DBS-C01 Workload-Specific Database Design Practice Test 19 — 15 Questions

Question 1

An application using the above IAM policy is trying to perform a Scan operation on the 'Orders' table. What will happen?

Accepted Answer

The Scan operation will fail because the explicit Deny on dynamodb:Scan overrides the Allow.. D is correct because IAM policy evaluation follows an explicit deny override: any explicit Deny statement for an action overrides any Allow for that same action, regardless of resource specificity. Since the policy includes an explicit Deny on dynamodb:Scan for all resources, the Scan operation on the 'Orders' table will be denied, even though an Allow statement grants other DynamoDB actions on that table.

Answer

The Scan operation will succeed because the Deny is on all resources but the Allow is specific to the table.

Answer

The Scan operation will succeed because the policy allows other operations on the table.

Answer

The Scan operation will fail because the policy does not explicitly allow Scan.

Question 2

A developer is designing a DynamoDB table for an order management system using the above CloudFormation template. The application needs to query orders by CustomerID. The current design has a GSI on CustomerID. However, the developer notices that the GSI has low write throughput and often throttles. What is the most cost-effective way to improve write throughput on the GSI?

Accepted Answer

Increase the WriteCapacityUnits of the GSI from 5 to a higher value.. The GSI's write throughput is independent of the base table's write capacity. Increasing the GSI's WriteCapacityUnits directly addresses the throttling on the GSI without affecting the base table's cost or performance. This is the most cost-effective approach because it only scales the specific resource that is throttling, rather than over-provisioning the entire table or adding unnecessary infrastructure.

Answer

Change the ProjectionType of the GSI to KEYS_ONLY.

Answer

Use Amazon SQS to buffer writes to the table.

Answer

Increase the WriteCapacityUnits of the table.

Question 3

An application is receiving the error shown in the exhibit. The application uses connection pooling. The RDS instance is a db.r5.large with max_connections set to 1000. What is the most likely cause?

Accepted Answer

The connection pool in the application is not releasing idle connections.. If the application's connection pool does not release idle connections, it can exhaust the maximum connections even though max_connections is set appropriately. Option A is incorrect because if the security group blocked connections, the application would receive a timeout or 'connection refused' error, not an error indicating max connections are reached. Option B is incorrect because max_connections is set to 1000, which is the default for a db.r5.large instance and is typically sufficient; the instance size supports that value. Option D is incorrect because a different VPC would cause network connectivity issues (e.g., timeout) rather than a 'too many connections' error, which indicates the connection is established but the limit is reached.

Answer

The security group is blocking incoming connections.

Answer

The max_connections parameter is set too low for the instance size.

Answer

The RDS instance is in a different VPC than the application.

Question 4

A company needs to store time-series data from IoT sensors. Each sensor sends a reading every minute. The data is rarely accessed after 30 days. The query pattern is to retrieve all readings for a specific sensor within a time range. Which AWS database is most cost-effective?

Accepted Answer

Amazon Timestream. Amazon Timestream is purpose-built for time-series data, offering automatic tiered storage (in-memory for recent data and magnetic for historical data) and built-in time-series functions. For IoT sensors sending readings every minute with rare access after 30 days, Timestream's cost-effective storage tiering and efficient querying for a specific sensor within a time range make it the most suitable and economical choice.

Answer

Amazon Redshift

Answer

Amazon DynamoDB with Time-to-Live (TTL)

Answer

Amazon RDS for PostgreSQL with partitioning

Question 5

A company runs a global application using Amazon Aurora Global Database. The primary region is us-east-1, and secondary regions are eu-west-1 and ap-southeast-1. The application reports that writes to the primary are taking longer than expected. What is the most likely cause?

Accepted Answer

The primary DB instance is under-provisioned.. Amazon Aurora Global Database uses asynchronous replication from the primary region to secondary regions. Writes to the primary are committed locally and do not wait for replication to complete. Therefore, replication to secondary regions does not introduce synchronous commit latencies. The most likely cause of slower writes to the primary is an under-provisioned primary DB instance that cannot handle the write workload efficiently.

