DBS-C01 Workload-Specific Database Design Practice Test 28 — 15 Questions

Question 1

A company is building a content management system that stores articles, images, and user comments. Articles are text-heavy and need full-text search. Images are binary files. Comments are relational with user IDs. Which TWO AWS services should be combined to best support this workload?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Amazon OpenSearch Service for full-text search. Amazon OpenSearch Service is correct because it provides full-text search capabilities, which are essential for the text-heavy articles in the content management system. It supports advanced querying, stemming, and relevance scoring, making it ideal for searching article content. Amazon S3 is correct because it is designed for storing binary files like images, offering high durability, scalability, and cost-effectiveness for object storage.

Answer

Amazon ElastiCache for Redis for caching

Answer

Amazon DynamoDB for articles and comments

Answer

Amazon RDS for MySQL for articles and comments

Question 2

An IAM policy is attached to an application role that accesses a DynamoDB table named 'Orders'. The table has a global secondary index named 'OrderDateIndex'. The application needs to write new orders and query the index. Based on the exhibit, will the application be able to perform these operations?

Accepted Answer

Yes, the policy allows both writes and querying the index.. The IAM policy grants `dynamodb:PutItem` on the table and `dynamodb:Query` on the index. Since the policy explicitly allows both actions on their respective ARNs, the application can write new orders to the 'Orders' table and query the 'OrderDateIndex' global secondary index. Option B is correct because the policy covers both required operations.

Answer

Yes, but only writes are allowed; index queries are denied.

Answer

No, the policy does not grant access to the index.

Answer

No, the policy denies Query on the index.

Question 3

A company runs an online auction platform on AWS. The application uses Amazon DynamoDB as the primary database, with a table 'Auctions' that has a partition key 'auction_id' (String) and sort key 'end_time' (Number). The table also has a global secondary index (GSI) on 'status' (String) and 'current_bid' (Number). The application frequently queries for active auctions sorted by current bid. Recently, the team noticed that queries on the GSI for active auctions with a high current_bid are returning results slowly. The DynamoDB table has 10,000 write capacity units (WCU) and 30,000 read capacity units (RCU) provisioned. The GSI has 5,000 RCU provisioned. The team suspects throttling on the GSI. What is the most likely cause of the slow queries?

Accepted Answer

The GSI's provisioned RCU is insufficient due to hot partitions.. The GSI has 5,000 RCU provisioned, but if the GSI's partition key (status) and sort key (current_bid) lead to a hot partition—for example, many active auctions have the same status and similar current_bid—that single partition can throttle even if the total RCU is not fully utilized. This is because DynamoDB distributes RCU evenly across partitions, and a hot partition can exceed its allocated RCU, causing throttling on that partition. Option B is incorrect because the index key schema (status, current_bid) is actually appropriate for querying active auctions sorted by current bid; the issue is hot partitions, not inefficiency. Option C is incorrect because WCU throttling does not directly affect read performance on the GSI. Option D is incorrect because the table's RCU is separate from the index's RCU, and the index's RCU is the relevant factor.

Answer

The GSI key schema is inefficient for the query pattern.

Answer

The table's WCU is too low, causing throttling on writes that affects reads.

Answer

The table's RCU is too low for the application's read load.

Question 4

A company is running a MySQL database on Amazon RDS for a customer relationship management (CRM) application. The database has a table named 'contacts' with over 100 million rows. The application frequently runs queries to find contacts by email address. The email column has a B-tree index. Recently, the application started experiencing slow query performance. The team checked CloudWatch metrics and saw that the ReadIOPS for the RDS instance is consistently at 80% of the provisioned IOPS limit. The instance type is db.r5.large with 3000 provisioned IOPS (gp2). The buffer pool hit ratio is 95%. What is the most cost-effective design change to improve query performance?

Accepted Answer

Upgrade the RDS instance to db.r5.xlarge with 6000 provisioned IOPS.. Upgrading to a db.r5.xlarge with 6,000 provisioned IOPS (gp2) doubles the IOPS capacity, directly addressing the high ReadIOPS utilization (80%) without requiring application changes. This is the most cost-effective solution as it leverages the existing RDS infrastructure. Option B is wrong because migrating to DynamoDB would require significant application rework and is not necessary when the bottleneck is IOPS. Option C is wrong because OpenSearch adds unnecessary complexity and cost for a simple email lookup. Option D is wrong because the buffer pool hit ratio is already at 95%, indicating that increasing memory would yield minimal benefit.

