DBS-C01 Mock Exam 91 — 25 Questions

Question 1

A company is migrating a 2 TB Oracle database to Amazon Aurora MySQL. The database has custom PL/SQL packages and triggers. The migration must be completed with minimal application changes. Which THREE steps should the company take? (Choose three.)

Accepted Answer

Use AWS DMS to perform live migration with ongoing replication.. AWS DMS supports live migration with ongoing replication, which is essential for migrating a 2 TB Oracle database to Amazon Aurora MySQL with minimal downtime. It can handle continuous changes from the source while the target is being loaded, ensuring near-zero application disruption.

Answer

Use Amazon S3 to store and transfer database dump files.

Answer

Manually rewrite all PL/SQL packages to MySQL syntax.

Question 2

A gaming company uses Amazon DynamoDB to store player profiles and game state. The access patterns include: (1) lookup by player ID, (2) query by game ID for recent games, and (3) leaderboard queries sorted by score. The current single-table design is causing hot partitions on the leaderboard queries. What design change should the company implement to resolve hot partitions?

Accepted Answer

Create a GSI with the game ID as the partition key and a composite sort key of score and timestamp.. Option C is correct because creating a Global Secondary Index (GSI) with game ID as the partition key and a composite sort key of score and timestamp allows efficient leaderboard queries without hot partitions. This design distributes write activity across multiple partitions by game ID, while the composite sort key enables sorted queries by score and timestamp within each game, avoiding the hot partition issue caused by the original single-table design.

Answer

Increase the read capacity units (RCUs) on the base table to handle the load.

Answer

Enable DynamoDB Accelerator (DAX) to cache frequent leaderboard queries.

Answer

Shard the table by player ID and use application-level aggregation for leaderboards.

Question 3

A company is moving a legacy on-premises Oracle database to AWS. The database has large tables with complex stored procedures and triggers. The company wants to minimize application changes. Which THREE AWS database services should the database specialist consider? (Choose THREE.)

Accepted Answer

Amazon RDS for Oracle. Amazon RDS for Oracle (Option C) is correct because it provides native Oracle compatibility, allowing the company to migrate the legacy Oracle database with minimal application changes. It supports large tables, complex stored procedures, and triggers without requiring code modifications, as it uses the same Oracle Database engine.

Answer

Amazon Redshift

Answer

Amazon DynamoDB

Question 4

A company is designing a database for a global e-commerce platform that requires sub-millisecond read latencies from multiple AWS regions. The data is mostly read, with occasional writes. Which database solution meets these requirements?

Accepted Answer

Amazon DynamoDB with Global Tables.. Amazon DynamoDB with Global Tables is the correct choice because it provides a fully managed, multi-Region, multi-master database that replicates data across AWS Regions with sub-second latency, enabling sub-millisecond read latencies for a globally distributed, mostly-read workload. The occasional writes are handled efficiently by the multi-master design, which automatically resolves conflicts using last-writer-wins semantics, ensuring strong eventual consistency.

Answer

Amazon ElastiCache for Redis with global datastore.

Answer

Amazon Aurora MySQL with Aurora Global Database.

Answer

Amazon RDS for MySQL with Multi-AZ and read replicas.

Question 5

A gaming company uses Amazon DynamoDB to store player profiles with partition key player_id. The access pattern is to retrieve profiles for multiple players in a single request. The application currently makes separate GetItem calls, causing high latency. Which design pattern reduces latency and cost?

Accepted Answer

Use BatchGetItem to retrieve multiple items in one request. BatchGetItem allows you to retrieve up to 100 items or 16 MB of data from multiple tables in a single API call, reducing the number of network round trips compared to individual GetItem calls. This directly addresses the high latency caused by multiple sequential requests and also reduces cost because you pay for read capacity units (RCUs) based on the total item size, not per request overhead.

Answer

Enable DynamoDB Accelerator (DAX)

Answer

Redesign to a single-table design

Answer

Create a global secondary index on player_id

Question 6

A company is running a MySQL database on Amazon RDS for a web application. The application experiences read-heavy traffic, and the company wants to improve read performance without changing the application code. Which design should the database specialist recommend?

