DBS-C01 Practice Test 13 — 15 Questions

Question 1

A company uses Amazon RDS for MySQL with a Multi-AZ deployment. During a recent failover, the application experienced a 2-minute downtime because it was connecting to the primary instance endpoint. The company needs to reduce failover downtime to under 30 seconds. What should be done?

Accepted Answer

Use the Multi-AZ DB cluster endpoint instead of the instance endpoint.. The Multi-AZ DB cluster endpoint provides a single DNS name that automatically routes connections to the current writer instance, eliminating the need for application-side reconnection logic. During a failover, the endpoint updates its DNS record to point to the new primary within seconds, reducing downtime to under 30 seconds. This is the recommended approach for minimizing failover disruption in Multi-AZ deployments.

Answer

Implement Amazon ElastiCache to cache database connections.

Answer

Increase the instance size to improve failover speed.

Answer

Deploy a read replica and promote it manually during failover.

Question 2

A financial services company runs an Amazon Aurora MySQL database. The application performs complex joins and aggregations on large tables, causing high CPU utilization on the writer instance. The team wants to reduce load without changing the application code. Which solution would best address this issue?

Accepted Answer

Create one or more Aurora Replicas and route read traffic to them.. Creating one or more Aurora Replicas and routing read traffic to them offloads the complex joins and aggregations from the writer instance, reducing CPU utilization without requiring application code changes. Aurora Replicas share the same underlying storage volume as the writer, so they serve read queries with minimal replication lag while the writer focuses on write operations.

Answer

Switch to asynchronous replication to reduce load on the primary.

Answer

Enable Aurora Auto Scaling to increase storage capacity.

Answer

Migrate to Aurora Serverless v2 for automatic scaling.

Question 3

A company is building a mobile application that requires users to be able to query their order history quickly. The data is stored in Amazon DynamoDB, and each user has up to 10,000 orders over time. The application needs to support pagination and filtering by order date. What is the MOST efficient way to model this data in DynamoDB?

Accepted Answer

Use user ID as the partition key and order date as the sort key. Option D is correct because using user ID as the partition key ensures all orders for a user are co-located on a single partition, enabling efficient queries. Adding order date as the sort key allows the application to filter and paginate by date range using the Query API with KeyConditionExpression, which is far more efficient than scanning or using a secondary index.

Answer

Scan the entire table and filter on user ID

Answer

Store all orders as a JSON document in a single item per user

Answer

Use user ID as the partition key and a Global Secondary Index on order date

Question 4

Which TWO factors should be considered when choosing between Amazon DynamoDB and Amazon RDS for MySQL for a new e-commerce application with variable traffic patterns?

Accepted Answer

Operational overhead: DynamoDB is serverless, RDS requires patching and provisioning.. Option B is correct because DynamoDB is a fully managed serverless service that eliminates the need for patching, provisioning, or managing servers, whereas Amazon RDS for MySQL requires manual patching, scaling, and instance provisioning. This operational overhead difference is critical for variable traffic patterns, as DynamoDB automatically handles infrastructure management, reducing administrative burden.

Answer

Ability to perform complex joins and aggregations: DynamoDB supports SQL-like queries.

Answer

Support for ACID transactions in both databases.

Answer

Encryption at rest is only available for RDS.

Question 5

A gaming company uses Amazon DynamoDB as the database for user profiles and game state. The application requires strongly consistent reads for the user's own profile, but eventually consistent reads for leaderboard queries. How should the company design the table and queries?

Accepted Answer

Use the ConsistentRead parameter set to true for profile queries and false for leaderboard queries.. DynamoDB supports both strongly consistent reads and eventually consistent reads on the same table, controlled by the `ConsistentRead` parameter in the `GetItem`, `Query`, or `Scan` API calls. Setting `ConsistentRead=true` for profile queries ensures the most up-to-date data, while `ConsistentRead=false` (the default) for leaderboard queries provides lower latency and higher throughput, which is ideal for read-heavy, non-critical data. This design avoids the cost and complexity of multiple tables or additional services.

Answer

Create two separate tables: one for strong consistency and one for eventual consistency.

Answer

Enable DynamoDB Accelerator (DAX) for strong consistency on all reads.

Answer

Configure DynamoDB Streams to replicate data to a second table for strong consistency.

Question 6

A company is running a production Amazon DynamoDB table that supports a gaming application with millions of concurrent users. The table uses on-demand capacity mode. Recently, the application started experiencing throttling (ProvisionedThroughputExceededException) during peak hours. The company wants to resolve this with minimal operational overhead. What should the company do?

Accepted Answer

Request a service quota increase for the on-demand table's maximum throughput. On-demand capacity mode in DynamoDB has a default throughput quota (typically 40,000 read/write request units per second per table, though this can vary by region and account). When traffic exceeds this soft limit, DynamoDB returns ProvisionedThroughputExceededException. Requesting a service quota increase raises this ceiling, allowing the table to handle higher bursts without throttling, and requires no architectural changes or capacity management—minimizing operational overhead.

Answer

Enable DynamoDB Accelerator (DAX) to cache reads

Answer

Partition the table across multiple tables and use application-level sharding

Answer

Switch to provisioned capacity mode with auto scaling

Question 7

Which THREE design patterns can improve the performance of a write-heavy application using Amazon DynamoDB?

Accepted Answer

Write sharding by using a composite key with a random suffix to distribute writes across partitions.. Option A is correct because write sharding with a random suffix on the partition key distributes writes evenly across multiple partitions, preventing hot partitions. This pattern avoids throttling by ensuring no single partition exceeds its write capacity limit, which is critical for write-heavy workloads in DynamoDB.

