. The table has a partition key of 'user_id' and a sort key of 'post_timestamp'. Each item is about 10 KB. The application needs to retrieve all posts for a given user within a date range. The company recently added a new feature that allows users to 'like' posts, and they store the like count as an attribute in the post item. The like count is updated frequently. The application experiences high write throttling on the table. The table has 1000 WCUs provisioned. The write pattern is bursty. Which design change would MOST effectively reduce write throttling?

Accepted Answer

Add a random suffix to the user_id partition key to distribute writes across multiple partitions.. Option C is correct because the write throttling is caused by a 'hot partition' — all writes for a given user_id go to the same partition, and the bursty write pattern (e.g., many likes on a single post) exceeds the partition's 1,000 WCU limit (1,000 write capacity units per partition). Adding a random suffix to the user_id partition key distributes writes across multiple partitions, effectively increasing the write throughput for that logical user's data. This is a common design pattern for DynamoDB to handle high-traffic items without increasing provisioned capacity.

Answer

Increase the WCUs to 5000.

Answer

Enable DynamoDB Accelerator (DAX) to cache writes.

Answer

Create a Global Secondary Index (GSI) on the like count attribute.

Question 11

A financial services company uses Amazon RDS for MySQL to store transaction data. The database has a single table 'transactions' with 500 million rows. The table has an auto-increment primary key and an index on 'transaction_date'. The company runs a monthly report that aggregates transactions by account_id and transaction_date. The report query uses a GROUP BY on account_id and transaction_date, and scans the entire table. The query takes over 2 hours to complete and often times out. The DBA suggests creating a materialized view. However, the company wants to minimize operational overhead. Which solution meets the requirements with the LEAST operational overhead?

Accepted Answer

Migrate the reporting workload to Amazon Redshift by loading the transactions table into Redshift and running the report query there.. Option B is correct because Amazon Redshift is purpose-built for large-scale analytical queries. By migrating the reporting workload to Redshift, the company offloads the heavy aggregation from the transactional RDS instance to a columnar storage engine that can scan and aggregate 500 million rows efficiently using massively parallel processing (MPP). This approach requires no changes to the existing RDS database and minimizes operational overhead compared to managing a materialized view or manual indexing.

Answer

Increase the RDS instance size to the largest available to improve performance.

Answer

Create a materialized view in MySQL that pre-aggregates the data and refreshes it nightly.

Answer

Add a composite index on (account_id, transaction_date) to speed up the GROUP BY.

Question 12

A company is using AWS Database Migration Service (AWS DMS) to migrate a 5 TB MySQL database to Amazon RDS for MySQL. The migration is taking longer than expected. The company notices that the source database has a high volume of write operations. Which configuration change would MOST likely improve the migration performance?

Accepted Answer

Increase the number of parallel threads in the DMS task settings.. Increasing the number of parallel threads in the DMS task settings allows AWS DMS to process multiple table partitions or row changes concurrently, which directly addresses the bottleneck caused by a high volume of write operations on the source. By default, DMS uses a single thread per table, but when the source is under heavy write load, parallel threads can capture and apply changes more efficiently, reducing the overall migration time.

Answer

Enable Multi-AZ on the target RDS instance.

Answer

Use a smaller instance class for the replication instance to reduce cost.

Answer

Set the LOB mode to 'Full LOB mode'.

Question 13

A company is migrating a 1 TB Oracle database to Amazon Aurora PostgreSQL using AWS DMS. They need to ensure minimal downtime and data consistency. Which TWO actions should the company take?

Accepted Answer

Enable change data capture (CDC) on the source Oracle database. Option B is correct because enabling Change Data Capture (CDC) on the source Oracle database allows AWS DMS to capture ongoing changes after the full load, enabling a near-zero downtime migration by continuously replicating transactions to the target Aurora PostgreSQL. Option C is correct because enabling data validation on the DMS task ensures that the data migrated is consistent between the source and target, verifying that no data loss or corruption occurred during the migration process.

Answer

Increase the target Aurora storage to 2 TB to avoid storage issues

Answer

Disable foreign key constraints on the target during migration

Answer

Use AWS Schema Conversion Tool to convert the schema

Question 14

A company is migrating an on-premises PostgreSQL database to Amazon RDS for PostgreSQL. The database is 2 TB in size and has a high write workload. The company needs to minimize downtime during the migration. Which AWS service or feature should the company use to achieve this?

Accepted Answer

Use AWS Database Migration Service (AWS DMS) with ongoing replication.. AWS DMS with ongoing replication (change data capture, CDC) is the correct choice because it allows continuous synchronization of the source PostgreSQL database with the target RDS for PostgreSQL instance after an initial full load. This minimizes downtime by enabling the target to stay up-to-date with changes until the cutover, which is critical for a 2 TB database with a high write workload.

Answer

Use pg_dump and pg_restore to export and import the database.

Answer

Use the AWS Schema Conversion Tool (AWS SCT) to convert the schema and migrate data.

Answer

Use AWS DataSync to replicate the database files.

Question 15

A company is migrating a self-managed Oracle database to Amazon Aurora PostgreSQL using AWS DMS. The source database has a large number of tables with foreign key constraints. During the full load phase, some tables fail to load due to foreign key violations. What is the most efficient way to resolve this?