Accepted Answer

Create one or more read replicas of the RDS DB instance.. Option B is correct because Amazon RDS read replicas allow you to offload read traffic from the primary DB instance without any application code changes. The application simply connects to the read replica endpoint(s) for SELECT queries, while writes continue to the primary instance. This directly addresses the read-heavy workload by distributing read requests across multiple copies of the database.

Answer

Implement an Amazon ElastiCache Redis cluster in front of the database.

Answer

Increase the instance size of the RDS DB instance.

Answer

Enable DynamoDB Accelerator (DAX) for the RDS instance.

Question 7

A company is migrating a legacy Oracle data warehouse to Amazon Redshift. The source uses complex stored procedures with cursors, temporary tables, and PL/SQL. Which THREE design considerations should the company evaluate?

Accepted Answer

Redshift does not support procedural languages like PL/SQL; stored procedures must be rewritten in SQL or Python.. Amazon Redshift does not support Oracle's PL/SQL procedural language. Stored procedures must be rewritten using Redshift's SQL-based procedural language (PL/pgSQL) or Python (via UDFs). This is a critical migration consideration because complex logic involving loops, conditionals, and error handling must be translated to supported constructs.

Answer

Redshift Concurrency Scaling can handle thousands of concurrent queries.

Answer

Redshift automatically scales compute capacity based on workload.

Question 8

An IoT company ingests sensor data into Amazon DynamoDB. The data has a partition key of device_id and sort key of timestamp. Queries often filter by device_id and a date range. Which design pattern improves query performance and reduces cost?

Accepted Answer

Create a local secondary index on device_id and timestamp. Option B is correct because a local secondary index (LSI) on device_id and timestamp allows efficient querying by device_id and a date range without scanning the entire table. Since the base table already uses device_id as the partition key, the LSI shares the same partition key but provides an alternate sort key (timestamp), enabling range queries on timestamp for each device_id. This reduces read capacity consumption and improves latency by avoiding full table scans or expensive filter expressions.

Answer

Use DynamoDB Time to Live (TTL) to expire old data

Answer

Enable DynamoDB Accelerator (DAX)

Answer

Create a global secondary index on timestamp

Question 9

A financial services company runs a critical application on Amazon RDS for MySQL. The database stores transaction data that must be retained for 7 years for regulatory compliance. The current retention policy stores all data in the same table, causing performance degradation on the main transactional table. The company needs to archive data older than 1 year while keeping it queryable. Which design should they implement?

Accepted Answer

Use Amazon RDS for MySQL with a read replica for reporting, and set up a Lambda function to export partitions older than 1 year to Amazon S3 in Parquet format, queryable via Amazon Athena.. Option C is correct because it uses RDS for MySQL read replicas to offload reporting traffic, while a Lambda function archives partitions older than 1 year to Amazon S3 in Parquet format. This keeps the main transactional table lean, improves performance, and retains data for 7 years in a cost-effective, queryable format via Amazon Athena, meeting both compliance and queryability requirements.

Answer

Migrate to Amazon Redshift and use workload management to prioritize transactions.

Answer

Use Amazon ElastiCache for Redis to cache recent data and move old data to S3.

Answer

Migrate to Amazon DynamoDB with TTL to automatically expire old data.

Question 10

A company is designing a global e-commerce platform using Amazon DynamoDB. The platform must support strong consistency for inventory updates and eventual consistency for product catalog reads. Which TWO design patterns should the company implement to meet these consistency requirements?

Accepted Answer

Configure DynamoDB Accelerator (DAX) for product catalog queries with eventual consistency.. Option A is correct because DynamoDB Accelerator (DAX) is an in-memory cache that can be configured to return eventually consistent results for read-heavy workloads like product catalog queries, reducing read latency and cost while meeting the eventual consistency requirement. Option E is correct because DynamoDB global tables replicate data across regions, and using strongly consistent reads for inventory queries ensures that the most recent write is returned, which is critical for inventory accuracy.

Answer

Use Amazon ElastiCache for Redis to cache inventory data with strong consistency.

Answer

Use DynamoDB transactions for all inventory operations.

Answer

Use DynamoDB Streams to replicate inventory changes to a separate table for reads.

Question 11

A company uses Amazon RDS for PostgreSQL for its CRM application. The application experiences intermittent spikes in read traffic. Which TWO actions can the company take to improve read scalability with minimal application changes?

