DBS-C01 Practice Test 23 — 15 Questions

Question 1

A startup is building a real-time chat application that requires storing messages with high write throughput and low-latency reads. The data model is simple: each message has a conversation ID, timestamp, and content. Which database design is MOST appropriate?

Accepted Answer

Amazon DynamoDB with conversation_id as partition key and timestamp as sort key. Option C is correct because DynamoDB is a NoSQL database that can handle high write throughput and low-latency reads with a simple key-value model. Option A is wrong because RDS may struggle with high write throughput. Option B is wrong because Timestream is for time-series, not chat messages. Option D is wrong because Redshift is for analytics.

Answer

Amazon RDS for MySQL with a single table and indexes on conversation_id and timestamp

Answer

Amazon Timestream to store messages as time-series data

Answer

Amazon Redshift with columnar storage and compression

Question 2

A retail company uses Amazon DynamoDB to store shopping cart data. The cart items are frequently updated as users add or remove products. The application reads the entire cart each time the user views it. The cart size averages 50 KB but can reach up to 400 KB. The company wants to reduce read costs and improve performance. Which design change would be most effective?

Accepted Answer

Compress the cart items before storing them in DynamoDB and decompress on read.. Option A is correct because compressing large attributes reduces read capacity unit consumption. Option B is wrong because caching with DAX adds cost and complexity. Option C is wrong because normalization would increase read costs due to more items. Option D is wrong because vertical scaling of DynamoDB is not applicable; it's serverless.

Answer

Switch to larger DynamoDB instance types to handle larger items.

Answer

Use DynamoDB Accelerator (DAX) to cache the cart data.

Answer

Normalize the cart data into separate tables for cart headers and line items.

Question 3

A company is designing a database for a real-time leaderboard in a mobile game. The leaderboard updates thousands of times per second and must return the top 100 scores with minimal latency. Which AWS database service is most suitable for this workload?

Accepted Answer

Amazon ElastiCache for Redis. Amazon ElastiCache for Redis with sorted sets is optimized for real-time leaderboards because it provides in-memory operations with O(log N) complexity for adding scores and O(log N+m) for retrieving top N items. Option B (DynamoDB) is wrong because it is not designed for sorted range queries with frequent updates at high throughput without secondary indexes and provisioned throughput. Option C (Aurora) is wrong because it is a relational database with higher latency and not optimized for this specific pattern. Option D (Neptune) is wrong because it is a graph database not suited for leaderboard operations.

Answer

Amazon Neptune

Answer

Amazon DynamoDB

Answer

Amazon Aurora

Question 4

A company wants to store and analyze time-series sensor data from millions of IoT devices. The data is append-only and rarely updated. Queries aggregate data over time ranges. Which AWS database service is most cost-effective and performant for this workload?

Accepted Answer

Amazon Timestream. Option A is correct because Timestream is purpose-built for time-series data, offering automatic tiering and optimized aggregation queries. Option B (DynamoDB) can handle time-series but requires careful design and is less cost-effective for large-scale. Option C (RDS) is relational and not optimized for time-series. Option D (Redshift) is for analytical workloads but is heavier and more expensive for IoT sensor data.

Answer

Amazon DynamoDB with time-series design pattern

Answer

Amazon Redshift

Answer

Amazon RDS for MySQL with partitioning by date

Question 5

Refer to the exhibit. An application uses Cognito identity pools to authenticate users and dynamodb:LeadingKeys condition to restrict access to items where the partition key matches the user's sub. Some users report that they can see items belonging to other users. What is the most likely cause?

Accepted Answer

The ForAllValues:StringEquals should be ForAnyValue:StringEquals. Option B is correct because ForAllValues:StringEquals allows the action if the condition value matches at least one of the request values, but if the request has multiple leading keys (e.g., batch operations), it may pass incorrectly. ForAllValues checks that every key in the request matches the condition; if no keys are present, it also passes. The correct condition should be ForAnyValue:StringEquals to ensure at least one key matches. Option A is wrong because the condition is set. Option C is wrong unless the condition is missing. Option D is wrong because the audience is not relevant to item-level access.