Answer

Multi-AZ failover occurred in the primary region.

Answer

The Global Database replication to secondary regions is causing synchronous commit latencies.

Answer

Read replicas in secondary regions are overloaded.

Question 6

A company uses Amazon DynamoDB to store user profiles. The access pattern is mostly GetItem by user_id. They want to reduce costs. Which design change is most effective?

Accepted Answer

Use DynamoDB Standard-IA table class for the user profiles table.. DynamoDB Standard-IA (Infrequent Access) table class offers a lower storage cost for data that is accessed infrequently, while still providing the same single-digit millisecond latency for GetItem operations. Since the access pattern is mostly GetItem by user_id, and assuming the data is not accessed frequently enough to justify the higher per-request cost of Standard, Standard-IA can significantly reduce overall costs. The trade-off is a slightly higher per-request cost, but for predominantly read-heavy workloads with low access frequency, the storage savings outweigh the request cost increase.

Answer

Increase the read capacity units to reduce throttling.

Answer

Add a Global Secondary Index on an additional attribute.

Answer

Add DynamoDB Accelerator (DAX) for caching.

Question 7

A company needs to store and query graph data (nodes and edges) for a social network. They require low-latency traversals. Which AWS database is best suited?

Accepted Answer

Amazon Neptune. Amazon Neptune is a fully managed graph database service optimized for storing and querying highly connected data, such as social network nodes and edges. It supports both property graph (Gremlin) and RDF (SPARQL) models, enabling low-latency traversals of complex relationships. This makes it the ideal choice for workloads requiring efficient graph queries over interconnected data.

Answer

Amazon RDS for MySQL

Answer

Amazon ElastiCache for Redis

Answer

Amazon DynamoDB

Question 8

A data analyst reports that a nightly ETL job to Amazon Redshift is failing with timeout errors shown in the exhibit. The cluster is a dc2.large with 2 nodes. The ETL job inserts large volumes of data. What is the most likely cause?

Accepted Answer

The cluster has insufficient disk space for the data load.. The dc2.large node type has a fixed storage limit of 160 GB per node (320 GB total for 2 nodes). When an ETL job inserts large volumes of data and the cluster runs out of disk space, Redshift cannot write new rows, causing the load to hang and eventually time out. Insufficient disk space is a common cause of timeout errors during bulk inserts because the database cannot complete the write operations.

Answer

The cluster has reached the maximum number of connections.

Answer

The workload manager (WLM) queue timeout is too low.

Answer

The security group is blocking inbound traffic from the ETL server.

Question 9

A company is designing a document management system using Amazon DocumentDB. Each document is up to 10 MB. The application needs to retrieve multiple documents by their IDs in a single request. The IDs are known at query time. Which query pattern is most efficient?

Accepted Answer

Use a find operation with the $in operator on the _id field.. The `$in` operator on the `_id` field allows DocumentDB to use the primary key index directly, retrieving multiple documents in a single round trip with minimal overhead. This is the most efficient pattern because it leverages the clustered index on `_id` and avoids the performance penalty of multiple queries or full scans.

Answer

Use a find operation with the $or operator on the _id field.

Answer

Use a scan operation with a filter on the _id field.

Answer

Issue multiple get operations in parallel.

Question 10

A social media application stores user posts in a DynamoDB table with a partition key of user_id and a sort key of timestamp. The most frequent query is to retrieve the 10 most recent posts for a given user. Which secondary index design would optimize this query?

Accepted Answer

Create an LSI with user_id as the partition key and timestamp as the sort key.. A Local Secondary Index (LSI) on the base table with the same partition key (user_id) and a sort key of timestamp allows efficient retrieval of the 10 most recent posts for a given user. An LSI shares the same partition key as the base table, so querying by user_id with ScanIndexForward=false and Limit=10 returns the most recent items without needing to replicate data or incur additional write costs.

Answer

Create a GSI with user_id as the partition key and timestamp as the sort key.

Answer

Use DynamoDB Streams to populate an Amazon Elasticsearch cluster for search queries.