Accepted Answer

Disable foreign key constraints on the target before migration and re-enable after. Option C is correct because disabling foreign key constraints on the target Aurora PostgreSQL database before the migration allows DMS to load tables in any order without violating referential integrity. After the full load completes, re-enabling the constraints ensures data consistency. This approach is the most efficient as it avoids complex dependency ordering and reduces migration failures.

Answer

Increase the DMS task memory and parallel load threads

Answer

Use the table preparation mode 'Do nothing' and load tables in dependency order

Answer

Pre-create all target tables with the same constraints and use 'Truncate' mode

Question 16

A company wants to migrate its on-premises Oracle database to Amazon Aurora PostgreSQL. The company needs to automatically convert the Oracle schema to PostgreSQL-compatible format. Which AWS service should the company use?

Accepted Answer

AWS Schema Conversion Tool (AWS SCT). AWS Schema Conversion Tool (AWS SCT) is designed specifically to convert database schemas from one engine to another, including Oracle to Amazon Aurora PostgreSQL. It automatically translates Oracle DDL (tables, indexes, stored procedures, functions, etc.) into PostgreSQL-compatible format, handling data type mappings, PL/SQL to PL/pgSQL conversion, and other schema-level transformations. AWS DMS handles data migration, not schema conversion, making SCT the correct choice for this requirement.

Answer

AWS Database Migration Service (AWS DMS) with the Oracle native dump and load option

Answer

AWS Server Migration Service (AWS SMS)

Answer

AWS Database Migration Service (AWS DMS)

Question 17

An IAM policy is attached to a user who needs to restore an Amazon RDS DB instance from a DB snapshot. The user attempts to restore and receives an 'Access Denied' error. Which missing permission is MOST likely causing the failure?

Accepted Answer

rds:CreateDBInstance. To restore an Amazon RDS DB instance from a DB snapshot, the user must have the `rds:CreateDBInstance` permission. This is because the restore operation internally calls the CreateDBInstance API to create a new DB instance from the specified snapshot. Without this permission, the request fails with an 'Access Denied' error, even if the user has permissions to describe snapshots or instances.

Answer

rds:DescribeDBSnapshots

Answer

rds:DescribeDBInstances

Answer

rds:CreateDBSubnetGroup

Question 18

A company is migrating a 500 GB on-premises PostgreSQL database to Amazon RDS for PostgreSQL. The migration must have minimal downtime and support ongoing replication after the initial load. The network link has limited bandwidth, and the company wants to avoid costly data transfer fees. Which approach should be used?

Accepted Answer

Use AWS DMS with ongoing replication (CDC) from the start.. AWS DMS with ongoing replication (CDC) enables a full load followed by continuous change data capture, which meets the requirements of minimal downtime and ongoing replication. DMS compresses data in transit and can throttle network usage, helping to manage limited bandwidth and avoid costly data transfer fees by using the existing network link efficiently.

Answer

Use AWS Database Migration Service (DMS) with a full load only, then cut over.

Answer

Use pg_dump to export the database and pg_restore to import into RDS.

Answer

Use AWS Snowball Edge to transfer the database files, then load into RDS.

Question 19

A company is migrating an on-premises Microsoft SQL Server database to Amazon RDS for SQL Server. The database uses SQL Server Agent jobs for ETL processes. After migration, the company needs to continue running these jobs. What is the MOST efficient way to manage these jobs in the AWS cloud?

Accepted Answer

Recreate the jobs as AWS Lambda functions and trigger them with Amazon CloudWatch Events.. AWS Lambda functions triggered by Amazon CloudWatch Events (now Amazon EventBridge) provide a serverless, fully managed alternative to SQL Server Agent jobs. This approach eliminates the need to manage any compute infrastructure, as Lambda handles the execution of ETL logic directly against the RDS for SQL Server instance. It is the most efficient because it requires no additional servers, reduces operational overhead, and integrates natively with AWS monitoring and scheduling services.

Answer

Install SQL Server Agent on an Amazon EC2 instance and connect to RDS.

Answer

Use the AWS Schema Conversion Tool (AWS SCT) to convert the jobs to PostgreSQL functions.

Answer

Use AWS Database Migration Service (AWS DMS) tasks to replicate the jobs.

Question 20

A company is migrating a 3 TB on-premises Oracle database to Amazon Aurora PostgreSQL using AWS DMS. The migration task is failing with an error indicating insufficient memory. Which TWO actions should the company take to resolve this issue? (Choose TWO.)

Accepted Answer

Increase the instance class of the DMS replication instance.. Option C is correct because the 'insufficient memory' error during an AWS DMS migration indicates that the replication instance lacks the memory required to handle the data volume, transformation rules, or cache for large transactions. Increasing the instance class (e.g., from dms.c5.large to dms.c5.2xlarge) provides more memory and CPU, directly resolving the resource constraint.

Answer

Change the target database engine to Amazon RDS for MySQL.

Answer

Disable Multi-AZ on the DMS replication instance.

Answer

Use a smaller instance class for the DMS replication instance.

DBS-C01 Practice Questions 2 — 50 Questions

Exhibit

DBS-C01 Practice Questions 2 — 50 Questions

Exhibit