DBS-C01 Practice Test 8 — 10 Questions

Question 1

A company is using Amazon DynamoDB to store session data for a web application. The session data expires after 24 hours. Which DynamoDB feature should the company use to automatically delete expired items?

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

Accepted Answer

DynamoDB Time to Live (TTL). DynamoDB Time to Live (TTL) is the correct choice because it provides a cost-effective, fully managed mechanism to automatically delete expired items based on a timestamp attribute in the table. TTL works by comparing the current time to the epoch time value stored in the designated TTL attribute; when the value is in the past, DynamoDB marks the item for deletion, typically within 48 hours. This eliminates the need for custom code or additional AWS services, directly addressing the requirement to remove session data after 24 hours.

Answer

A retention policy on the DynamoDB table

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DynamoDB Streams

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A scheduled AWS Lambda function that scans and deletes expired items

Question 2

Which TWO techniques can reduce read latency for frequently accessed data in Amazon DynamoDB? (Choose 2.)

Accepted Answer

Add Global Secondary Indexes (GSI) for common query patterns. Adding Global Secondary Indexes (GSI) allows you to pre-materialize alternative query patterns, enabling efficient lookups on non-key attributes without scanning the entire table. This reduces read latency for frequently accessed data by providing a pre-sorted and partitioned index that DynamoDB can query directly, avoiding expensive full-table scans.

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Use strongly consistent reads

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Increase write capacity units

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Decrease read capacity units

Question 3

A company runs a critical Amazon DynamoDB table with on-demand capacity. The table experiences throttling errors (ProvisionedThroughputExceededException) during peak hours. The table has a global secondary index (GSI) with a different partition key. What is the MOST likely cause of the throttling?

Accepted Answer

The global secondary index is throttling due to uneven access patterns.. On-demand DynamoDB tables automatically scale capacity for the base table, but a GSI can throttle writes if its partition key creates hot partitions. The throttling on the base table occurs because the GSI write capacity is exceeded due to uneven access patterns. Options A, C, and D are incorrect because on-demand tables handle base table capacity scaling, hot partitions on the base table would cause throttling on the base table but the issue is with the GSI, and read capacity is not relevant given the write errors.

Answer

The base table's write capacity is insufficient for the workload.

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The partition key design causes hot partitions in the base table.

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The table's read capacity is too low for the read-heavy workload.

Question 4

A startup is building a mobile application that requires a database to store user preferences and session data. The data is accessed by user ID and requires single-digit millisecond latency. The workload is read-heavy with occasional writes. Which database service is MOST cost-effective?

Accepted Answer

Amazon DynamoDB with on-demand capacity. Amazon DynamoDB with on-demand capacity is the most cost-effective choice because it provides single-digit millisecond latency for key-value lookups by user ID, scales automatically to handle read-heavy workloads with occasional writes, and charges only for the reads and writes consumed, avoiding the cost of provisioning for peak capacity. The on-demand mode eliminates the need for capacity planning, making it ideal for unpredictable or variable traffic patterns typical of a startup's mobile application.

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Amazon Aurora Serverless

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Amazon ElastiCache for Memcached

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Amazon RDS for MySQL with provisioned IOPS

Question 5

A company uses Amazon DynamoDB to store order data. The table has a primary key (OrderID) and a Global Secondary Index (GSI) on CustomerID. The application often queries for all orders of a customer sorted by order date. The GSI projects only the keys. The queries are slow. What should the team do to improve query performance? (Choose two.)

Accepted Answer

Modify the GSI to include OrderDate as a sort key. The queries are slow because the GSI projects only keys. When querying the GSI, DynamoDB must fetch the full items from the base table (a 'fetch' operation) for each key, which is inefficient. Option C (modifying the GSI to include OrderDate as a sort key) allows the GSI to sort results by order date natively, improving query performance. Option E (changing the GSI projection to include all attributes) avoids the extra fetch by storing all attributes in the GSI, eliminating the need to access the base table. Option A (DAX) caches results but doesn't solve the sorting or projection issue. Option B (increasing read capacity) doesn't address the inefficiency of key-only projection. Option D (using an LSI) is not possible because LSIs require the same partition key as the base table; CustomerID is different from OrderID, so an LSI cannot be used.

Answer

Enable DynamoDB Accelerator (DAX) for the table

Answer

Increase the read capacity of the GSI

Answer

Use a Local Secondary Index (LSI) instead of a GSI

Question 6

A company is migrating an on-premises Oracle database to Amazon Aurora PostgreSQL. The database contains a large table that is partitioned by date. The company wants to minimize downtime during migration and ensure compatibility. Which TWO actions should the company take?

Accepted Answer

Use AWS Database Migration Service (DMS) with ongoing replication to migrate the data.. AWS DMS with ongoing replication (change data capture) allows continuous synchronization from the source Oracle database to the target Aurora PostgreSQL, minimizing downtime by keeping the target nearly up-to-date until the final cutover. This approach supports large partitioned tables and reduces the migration window compared to a full load only.

Answer

Migrate the database using a full load only and accept downtime.

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Use AWS Schema Conversion Tool (SCT) to convert the schema automatically without any manual adjustments.

Answer

Replace the Oracle database with Amazon RDS for MySQL.

Question 7

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.

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Amazon Aurora MySQL with Aurora Global Database.

Answer

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

Question 8

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 9

A financial services company needs to enforce row-level security on a MySQL database hosted on Amazon RDS. They want to restrict access so that each application user can only see their own data. Which approach should they take?

Accepted Answer

Use a MySQL proxy that injects session context variables and enable row-level security in the application queries. MySQL does not natively support row-level security (RLS) like PostgreSQL or SQL Server. Instead, you can implement RLS by using a MySQL proxy (e.g., ProxySQL, Amazon RDS Proxy) that injects session context variables (e.g., user_id) at connection time. Application queries then reference these variables in WHERE clauses (e.g., WHERE user_id = @@session.user_id), effectively restricting each user to their own data without modifying the underlying schema.

Answer

Place each user's data in a separate database and use VPC endpoints to isolate access

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Create separate database views for each user

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Use IAM database authentication and define fine-grained access policies

Question 10

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.

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

Answer

Use Amazon ElastiCache for Redis with persistence enabled.