Answer

Migrate the contacts table to Amazon DynamoDB with email as partition key.

Answer

Implement an Amazon OpenSearch Service cluster for email search.

Answer

Increase the buffer pool size by changing to a memory-optimized instance.

Question 5

A company is designing a database for an IoT application that receives millions of sensor readings per second. Each reading is a small JSON payload (timestamp, device_id, metric, value). The primary query pattern retrieves the most recent reading for a given device. Which AWS database service is BEST suited for this workload?

Accepted Answer

Amazon DynamoDB. Amazon DynamoDB is best suited because it is a fully managed NoSQL key-value database that delivers single-digit millisecond latency at any scale. The primary query pattern—retrieving the most recent reading for a given device—maps directly to a DynamoDB table with a composite primary key (device_id as partition key, timestamp as sort key) and a Query operation with ScanIndexForward=false and Limit=1. This design handles millions of writes per second with consistent performance, unlike relational databases that struggle with such high-velocity ingestion.

Answer

Amazon Aurora

Answer

Amazon ElastiCache for Redis

Answer

Amazon RDS for MySQL

Question 6

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?

Accepted Answer

The Deny statement does not prevent retrieval of all attributes when no ProjectionExpression is specified.. 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.

Answer

The Deny statement uses the wrong condition key; it should use 'dynamodb:Select' instead of 'dynamodb:Attributes'.

Answer

The policy should use 'dynamodb:ReturnValues' condition key.

Answer

The Allow statement should include 'dynamodb:Scan' to allow Query operations.

Question 7

A company wants to migrate their on-premises Oracle database to Amazon RDS for Oracle. They have a complex data loading process that uses Oracle Data Pump. Which migration approach is MOST efficient and minimizes downtime?

Accepted Answer

Use AWS Database Migration Service (DMS) with ongoing replication from the source Oracle database.. AWS DMS with ongoing replication (change data capture, CDC) is the most efficient approach for migrating an Oracle database to Amazon RDS for Oracle with minimal downtime. It allows a full load of the existing data followed by continuous replication of changes from the source until cutover, reducing the outage window to seconds or minutes. This directly addresses the requirement to minimize downtime while handling complex data loading processes.

Answer

Use AWS Schema Conversion Tool (SCT) to convert the schema and then copy data files directly.

Answer

Take a physical backup of the on-premises database and restore to RDS.

Answer

Export data using Oracle Data Pump and import into RDS.

Question 8

A company runs a MongoDB-compatible workload on Amazon DocumentDB. They notice that many read requests are returning stale data even though reads are directed to the primary instance. What is the MOST likely cause?

Accepted Answer

The application is using a read preference that allows secondary reads.. The most likely cause of stale reads from the primary instance is that the application is using a read preference that allows secondary reads. In Amazon DocumentDB, even if the connection string specifies the primary endpoint, the MongoDB driver's read preference setting (e.g., `secondaryPreferred` or `nearest`) can cause reads to be served from replica instances, which may have replication lag and thus return stale data. The default read preference is `primary`, but if the application explicitly sets a different preference, reads can be directed to secondaries without the developer realizing it.

Answer

The application's session is pinned to a secondary replica despite requesting the primary.

Answer

The primary instance is experiencing high CPU utilization, causing delayed writes.

Answer

The storage volume is using the default eventually consistent configuration for primary reads.

Question 9

A company runs a critical PostgreSQL database on Amazon RDS Multi-AZ. They need to perform a major version upgrade (e.g., from 12 to 13) with minimal downtime. Which approach should they take?

Accepted Answer

Create a read replica of the DB instance, perform the major version upgrade on the replica, then promote the replica to a new primary and update the connection string.. It leverages Amazon RDS read replicas to perform a major version upgrade with minimal downtime. By creating a read replica, upgrading it to PostgreSQL 13, and then promoting it to a new primary, you avoid any downtime on the original primary during the upgrade process. The promotion is a fast operation, and traffic is redirected by updating the connection string, resulting in only a brief interruption.

