DBS-C01 Hard Questions — 25 Difficult Questions

Question 1

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

Accepted Answer

The TargetTablePrepMode is set to DROP_AND_CREATE, which creates tables without indexes or constraints.. When TargetTablePrepMode is set to DROP_AND_CREATE, AWS DMS drops the target table if it exists and then creates a new one using only the basic column definitions from the source. It does not migrate indexes, primary keys, or foreign keys because DMS is not designed to replicate schema objects beyond table structure and data. To preserve constraints and indexes, you must either pre-create them on the target or use a different prep mode like TRUNCATE_BEFORE_LOAD.

Answer

The migration task did not include transformation rules to create indexes and constraints.

Answer

The table selection rule uses wildcards and excludes system tables, which also excludes index definitions.

Answer

The task is using a full load without LOB support, which prevents index creation.

Question 2

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

Accepted Answer

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

Answer

Cross-database queries

Answer

Custom CLR assemblies

Answer

Stored procedures that use dynamic SQL

Question 3

A company is running a production Amazon DynamoDB table with on-demand capacity. The table experiences occasional throttling during traffic spikes. The table's partition key is a timestamp, and the workload is write-heavy. The operations team needs to reduce throttling. Which THREE actions should the team take? (Choose three.)

Accepted Answer

Add a random suffix to the partition key to distribute writes evenly.. Option A is correct because adding a random suffix to the timestamp partition key breaks the sequential write pattern, distributing writes evenly across all partitions. This prevents hot partitions, which are the root cause of throttling in a write-heavy workload with a monotonically increasing partition key.

Answer

Increase the read capacity units for the table.

Answer

Enable DynamoDB global tables to distribute write traffic.

Question 4

A company is migrating its on-premises Oracle database to Amazon RDS for Oracle. The database specialist needs to monitor the migration process and ensure data consistency. Which TWO AWS services should be used together to continuously monitor the replication lag and data integrity?

Accepted Answer

AWS Database Migration Service (AWS DMS). AWS DMS is the correct service because it is specifically designed for database migrations and provides built-in monitoring of replication lag via the 'CDC latency' metric. Amazon CloudWatch is the correct complementary service because it collects and visualizes DMS metrics, including replication lag and task status, and can trigger alarms if data integrity or latency thresholds are breached.

Answer

Amazon RDS Performance Insights

Answer

AWS Schema Conversion Tool (AWS SCT)

Answer

AWS Glue

Question 5

A database engineer is reviewing Amazon RDS for MySQL error logs and sees repeated authentication failures from the same IP address. The application team confirms the password is correct. What is the most likely cause of these errors?

Accepted Answer

The user 'app_user' does not have access from host '10.0.1.50'. The error logs show authentication failures despite the password being correct, which indicates the issue is not with the password itself but with the host-based access control. In MySQL, user accounts are defined as 'user'@'host', and if the application is connecting from an IP address (e.g., 10.0.1.50) that is not included in the user's allowed hosts, MySQL will reject the connection with an authentication error even if the password is correct. This is a common misconfiguration when migrating or scaling applications across different subnets.

Answer

The password is incorrect

Answer

The 'app_user' account is locked

Answer

The database requires SSL connections

Question 6

Refer to the exhibit. A security engineer has applied this key policy to a customer managed KMS key used to encrypt a Secrets Manager secret containing database credentials. An application running on an Amazon EC2 instance in the same account and region is unable to decrypt the secret. What is the MOST likely cause?

Accepted Answer

The condition kms:ViaService restricts the decryption to requests made via Secrets Manager, but the application is calling KMS directly.. The condition `kms:ViaService` in the key policy restricts the `Decrypt` permission to requests that originate specifically from the AWS Secrets Manager service. When the application on the EC2 instance calls KMS directly (e.g., via the `Decrypt` API) to decrypt the secret, the request does not come through Secrets Manager, so the condition is not satisfied and the request is denied. This is the most likely cause of the decryption failure.

Answer

The KMS key ARN in the resource field is incorrect.

Answer

The policy is missing an encryption context that matches the secret's encryption context.

Answer

The policy only allows Decrypt, but the application needs Encrypt permission.

Question 7

A security engineer is troubleshooting an issue where an application using IAM role 'app-role' with a trust policy to assume the 'app-user' user is unable to decrypt an RDS database that uses a customer-managed KMS key. The above key policy is attached to the KMS key. What is the likely cause of the failure?

Accepted Answer

The principal in the policy is 'app-user', but the application uses an IAM role that does not have the necessary permissions.. Option C is correct because the KMS key policy specifies the principal as 'app-user', which is an IAM user. However, the application uses an IAM role 'app-role' to make the decryption requests. Since the principal in the key policy does not include the role or its associated permissions, the KMS key policy denies access to the role, causing the decryption failure. The trust policy on the role allows the user to assume it, but that does not grant the role any KMS permissions unless explicitly added.

Answer

The 'kms:ViaService' condition requires the request to come from RDS, but the application is making KMS API calls directly.

Answer

The policy has a missing 'Effect': 'Deny' statement that is blocking access.

Answer

The condition 'kms:ViaService' restricts the key to be used only with RDS, but the application is using a different service.

