DBS-C01 Practice Test 18 — 15 Questions

Question 1

A company wants to migrate an on-premises Oracle database to Amazon Aurora PostgreSQL. They need to convert the schema and code. Which AWS service should they use?

Accepted Answer

AWS Schema Conversion Tool (SCT). AWS Schema Conversion Tool (SCT) is the correct choice because it is specifically designed to convert database schemas and code objects (such as stored procedures, functions, and triggers) from one database engine to another. For an Oracle-to-Aurora PostgreSQL migration, SCT analyzes the source schema, identifies incompatible data types and syntax, and generates equivalent PostgreSQL DDL and PL/pgSQL code, handling complex conversions like Oracle sequences to PostgreSQL sequences and Oracle packages to PostgreSQL schemas.

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AWS Lambda

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Amazon S3

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AWS Database Migration Service (DMS)

Question 2

A company is using Amazon RDS for MySQL with Multi-AZ deployment. The database experiences a failover event, and after failover, the application reports increased latency. Which action should the database specialist take to diagnose the issue?

Accepted Answer

Check Amazon CloudWatch metrics for Database Connections and Latency, and verify DNS resolution.. After a Multi-AZ failover, the DNS record for the RDS endpoint is updated to point to the new primary in a different Availability Zone. If the application's DNS cache still resolves to the old IP (which is now unreachable or in a different AZ), connections may time out or route suboptimally, increasing latency. Checking CloudWatch metrics for Database Connections and Latency helps isolate whether the issue is due to connection churn, resource contention, or DNS caching, while verifying DNS resolution confirms the application is connecting to the correct endpoint.

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Promote the secondary instance to primary manually.

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Create a new read replica and redirect traffic.

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Force a reboot of the DB instance to reset connections.

Question 3

A database administrator needs to automate the backup process for an Amazon RDS for MySQL DB instance. The backups must be retained for 30 days and should be taken daily. Which AWS service should be used to implement this automation?

Accepted Answer

Amazon RDS automated backups. Amazon RDS automated backups are the native, built-in mechanism for backing up RDS DB instances. They automatically take daily snapshots during the backup window and retain transaction logs for point-in-time recovery. By default, the retention period can be set from 1 to 35 days, so configuring it to 30 days meets the requirement without any additional services or custom scripting.

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Amazon S3 lifecycle policies

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AWS Backup

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AWS Lambda with custom scripts

Question 4

Refer to the exhibit. A database administrator runs the AWS CLI command to describe events for an RDS instance. Which conclusion is most likely correct based on the output?

Accepted Answer

The DB instance experienced a Multi-AZ failover and subsequently restarted.. The output shows a failover event followed by a restart, which is typical after a failover. Option A is incorrect because the events show a failover, not a manual restart. Option B is incorrect because the failover was completed, not failed. Option D is incorrect because there is no indication of a snapshot restore.

Answer

The Multi-AZ failover failed and the instance restarted.

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The DB instance was manually restarted by an administrator.

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The DB instance was restored from a snapshot and then restarted.

Question 5

A company needs to store and query a graph of relationships between users for a recommendation engine. The queries involve traversing multiple edges. Which AWS database service is most suitable?

Accepted Answer

Amazon Neptune. Amazon Neptune is a fully managed graph database service purpose-built for storing and traversing highly connected data. It supports both property graph (Gremlin, openCypher) and RDF (SPARQL) models, making it ideal for recommendation engines that require multi-edge traversals across user relationships.

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Amazon DynamoDB with adjacency list design

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Amazon DocumentDB (with MongoDB compatibility)

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Amazon RDS for PostgreSQL with recursive CTEs

Question 6

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. The query uses SELECT *, which forces the database engine to retrieve all columns from the table. Even though the index on creation_date is used for sorting and filtering, the query optimizer may choose a full index scan because it still needs to access the table rows for the non-indexed columns. This is often more efficient than random lookups for a large range, but it still results in scanning many index entries and performing table lookups, causing the observed slowness.

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

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The table is not partitioned by creation_date

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The index on creation_date is not being used

Question 7

A company is designing a relational database for an e-commerce application that requires high availability and automated failover across AWS Regions. Which AWS database service should they use?

Accepted Answer

Amazon Aurora Global Database. Amazon Aurora Global Database is the correct choice because it is designed for cross-Region disaster recovery and high availability, replicating data with a typical latency of under one second across multiple AWS Regions. It supports automated failover from the primary Region to one of the secondary Regions, meeting the requirement for automated cross-Region failover without manual intervention.

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Amazon DynamoDB Global Tables

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Amazon RDS with Multi-AZ deployment

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Amazon Redshift with cross-Region snapshot copy

Question 8

A company needs to migrate a 100 GB MongoDB database to Amazon DocumentDB (with MongoDB compatibility). The migration must have minimal impact on the source database performance. Which approach should the company take?

Accepted Answer

Use AWS Database Migration Service (AWS DMS) with ongoing replication from the MongoDB source.. AWS DMS with ongoing replication is the correct approach because it supports continuous change data capture (CDC) from MongoDB, enabling a live migration with minimal performance impact on the source. DMS reads the MongoDB oplog to capture changes without locking the database, which is critical for a 100 GB production database. This allows the target DocumentDB to stay synchronized until cutover, reducing downtime and avoiding the need for a full export/import that would strain the source.