Answer

Take a snapshot, restore as a new instance with the upgraded engine version, and redirect traffic.

Answer

Initiate a major version upgrade directly on the Multi-AZ instance; the upgrade will be applied during the next maintenance window with minimal downtime.

Answer

Modify the DB instance to disable Multi-AZ, perform the upgrade, then re-enable Multi-AZ.

Question 10

A web application uses Amazon DynamoDB as its database. The application frequently queries items using a secondary index. The index's partition key has high cardinality, but the query latency is higher than expected. Which action would MOST likely improve query performance?

Accepted Answer

Increase the read capacity units (RCU) for the global secondary index.. A Global Secondary Index (GSI) has its own provisioned read capacity, separate from the base table. If the GSI's read capacity units (RCU) are insufficient to handle the query workload, requests may be throttled, leading to higher latency. Increasing the RCU for the GSI directly addresses this bottleneck by allowing more concurrent read operations against the index.

Answer

Use a Scan operation instead of Query to retrieve items from the index.

Answer

Add additional sort key attributes to the index to better distribute the data.

Answer

Convert the global secondary index to a local secondary index.

Question 11

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?

Accepted Answer

Use the SELECT INTO OUTFILE S3 extension to export the query results directly to S3.. 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.

Answer

Use the COPY command from Amazon Redshift to pull data from RDS.

Answer

Use the AWS Schema Conversion Tool (SCT) to extract the data to S3.

Answer

Use mysqldump to export the tables and then upload the files to S3.

Question 12

A company recently migrated their PostgreSQL database to Amazon Aurora PostgreSQL. They notice that the average query latency has increased, especially for complex read queries. The DB instance size is adequate and CPU utilization is below 50%. What is the MOST likely cause of the increased latency?

Accepted Answer

The application is sending all read queries to the primary instance instead of offloading them to Aurora Replicas.. If all read queries are sent to the primary (writer) instance, it must handle both writes and reads, leading to contention and increased latency. Aurora Replicas are designed to offload read traffic, and by not using them, the primary instance becomes a bottleneck even if CPU utilization is low. This is a common misconfiguration after migration, as applications often retain the original connection string pointing to the primary endpoint.

Answer

The Auto Scaling policy for the Aurora cluster is set to scale down too aggressively.

Answer

The Aurora cluster volume uses a smaller buffer cache than the original RDS instance.

Answer

The Aurora cluster is using the parallel query feature which adds overhead for complex queries.

Question 13

A startup is building a mobile app backend using Amazon DynamoDB. They anticipate unpredictable traffic spikes. Which DynamoDB feature should they use to handle the spikes without manual intervention?

Accepted Answer

Enable DynamoDB Auto Scaling for read and write capacity.. DynamoDB Auto Scaling (option B) automatically adjusts the provisioned read and write capacity based on actual traffic patterns, using CloudWatch alarms and the Application Auto Scaling service. This allows the startup to handle unpredictable spikes without manual intervention, as the service will increase capacity during high demand and decrease it during low demand, ensuring consistent performance and cost efficiency.

Answer

Use DynamoDB Accelerator (DAX) as a cache layer.

Answer

Set up a TTL (Time to Live) to automatically expire old items.

Answer

Implement DynamoDB Global Tables for multi-region replication.

Question 14

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 15

A company is designing a database for a global e-commerce platform that requires low-latency reads and writes from multiple AWS Regions. The database must support ACID transactions and complex queries with joins. Which TWO services should they consider? (Choose two.)

Accepted Answer

Amazon RDS for MySQL with cross-Region read replicas. Both Amazon Aurora with Aurora Global Database and Amazon RDS for MySQL with cross-Region read replicas support ACID transactions and complex queries with joins. They can provide low-latency reads from multiple regions through read replicas in each region, though writes are directed to a single primary region, which may impact write latency from non-primary regions. Amazon DynamoDB Global Tables provide multi-region writes but do not support joins for complex queries, so they cannot fully meet the requirements. ElastiCache Redis and Amazon Redshift lack the necessary ACID and join capabilities.

Answer

Amazon DynamoDB with Global Tables

Answer

Amazon ElastiCache for Redis with global datastore

Answer

Amazon Redshift with cross-Region snapshots