Answer

The IAM policy does not include a condition for the table

Answer

The Cognito identity pool is not configured correctly

Answer

The table ARN is incorrect

Question 6

A company needs to store JSON documents that require complex querying on nested attributes. The database must support ACID transactions and be fully managed. Which service should they use?

Accepted Answer

Amazon Aurora MySQL. Amazon Aurora MySQL-Compatible Edition supports JSON data type and ACID transactions. Option B (DynamoDB) is NoSQL but does not support complex nested queries natively. Option C (DocumentDB) is MongoDB-compatible but not ACID. Option D (Neptune) is graph database.

Answer

Amazon DocumentDB (with MongoDB compatibility)

Answer

Amazon DynamoDB

Answer

Amazon Neptune

Question 7

A media company stores video metadata in Amazon Aurora MySQL. The application performs frequent range queries on a 'creation_date' column. The table has 10 million rows. The team notices that queries filtering on 'creation_date' are slow despite an index on that column. The query pattern is: SELECT * FROM videos WHERE creation_date BETWEEN '2023-01-01' AND '2023-01-31' ORDER BY creation_date LIMIT 100. The execution plan shows a full index scan. What is the MOST likely cause?

Accepted Answer

The query selects all columns, causing excessive table access. A full index scan occurs when the query selects a large portion of the table; however, the issue may be that the index is not selective enough, leading to many rows being read. But the key problem is that the index on 'creation_date' is a B-tree, and the query uses LIMIT with ORDER BY, so the optimizer may decide to scan the entire index to find matching rows because the range is wide. However, the correct answer is that the query is selecting all columns (SELECT *), causing the database to fetch rows from the table after the index lookup, which is expensive. Option A is wrong because the index is being used (full index scan). Option B is wrong because partitioning would help if designed correctly, but the issue is more fundamental. Option D is wrong because a composite index with columns in wrong order may not help.

Answer

The index should be a composite index on (creation_date, id)

Answer

The table is not partitioned by creation_date

Answer

The index on creation_date is not being used

Question 8

A company is designing a database for a global e-commerce application that requires low-latency reads and writes with strong consistency. The application is expected to handle millions of requests per second and requires high availability. Which AWS database service is most suitable for this workload?

Accepted Answer

Amazon DynamoDB with DynamoDB Accelerator (DAX). Amazon DynamoDB with DAX can provide single-digit millisecond latency at scale, supports strongly consistent reads, and is designed for high availability across multiple AWS regions. Option A (Amazon RDS MySQL with Multi-AZ) does not provide the same scalability for millions of requests per second. Option C (Amazon ElastiCache for Memcached) is a caching layer, not a primary database. Option D (Amazon S3) is object storage, not suitable for low-latency transactional workloads.

Answer

Amazon ElastiCache for Memcached

Answer

Amazon S3 with S3 Select

Answer

Amazon RDS for MySQL with Multi-AZ deployment

Question 9

A company uses Amazon Aurora MySQL for its online transaction processing (OLTP) application. Recently, read traffic has increased significantly, causing performance issues. The company wants to offload read traffic with minimal application changes. Which solution should they implement?

Accepted Answer

Create one or more Aurora Replicas. Aurora Replicas are asynchronous read replicas that can handle read traffic without application changes (just use different endpoint). Option A (Multi-AZ) is for high availability, not read scaling. Option C (ElastiCache) requires application changes. Option D (DynamoDB) would require a full redesign.

Answer

Enable Multi-AZ deployment

Answer

Migrate to Amazon DynamoDB

Answer

Add an Amazon ElastiCache cluster in front of Aurora

Question 10

A startup is building a social media analytics platform that requires storing time-series data with frequent writes and queries for the last hour. Which AWS database service is BEST suited for this workload?

Accepted Answer

Amazon Timestream. Option C is correct because Timestream is purpose-built for time-series data and supports high-frequency writes and recent data queries. Option A is wrong because RDS is not optimized for time-series. Option B is wrong because DynamoDB can store timestamps but lacks time-series optimizations. Option D is wrong because Neptune is a graph database.

