AWS Database Specialty (DBS-C01) Exam Prep Course

Get an overview of AWS, the DBS-C01 certification, and exam tips, covering AWS services, cloud benefits, and how to set up your account across regions and availability zones.

Discover the AWS DBS-C01 exam overview, including levels, format, and five domain weights, with emphasis on database services and practical study paths.

Learn how to create an AWS account and set up IAM, using the management console, CLI, and SDK, with MFA and best practices for secure access.

Explore the AWS database landscape from relational to NoSQL, review services like RDS, Aurora, DynamoDB, DocumentDB, and Neptune, and learn ACID and BASE properties plus migration and management basics.

Explore Amazon RDS, a managed relational database service, covering design, deployment, migration, security, backups, monitoring, and high availability with read replicas and RDS proxy.

Explore deployment and migration from on-premises to AWS, including MariaDB, MySQL, Oracle, SQL Server via S3, and PostgreSQL migrations, plus RDS to RDS and cross-engine migrations with DMS.

Learn AWS database management with automatic backups, manual snapshots, point-in-time recovery, and cross-region copies. Explore maintenance windows, parameter groups, and option groups for optimal performance.

Learn monitoring and troubleshooting for RDS using CloudWatch metrics and logs, extended monitoring for OS metrics, and performance insights for SQL wait events and database load.

Learn database security with VPC filters and security groups, encryption in transit and at rest using KMS, backups, snapshots, and logs, access control via IAM authentication, per-table or per-instance grants.

Explore how to create and configure an Amazon RDS MySQL database in the AWS management console, including free tier options, VPC, security groups, and endpoint details.

This hands-on session demonstrates creating a read replica across regions, configuring encryption consistency, handling separate replica endpoints, and managing manual snapshots via copy, encryption, and restore operations.

Practice configuring a multi-AZ RDS MySQL database, enabling failover across availability zones, creating a read replica, and promoting it to primary while understanding replication lag and deletion steps.

Explore Amazon Aurora, a cloud-optimized, fully managed database engine compatible with MySQL and PostgreSQL, delivering high performance, scale, serverless options, 99.99% uptime, and multi-region disaster recovery.

Gain practical guidance on deploying and migrating with Aurora, including MySQL and PostgreSQL migrations via S3, snapshots, and read replicas; and cross-engine moves using DMS and SCT.

Develop and maintain backups and recovery for Aurora and RDS with automatic backups, Aurora continuous backups, and point-in-time recovery; use backtrack to rewind up to 72 hours and clone instances.

Explore monitoring and troubleshooting for Aurora and RDS with CloudWatch extended monitoring, test query-level performance, and leverage database activity streams with Kinesis Firehose and CloudWatch events for DML and DDL.

Explore database security concepts including VPC, firewalls, encryption in transit and at rest, IAM authentication, Aurora, and advanced auditing with SQL-level history and filters by user, IP, database, and table.

Create an aurora database from the RDS console, selecting aurora MySQL or PostgreSQL and production template, then configure version, storage, and writer and reader endpoints for monitoring.

Perform hands-on Aurora restoration, backup, and failover by restoring to a point in time from automated backups or snapshots, including backtrack and fast leader-to-primary failover for high availability.

Explore aurora failover with multiple read replicas: the largest replica becomes writer when priorities are equal, typically within a minute, with AWS recommending same-size replicas for stable performance.

Explore Amazon Redshift, a fully managed data warehouse delivering high-performance analytics with columnar storage and data compression, including distributed architecture, distribution styles, and Redshift Spectrum for querying data in S3.

Learn to load data into Redshift from S3 and DynamoDB with the copy command, designed for parallel high throughput. The session also covers vacuum, analyze, and updates to optimize performance.

Explore Redshift security features, including encryption at rest, network security with VPC and logging, and access control for tables, schemas, functions, and column views; migrate clusters by creating encrypted replicas.

conduct a hands-on session on Amazon Redshift, running queries on CSV files in S3 and using the new editor to fetch external table schemas and query data directly from S3.

Explore DynamoDB, a fully managed serverless NoSQL service, its capacity modes on-demand and provisioned, pricing, auto-scaling, and cost factors like read/write units, backups, and global tables.

Explore keys and secondary indexes in DynamoDB, including partition keys, sort keys, and local/global indexes, and see how distribution, bias, and indexing choices affect performance and cost.

Capture a time-ordered sequence of item-level changes in a DynamoDB table for 24 hours, enabling replication across the AWS region and feeding changes into Redshift or AWS Lambda.

Accelerate DynamoDB read performance with DynamoDB Accelerator (DAX), an in-memory cache delivering microsecond responses and up to tenfold speedups. Write-through to cache and DynamoDB with a five-minute TTL.

Explore DynamoDB global table for multi-master replication across regions, using DynamoDB streams to keep writes in sync. Ensure name matches, capacity, inscription, time to live, and global secondary index settings.