Question 8

A database specialist sees the above error log entries from an Amazon RDS for MySQL DB instance. Which action should be taken to resolve the issue?

Accepted Answer

Restore the DB instance from the latest automated snapshot.. The error log entries indicate corruption in the InnoDB tablespace, likely due to a crash or hardware failure. Restoring from the latest automated snapshot is the correct action because it provides a consistent, point-in-time recovery of the entire database, including all table structures and data, without relying on potentially corrupted files. This approach ensures data integrity and avoids further damage that could occur from attempting repairs on a corrupted instance.

Answer

Increase the allocated storage for the DB instance.

Answer

Reboot the DB instance to clear the error.

Answer

Run the mysqlcheck command to repair the corrupted tables.

Question 9

A social media analytics company uses Amazon DynamoDB as the primary data store for user session data. Each session record has a partition key of user_id (String) and a sort key of session_start_time (Number, epoch). The application often queries the most recent 10 sessions for a given user. The traffic pattern shows that 90% of reads are for the last 10 sessions, while 10% are for historical sessions. The table has a provisioned read capacity of 5000 RCU and consistently experiences throttled read requests during peak hours. The company wants to optimize read performance without changing the provisioned capacity. Which design change will MOST improve read performance for this workload?

Accepted Answer

Create a Global Secondary Index (GSI) with the same partition key and a sort key of session_start_time, but query with ScanIndexForward=false and Limit=10.. Option A is correct because creating a GSI with the same partition key (user_id) and sort key (session_start_time) allows you to query with ScanIndexForward=false and Limit=10 to efficiently retrieve only the most recent 10 sessions per user. This avoids scanning all sessions for a user, reducing consumed read capacity and eliminating throttling without increasing provisioned RCU. The GSI also supports the 90% workload pattern by providing a targeted index that minimizes read unit consumption.

Answer

Increase the provisioned read capacity to 10000 RCU to handle the peak load.

Answer

Enable DynamoDB Accelerator (DAX) with default settings to cache the most recent sessions.

Answer

Configure Amazon ElastiCache for Redis as a read-through cache for session data.

Question 10

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

Accepted Answer

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

Answer

Create an AWS Lambda function that runs every hour and deletes expired session data

Answer

Store session data in Amazon S3 with a lifecycle policy to delete objects after 24 hours

Answer

Use DynamoDB Accelerator (DAX) to cache session data and set a 24-hour TTL on the cache

Question 11

A company is designing a multi-tenant SaaS application on Amazon Aurora MySQL. Each tenant has its own database, but some tenants are very large and generate high write traffic. The company wants to isolate tenant workloads to prevent a noisy neighbor from affecting other tenants. Which TWO design strategies should the database specialist recommend?

Accepted Answer

Use Aurora Serverless for tenants with variable workloads. Option A is correct because Aurora Serverless automatically scales compute capacity based on application demand, which is ideal for tenants with variable workloads. This prevents a noisy neighbor scenario by ensuring that a tenant's burst of write traffic does not consume shared resources that would degrade performance for other tenants.

Answer

Use a single Aurora cluster with read replicas for each tenant

Answer

Migrate all tenants to Amazon DynamoDB and use DynamoDB Accelerator (DAX) for caching

Answer

Use Amazon RDS Proxy to pool connections and limit throughput per tenant

Question 12

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

Accepted Answer

The GSI is not designed with a high-cardinality partition key, causing write hot spots on the GSI.. The correct answer is B. A global secondary index (GSI) has its own provisioned read and write capacity, separate from the base table. If the GSI's partition key (player_id) has low cardinality (e.g., only a few distinct player_id values), writes to the base table will concentrate on a small number of GSI partitions, causing throttling on the GSI. This throttling on the GSI then back-pressures the base table, resulting in write throttling on the base table even if its own write capacity is underutilized.

Answer

The application is using strongly consistent reads on the GSI, which consumes double the read capacity.

Answer

Point-in-time recovery (PITR) is enabled, consuming extra write capacity.

Answer

The table's auto-scaling settings are not configured correctly for the GSI.

Question 13

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 14

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 15

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 16

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 17

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 18

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 19

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 20

A team is setting up a DMS migration task. The IAM policy above is attached to the DMS replication instance's IAM role. The team is unable to modify the target RDS instance's maintenance window during the migration. Which additional permission is missing?

Accepted Answer

Add 'rds:ModifyDBInstance' permission for the source RDS instance.. The correct answer is A because modifying the target RDS instance's maintenance window requires the `rds:ModifyDBInstance` permission. The DMS replication instance's IAM role must have this permission for the target RDS instance to allow the migration task to adjust the maintenance window, which is necessary to prevent conflicts during ongoing replication. Without this permission, the DMS task cannot alter the maintenance window, even if the IAM role has other RDS permissions.

Answer

Add 'rds:ModifyOptionGroup' permission for the target RDS instance.

Answer

Add 'dms:ModifyReplicationInstance' permission for the DMS replication instance.

Answer

Add 'ec2:DescribeVpcs' permission to the policy.

DBS-C01 Hard Questions — 25 Difficult Questions

Exhibit

DBS-C01 Hard Questions — 25 Difficult Questions

Exhibit