Answer

Amazon RDS with MySQL

Answer

Amazon Neptune

Answer

Amazon DynamoDB

Question 11

A company uses Amazon Redshift for data warehousing. They run a query that joins a large fact table (10 billion rows) with a small dimension table (1 million rows). The query is slow. The distribution style of the fact table is AUTO, and the dimension table has DISTSTYLE ALL. The join key is user_id. What is the MOST likely reason for the poor performance?

Accepted Answer

The fact table's distribution key is not user_id, causing redistribution. Redshift distributes data across slices. For joins, distribution keys should align to avoid data redistribution. With DISTSTYLE ALL on the dimension table, it is copied to all nodes, which is good. The fact table uses AUTO, which may choose a suboptimal distribution key. If the fact table is distributed by user_id, it should be fine. However, the issue may be that the dimension table is not sorted on the join key, or the fact table's distribution key is not user_id. Option B is wrong because DISTSTYLE ALL is actually beneficial for small tables. Option C is wrong because no sort key on dimension table is less critical. Option D is wrong because column compression helps scan performance but not joins.

Answer

The dimension table does not have a sort key on user_id

Answer

The dimension table uses DISTSTYLE ALL, which is inefficient for joins

Answer

The fact table should have column compression disabled for the join key

Question 12

A team is migrating an on-premises Microsoft SQL Server database to AWS. The database is used for reporting and analytics, with complex queries that join multiple tables. The team wants to minimize application changes and ensure compatibility. Which AWS service should they use?

Accepted Answer

Amazon RDS for SQL Server. Amazon RDS for SQL Server provides native SQL Server compatibility, minimizing application changes. Option B (Amazon RDS for MySQL) is a different database engine requiring SQL dialect changes. Option C (Amazon DynamoDB) is NoSQL and not suited for complex joins. Option D (Amazon Redshift) is a data warehouse optimized for analytics but requires re-engineering of queries and schema.

Answer

Amazon RDS for MySQL

Answer

Amazon Redshift

Answer

Amazon DynamoDB

Question 13

A financial services company needs a database to store transaction records with strong consistency and the ability to run complex analytical queries. The data volume is in the terabytes and is expected to grow. The company also needs point-in-time recovery. Which AWS database solution meets these requirements?

Accepted Answer

Amazon Redshift with automated snapshots. Amazon Redshift with automated snapshots provides point-in-time recovery and is optimized for analytical queries on large datasets. Option A (DynamoDB) does not support complex SQL analytics. Option B (RDS MySQL) is for OLTP and may not handle terabytes of analytical queries efficiently. Option D (ElastiCache) is in-memory caching.

Answer

Amazon RDS for MySQL with read replicas

Answer

Amazon ElastiCache for Redis with AOF persistence

Answer

Amazon DynamoDB with on-demand backup

Question 14

A financial services company needs a database to store transaction records with strict ACID compliance and the ability to run complex JOIN queries for reporting. The workload is read-heavy with occasional batch inserts. Which AWS database service should they choose?

Accepted Answer

Amazon RDS for PostgreSQL. Option A is correct because Amazon RDS for PostgreSQL is a relational database that provides ACID compliance and supports complex joins. Option B is wrong because DynamoDB is NoSQL and does not support complex joins. Option C is wrong because Aurora Serverless is good but may not be the best for complex joins; however, it is still relational. Option D is wrong because Timestream is for time-series.

Answer

Amazon DynamoDB

Answer

Amazon Aurora Serverless

Answer

Amazon Timestream

Question 15

A developer is configuring IAM permissions for a Lambda function that accesses a DynamoDB table named 'Orders'. The policy shown is attached to the Lambda execution role. The function needs to delete items but only if the item contains only 'order_id' and 'status' attributes. Which statement about this policy is correct?

Accepted Answer

The function can only delete items that contain exactly the attributes 'order_id' and 'status'. Option D is correct because the condition restricts DeleteItem to items that have only those attributes. Option A is wrong because the condition is on the delete action, not on the resource. Option B is wrong because the policy does allow DeleteItem with the condition. Option C is wrong because the function has GetItem and Query permissions.

Answer

The function can delete any item in the Orders table because the condition is on the resource

Answer

The function cannot delete any items because the DeleteItem action is not allowed

Answer

The function cannot call Query on the table because it is not listed in the actions