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Use AWS DataSync to transfer the MongoDB data files.

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Set up a MongoDB replica set on Amazon EC2 and promote it to primary, then migrate to DocumentDB.

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Use mongodump to export the data and mongorestore to import into DocumentDB.

Question 9

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. Amazon Timestream is purpose-built for time-series data, offering automatic tiered storage (in-memory for recent data and magnetic for historical data) and optimized query performance for time-range aggregations. Its serverless architecture eliminates provisioning overhead, making it the most cost-effective and performant choice for append-only IoT sensor data with infrequent updates.

Answer

Amazon DynamoDB with time-series design pattern

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Amazon Redshift

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Amazon RDS for MySQL with partitioning by date

Question 10

A company is migrating a large Oracle data warehouse to Amazon Redshift. Which THREE design considerations are important for performance optimization?

Accepted Answer

Choose appropriate distribution styles (KEY, ALL, EVEN).. Distribution styles (KEY, ALL, EVEN) control how data is distributed across compute nodes in Amazon Redshift. Choosing the right distribution style minimizes data movement during joins and aggregations, which is critical for performance in a large data warehouse migration from Oracle. For example, using KEY distribution on a frequently joined column keeps related rows on the same node, reducing network traffic.

Answer

Enable cross-Region replication for data locality.

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Use row-based storage for fact tables.

Question 11

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. Amazon DynamoDB with conversation_id as partition key and timestamp as sort key is the most appropriate design because it directly supports high write throughput and low-latency reads for a real-time chat application. The partition key enables even distribution of writes across partitions, while the sort key allows efficient range queries for messages within a conversation ordered by time, matching the access pattern perfectly.

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

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Amazon Timestream to store messages as time-series data

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Amazon Redshift with columnar storage and compression

Question 12

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. Amazon Timestream is a purpose-built time-series database that efficiently handles high-frequency writes and recent-time queries (e.g., last hour) with automatic data tiering (in-memory store for recent data, magnetic store for historical). Its serverless architecture and built-in time-series functions (e.g., interpolation, smoothing) make it ideal for social media analytics workloads requiring low-latency queries on recent data.

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Amazon RDS with MySQL

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Amazon Neptune

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Amazon DynamoDB

Question 13

A company is using Amazon RDS for Oracle and needs to comply with regulatory requirements that mandate encryption of all data at rest and in transit. Which TWO actions should be taken to meet these requirements?

Accepted Answer

Enable encryption at rest by specifying a KMS key when creating the DB instance.. Options A and E are correct. Option A enables encryption at rest by specifying a KMS key when creating the DB instance. Option E enables encryption in transit by setting the rds.force_ssl parameter and using the RDS SSL certificate. Option B is incorrect because Oracle TDE is not required when RDS encryption at rest is used; RDS native encryption with KMS is sufficient. Option C is incorrect because CloudWatch Logs encryption does not encrypt the database data. Option D is incorrect because Oracle Native Network Encryption is less secure than SSL/TLS and is not the recommended method for encryption in transit.

Answer

Use Oracle Transparent Data Encryption (TDE) to encrypt the data at rest.

Answer

Enable encryption for CloudWatch Logs.

Answer

Configure Oracle Native Network Encryption in the sqlnet.ora file.

Question 14

A startup is building a real-time analytics dashboard on AWS. The data arrives as time-series events from IoT devices at a rate of 10,000 writes per second. Each event is approximately 1 KB. The dashboard requires sub-second query latency for the last hour of data and must support ad-hoc analytical queries on historical data spanning months. The team needs to design a cost-effective database solution. Which combination of AWS services should be used?

Accepted Answer

Amazon DynamoDB with DynamoDB Accelerator (DAX) for real-time queries, and Amazon S3 with Amazon Athena for historical analytics.. DynamoDB with DAX provides microsecond to sub-millisecond latency for real-time queries on the last hour of data, while S3 with Athena offers a cost-effective serverless solution for ad-hoc analytical queries on historical data spanning months. DynamoDB's time-to-live (TTL) feature can automatically expire data older than one hour, keeping the hot dataset small and performant, and Athena's pay-per-query pricing avoids the cost of maintaining a separate analytics cluster.

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Amazon ElastiCache for Redis for real-time queries, and Amazon OpenSearch Service for historical analytics.

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Amazon Redshift for both real-time and historical queries, using auto-scaling and materialized views.

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Amazon RDS for PostgreSQL with read replicas for real-time queries, and Amazon Redshift for historical analytics.

Question 15

A company is migrating a PostgreSQL database to Amazon Aurora PostgreSQL. To minimize downtime, they plan to use the AWS DMS with change data capture (CDC). During the full load phase, DMS reports an error: 'Failed to add foreign key constraint'. What is the most likely cause?

Accepted Answer

Referential integrity violations in the source data. The error 'Failed to add foreign key constraint' during the full load phase of AWS DMS indicates that the source data contains referential integrity violations. DMS attempts to apply foreign key constraints on the target Aurora PostgreSQL after loading data, but if parent-child relationships are broken (e.g., orphan rows), the constraint creation fails. This is a common issue when migrating databases with existing data integrity problems, as DMS does not automatically validate or fix source data.

Answer

CDC is incompatible with foreign key constraints

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The target table lacks a primary key

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Data type mismatch between source and target