Explore DynamoDB operation and backups, detailing writing and reading with eventual and strong consistency, replication across three AZs, and how latency and throughput are impacted.

Monitor DynamoDB with CloudWatch for table performance and capacity planning; adjust read/write capacity units and monitor latency, throttling, and scans, while CloudTrail logs management and data API calls and backups.

Identify common DynamoDB troubleshooting strategies for high latency and throughput issues; apply throughput tuning, hot-key distribution, batch operations, autoscaling, capacity planning, and DynamoDB streams.

Implement DynamoDB security with IAM access control for tables, indexes, and streams, using policies to govern API access, encryption, and network isolation via a gateway VPC endpoint.

Practice creating and configuring a DynamoDB table with partition key product ID and optional sort key, and explore on-demand vs provisioned capacity and composite keys.

Create a DynamoDB test product three table and load more than ten items with batch write. Preview scan and query operations, using projection and filter expressions and key conditions.

Perform a hands-on DynamoDB setup to create and query global and local secondary indexes, including configuring a GSI with SKU and price keys.

Learn to configure time-to-live in DynamoDB by adding an expiry attribute, setting an epoch timestamp, and enabling TTL to auto-delete expired items for cost and performance benefits.

Learn to back up DynamoDB with on-demand and scheduled backups, configure point-in-time recovery, and restore to a new table within a 35-day window.

Create a DynamoDB accelerator (DAX) cluster, configure node type and subnet group, set an IAM role and encryption, and use the DAX endpoint to reflect changes on the source table.

Learn how AWS ElastiCache and Elasticsearch simplify caching, session storage, and real-time analytics, and compare Redis and Memcached with their scalability and high availability features.

Explore the ElastiCache memcached architecture, including cluster with multiple nodes, automatic node failure detection, per-node endpoints, and autodiscovery to manage connections and scale.

Explore ElastiCache for Redis architecture, including cluster and node endpoints, primary and replica nodes, cluster mode, and horizontal and vertical scaling with cross-region global data store features.

Understand Elastic Cache cost structure, including node cost per hour, reserved 1- or 3-year terms, outbound data transfer charges, and backup storage pricing with a free snapshot.

Learn management and operation for the DBS-C01 training, covering upgrades with reboots, parameter groups, Elasticsearch backups to S3, automatic and manual backups, and multi-az redundancy.

Use CloudWatch to monitor Elasticsearch and ElastiCache, tracking cache hit rate and misses, then address latency and performance issues across shards by tuning retention.

Secure Elasticsearch deployments with encryption in transit and at rest via KMS and default keys, enable password authentication, role-based access, and VPC clusters with CIDR ranges and network ACLs.

Learn basic ElastiCache configuration for memcached and cluster mode, including subnet groups, VPC settings, availability zones, encryption in transit and at rest, backups, maintenance windows, and cluster management.

Explore Amazon DocumentDB, a managed, MongoDB-compatible document database designed for web, mobile, and IoT workloads; deploy replicas with multi-AZ and migrate MongoDB apps to DocumentDB without modification via MongoDB API.

Explore DocumentDB architecture with 0–16 db instances, a primary read-write node, up to 15 read replicas, multi-az high availability, elastic cluster, and geospatial queries.

Manage DocumentDB with automated maintenance windows, patching, and optional major version upgrades. Rely on automatic backups and manual snapshots up to 100, share across regions, deletions tied to cluster deletion.

Monitor DocumentDB with CloudWatch metrics, tracking cluster and instance metrics like connections, storage, IOPs, and latency, and use performance insights to visualize query latency and troubleshoot common issues in VPC.

Explore how DocumentDB, a fully managed database service, uses VPC network isolation, encryption, and IAM to manage access and permissions.

Create and configure an Amazon DocumentDB cluster, compare instance-based and elastic clusters, set shard or instance details, and establish connectivity via MongoDB API endpoints.

Amazon Neptune is a fully managed graph database service with high performance. Models data as nodes and edges, supports Gremlin and SPARQL, and enables automatic backups, failover, and security.

Explore management and operation for Neptune databases, including maintenance windows, potential downtime from upgrades, automatic and manual backups, and cross-region, cross-account snapshot copies with KMS permissions.

Monitor Amazon Neptune with CloudWatch-like metrics as CPU usage, connections, and memory; use Neptune streams for real-time change data capture and trigger Lambda events; troubleshoot access denied and internal errors.

Enable encryption with AWS KMS and encrypt backup snapshots to secure Neptune instances. Create a new cluster with IAM authentication; password-based access is unsupported; use network ACLs and security groups.

Launch Amazon Neptune using CloudFormation to create a database with provisioned or serverless options. Set engine version, cluster settings, capacity units 1 to 128, and enable SageMaker Jupyter notebook integration.