Accepted Answer

Create one or more read replicas in the same region.. Option C is correct because creating read replicas in Amazon RDS for PostgreSQL offloads read traffic from the primary DB instance, directly addressing intermittent read spikes with minimal application changes. Read replicas are asynchronous replicas that can serve read queries, and the application only needs to update its connection string to point to the replica endpoint for read operations.

Answer

Enable Multi-AZ deployment for automatic failover.

Answer

Migrate to Amazon Aurora and enable Auto Scaling.

Answer

Upgrade to a larger DB instance class.

Question 12

A company is running a MongoDB-compatible database on Amazon DocumentDB. The application performs frequent updates to a small subset of fields in documents. The company notices that write latency is high. What should the database specialist recommend to reduce write latency?

Accepted Answer

Review and remove unnecessary indexes on the collection.. Option A is correct because unnecessary indexes impose a write penalty on every insert, update, and delete operation. In Amazon DocumentDB, each write must update all indexes on the collection, so removing unused indexes reduces the per-document write overhead and directly lowers write latency for frequent updates to a small subset of fields.

Answer

Increase the instance size of the DocumentDB cluster.

Answer

Enable multi-master writes to distribute write load.

Answer

Change the storage type from standard to provisioned IOPS.

Question 13

A startup is building a social media analytics application that ingests high-velocity streaming data from multiple sources. The data consists of JSON objects with varying schemas. The application needs to store this data for real-time querying and later batch processing. Which AWS database solution is most cost-effective and scalable for this workload?

Accepted Answer

Amazon DynamoDB with on-demand capacity.. Amazon DynamoDB with on-demand capacity is the most cost-effective and scalable solution for this workload because it is a fully managed NoSQL database that can handle high-velocity streaming data with varying JSON schemas without requiring schema definition or provisioning. Its on-demand capacity mode automatically scales to accommodate unpredictable traffic spikes, making it ideal for real-time querying and batch processing via features like DynamoDB Streams and integration with AWS Glue or EMR.

Answer

Amazon ElastiCache for Redis with persistence enabled.

Answer

Amazon RDS for MySQL with multiple read replicas.

Answer

Amazon Redshift with auto-ingest from Kinesis.

Question 14

A company uses Amazon DynamoDB for a gaming leaderboard. The application updates scores frequently. Reads must be strongly consistent, and writes must be optimized for cost. Which table design minimizes cost while meeting consistency requirements?

Accepted Answer

Use Amazon DynamoDB Accelerator (DAX) for caching.. Amazon DynamoDB Accelerator (DAX) provides an in-memory cache that supports strongly consistent reads, which meets the application's requirement for strongly consistent reads. By caching frequently accessed leaderboard data, DAX reduces the number of read capacity units consumed from the DynamoDB table, thereby lowering read costs. Writes are still performed directly on the DynamoDB table, and DAX does not affect write costs, so the design optimizes overall cost while maintaining consistency.

Answer

Use eventually consistent reads with a conditional write.

Answer

Store scores in Amazon S3 and use S3 Select for reads.

Answer

Use DynamoDB Streams to replicate reads to a separate table.

Question 15

A company is designing a multi-tenant SaaS application using Amazon Aurora PostgreSQL. Each tenant's data must be isolated for security and compliance. The application has a few large tenants and many small tenants. Queries must be able to access data across tenants for reporting, but with strict access controls. Which design best meets these requirements?

Accepted Answer

Use a single Aurora cluster with a separate schema per tenant and implement row-level security policies.. Option A is correct because it uses a single Aurora cluster with a separate schema per tenant and row-level security (RLS) policies. This design provides strong tenant isolation at the schema level while allowing cross-tenant reporting queries with strict access controls enforced by RLS policies, which automatically filter rows based on the current session's tenant context. It balances security, compliance, and operational efficiency for a mix of large and small tenants.

Answer

Create a separate database per tenant in the same Aurora cluster.

Answer

Create a separate Aurora cluster per tenant.

Answer

Use a single Aurora cluster with a single schema but add a tenant_id column to every table.

Question 16

A company is using Amazon RDS for Oracle with a very large database (10 TB). They need to migrate to Amazon Aurora PostgreSQL with minimal downtime. The source database is heavily used with constant writes. Which migration strategy is most appropriate?