Answer

Query the base table using the sort key timestamp with a limit of 10.

Question 11

A company runs an OLTP workload on an RDS for MySQL instance. The database has a table with 50 million rows. The application frequently runs queries that join this table with a small lookup table (1000 rows) using a foreign key. The queries are slow. Which design change would most improve performance?

Accepted Answer

Add an index on the foreign key column in the large table.. The query joins a large table (50M rows) with a small lookup table (1000 rows) on a foreign key column. Without an index on the foreign key column in the large table, MySQL must perform a full table scan for each join, leading to slow performance. Adding an index on that column allows MySQL to use an index lookup (e.g., B-tree) to quickly locate matching rows, dramatically reducing query time.

Answer

Partition the large table by the foreign key column.

Answer

Scale up the RDS instance to a larger size.

Answer

Create a read replica and direct all read queries to it.

Question 12

A startup wants to store session data for a web application. Each session is small (under 1 KB) and accessed frequently with low latency. The data can be ephemeral and does not require complex queries. Which AWS database service is most suitable?

Accepted Answer

Amazon ElastiCache for Redis. Amazon ElastiCache for Redis is the most suitable choice because it is an in-memory data store that provides sub-millisecond latency for frequent reads and writes, ideal for ephemeral session data under 1 KB. Redis supports key-value storage with built-in time-to-live (TTL) expiration, which automatically removes stale sessions without additional application logic, and its simple data model avoids the overhead of complex queries.

Answer

Amazon DynamoDB

Answer

Amazon RDS for PostgreSQL

Answer

Amazon Neptune

Question 13

A company is designing a database for a global IoT application that ingests millions of events per second. Each event includes a device ID, timestamp, and sensor readings. The requirement is to store data for historical analysis and to support queries that aggregate data by device ID over time ranges. The team needs a cost-effective solution that can scale write throughput. Which database design is most appropriate?

Accepted Answer

Use Amazon DynamoDB with a table keyed by device ID (partition) and timestamp (sort).. Amazon DynamoDB with a composite primary key of device ID (partition key) and timestamp (sort key) is ideal for this IoT workload because it provides scalable write throughput to handle millions of events per second, while the sort key enables efficient time-range queries and aggregation by device ID. DynamoDB's fully managed, serverless architecture ensures cost-effectiveness by automatically scaling capacity and charging only for consumed resources, making it suitable for high-velocity time-series data.

Answer

Use Amazon RDS for MySQL with Multi-AZ and auto-scaling storage.

Answer

Use Amazon Redshift with a schema optimized for time-series data.

Answer

Use Amazon ElastiCache for Redis with persistence enabled.

Question 14

A company runs a financial application that requires ACID transactions on a relational database. The workload has a high volume of writes and reads, and the team wants to minimize operational overhead. Which AWS database service should they choose?

Accepted Answer

Amazon Aurora (MySQL-compatible). Amazon Aurora (MySQL-compatible) is the correct choice because it provides full ACID transaction support required for a financial application, while delivering high throughput for both reads and writes through its distributed, SSD-backed storage architecture. Aurora also minimizes operational overhead by automating tasks like replication, backups, and failover, and it offers up to 5x the throughput of standard MySQL without requiring manual sharding or tuning.

Answer

Amazon RDS for Oracle

Answer

Amazon DynamoDB with transactions enabled

Answer

Amazon ElastiCache for Memcached

Question 15

A gaming company wants to store player profiles and game state. The data is accessed via a REST API and must be highly available with single-digit millisecond latency. The schema is simple and may evolve over time. Which database should they use?

Accepted Answer

Amazon DynamoDB. Amazon DynamoDB is the correct choice because it is a fully managed NoSQL key-value and document database that delivers single-digit millisecond latency at any scale, making it ideal for high-availability gaming workloads. Its schema-less design allows the player profile and game state schema to evolve over time without downtime or complex migrations, and it integrates seamlessly with REST APIs via AWS SDKs or API Gateway.

Answer

Amazon S3

Answer

Amazon RDS for PostgreSQL

Answer

Amazon Redshift