Answer

Create local secondary indexes (LSIs) for all query patterns.

Answer

Increase provisioned write capacity units (WCUs) to the maximum allowed.

Question 8

A company uses the IAM policy shown in the exhibit to control access to a DynamoDB table. The table has a partition key user_id and a sort key timestamp. The application uses the AWS SDK to query items. When a user tries to query items with a filter condition, they receive an AccessDeniedException. What is the most likely cause?

Accepted Answer

The query does not specify a partition key that matches the user's LeadingKeys condition.. The IAM policy uses a `Condition` block with `ForAllValues:StringEquals` on `dynamodb:LeadingKeys` to restrict access to items where the partition key (`user_id`) matches the caller's IAM user ID (`${aws:userid}`). When a query does not specify a partition key that satisfies this condition, the request fails with an `AccessDeniedException`. The error occurs because the query must include a partition key equal to the user's ID to pass the leading keys restriction.

Answer

The aws:userid variable is not being resolved correctly.

Answer

The policy is missing a Condition element with dynamodb:Select.

Answer

The policy does not allow the Query action.

Question 9

A gaming company runs a leaderboard application on Amazon DynamoDB. The application experiences sudden spikes in read traffic during tournaments. The table uses on-demand capacity and the reads are eventually consistent. However, some users report stale data for several seconds. What is the most likely cause?

Accepted Answer

The application is using eventually consistent reads.. The correct answer is A because eventually consistent reads in DynamoDB can return stale data for up to one second under normal conditions, but during sudden spikes in read traffic, the replication lag can extend to several seconds. The application is using eventually consistent reads, which trade immediate consistency for higher throughput and lower latency, making stale data more likely during high-traffic periods like tournaments.

Answer

The table is using on-demand capacity instead of provisioned capacity.

Answer

The table has a global secondary index (GSI) that is not updated synchronously.

Answer

The read capacity units are insufficient for the traffic spikes.

Question 10

A startup needs a fully managed relational database with automated backups and scaling. They expect unpredictable workloads. Which AWS service meets these requirements?

Accepted Answer

Amazon Aurora Serverless. Amazon Aurora Serverless is a fully managed relational database that automatically scales capacity up or down based on application demand, making it ideal for unpredictable workloads. It also provides automated backups, continuous backups to Amazon S3, and point-in-time recovery, meeting all stated requirements.

Answer

Amazon DynamoDB

Answer

Amazon Redshift

Answer

Amazon ElastiCache

Question 11

A company is migrating an on-premises Oracle database to Amazon Aurora PostgreSQL. The database is used for a financial application that requires complex joins and transactions. Which migration strategy is MOST appropriate?

Accepted Answer

Use AWS SCT to convert the schema and AWS DMS to migrate data. Option C is correct because AWS Schema Conversion Tool (SCT) is required to convert the Oracle schema (including complex joins and transaction logic) to a PostgreSQL-compatible format, and AWS Database Migration Service (DMS) performs the ongoing data migration with minimal downtime. This combination handles both schema transformation and data replication for heterogeneous migrations, which is essential for a financial application with complex joins and transactions.

Answer

Use AWS DMS with native Oracle to PostgreSQL endpoint

Answer

Use Oracle GoldenGate to replicate data to Aurora PostgreSQL

Answer

Use pg_dump to export the Oracle database and restore to Aurora PostgreSQL

Question 12

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?

Accepted Answer

DynamoDB typically deletes expired items within 48 hours. Option C is correct because 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.

Answer

The table has insufficient write capacity

Answer

TTL is not enabled on the table

Answer

The TTL attribute is set as a string instead of a number

Question 13

A healthcare application stores patient records in Amazon DynamoDB. Each record has a unique patient ID and contains sensitive health information. The application must encrypt data at rest and ensure that only authorized services can access the data. Which combination of design choices meets these requirements?

Accepted Answer

Enable DynamoDB encryption at rest using AWS KMS and use IAM policies to restrict access.. Option C is correct because DynamoDB encryption at rest using AWS KMS provides server-side encryption for sensitive patient data, while IAM policies allow fine-grained access control to ensure only authorized services can access the table. This combination directly meets both the encryption and access control requirements without unnecessary complexity or service mismatches.

Answer

Implement client-side encryption and use Lambda to validate access.

Answer

Enable S3 server-side encryption with AWS KMS and use bucket policies.

Answer

Use AWS CloudHSM for key storage and VPC endpoints for access control.

Question 14

A social media startup stores user posts in Amazon DynamoDB with a partition key of user_id and sort key of post_timestamp. The application frequently queries the five most recent posts for a given user. Which design pattern improves query performance and reduces cost?

Accepted Answer

Create a local secondary index on user_id and post_timestamp. Option D is correct because a Local Secondary Index (LSI) on user_id (hash key) and post_timestamp (range key) allows DynamoDB to efficiently query the five most recent posts for a given user without scanning the entire table. Since the LSI shares the same partition key as the base table, it provides strongly consistent reads and avoids the overhead of a separate index, reducing both latency and consumed read capacity.

Answer

Enable DynamoDB Accelerator (DAX) for the table

Answer

Increase the read capacity units on the table

Answer

Use a global secondary index on post_timestamp

Question 15

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

Accepted Answer

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

Answer

Enable DynamoDB Streams and export to Amazon Elasticsearch Service

Answer

Use DynamoDB Accelerator (DAX) to cache queries

Answer

Create a Local Secondary Index on CustomerID