Accepted Answer

Use AWS Database Migration Service (DMS) with ongoing replication to migrate from Oracle to Aurora PostgreSQL.. AWS DMS with ongoing replication (change data capture) is the most appropriate strategy for migrating a heavily written 10 TB Oracle database to Aurora PostgreSQL with minimal downtime. DMS can perform a full load of the existing data and then continuously replicate changes from Oracle's redo logs to Aurora PostgreSQL, allowing the source to remain fully operational until a brief cutover window. This approach minimizes downtime compared to offline export/import methods and is natively supported by AWS.

Answer

Export the Oracle database using expdp and import into Aurora PostgreSQL using pg_restore.

Answer

Create a read replica of the RDS Oracle instance and promote it to an Aurora PostgreSQL instance.

Answer

Use Oracle GoldenGate to replicate data to an Aurora PostgreSQL instance.

Question 17

A company is migrating a self-managed MongoDB database to Amazon DocumentDB. The database stores user profiles and activity logs. The activity logs are write-heavy and accessed infrequently. The company wants to optimize cost and performance for the workload. Which THREE design decisions should the company make?

Accepted Answer

Enable encryption at rest for both databases.. Option A is correct because enabling encryption at rest for Amazon DocumentDB is a security best practice that ensures data is encrypted using AWS Key Management Service (KMS) keys. This is a mandatory design consideration for compliance and data protection, and it does not significantly impact cost or performance for the workload described.

Answer

Use Amazon S3 for storing activity logs instead of DocumentDB.

Answer

Use a single DocumentDB cluster for both workloads with appropriate read replicas.

Question 18

A company is designing a database for an IoT application that ingests millions of small sensor readings per second. The data is time-series and queries are mostly range scans over time. The company needs a cost-effective solution with high write throughput. Which AWS service should the database specialist recommend?

Accepted Answer

Amazon Timestream. Amazon Timestream is a purpose-built time-series database designed for IoT and operational applications that ingest millions of data points per second. It automatically manages storage tiers (in-memory and magnetic) to optimize cost, and its query engine is optimized for range scans over time, making it the most cost-effective and high-throughput choice for this workload.

Answer

Amazon Redshift with auto-ingest

Answer

Amazon RDS for PostgreSQL with pg_partman extension

Answer

Amazon DynamoDB with time-series data modeling

Question 19

A company runs a critical e-commerce platform on Amazon Aurora MySQL. The database is 2 TB and experiences a sudden spike in write latency during flash sales. The application uses auto-generated UUIDs as primary keys. The CPU utilization on the writer instance is 80%, and the read replicas show low utilization. Write latency has increased from 5 ms to 200 ms. The company needs to reduce write latency with minimal application changes. Which course of action is MOST effective?

Accepted Answer

Change the primary key to an auto-increment BIGINT and recreate indexes.. B is correct because UUID primary keys cause random writes that fragment the B-tree index, leading to frequent page splits and high write latency. Changing to an auto-increment BIGINT allows sequential writes, which fill index pages contiguously and reduce the write amplification that drives latency from 5 ms to 200 ms. This requires no application logic changes beyond the schema migration, making it the most effective minimal-change solution.

Answer

Implement sharding across multiple Aurora clusters.

Answer

Add more read replicas and redirect write-heavy queries to replicas.

Answer

Upgrade the writer instance to a larger instance type with more IOPS.

Question 20

A company is designing a document management system using Amazon S3 and needs to store metadata such as document ID, owner, creation date, and tags. The metadata must be searchable with low latency, supporting queries like 'Find all documents owned by user X with tag Y created after date Z'. Which AWS database service is most suitable for storing and querying this metadata?

Accepted Answer

Amazon DynamoDB with a GSI on (owner, creation_date) and a filter on tags.. Amazon DynamoDB is the most suitable choice because it provides single-digit millisecond latency for queries at any scale, which meets the low-latency search requirement. By creating a Global Secondary Index (GSI) on (owner, creation_date), you can efficiently query documents by owner and date range, and then apply a filter expression on tags to narrow results. This schema avoids the overhead of joins and normalization, making it ideal for high-throughput metadata lookups.

Answer

Amazon Redshift Spectrum querying metadata stored in S3 as CSV.

Answer

Amazon RDS for PostgreSQL with a normalized schema.

Answer

Amazon ElastiCache for Redis with sorted sets for tags.