The Short Answer
AWS offers 10 microcredentials. Each one is a free, timed lab in a live AWS account, with no multiple-choice questions. The hardest, in our view, is AWS Databases for Agentic AI Demonstrated: 140 minutes, seven challenges and no hints, across Aurora PostgreSQL with pgvector, DynamoDB, Bedrock Knowledge Bases and MCP servers. The easiest is AWS Incident Response Demonstrated: 90 minutes on services most AWS engineers already use every day, such as IAM, EC2, S3, CloudTrail and AWS Config.
AWS rates all ten as Intermediate. Our grades compare them with each other, not with the certification exams. A lab rated "easier" here still fails people who have only read about the services.
What an AWS Microcredential Is
A microcredential is a hands-on exam lab on AWS Skill Builder. AWS gives you a working environment with a business problem already built into it, such as a broken streaming pipeline, an agent missing its identity controls, or a compromised account. You fix it in the AWS console before the timer runs out. AWS checks the result, not your answers to questions.
Since April 2026, every microcredential has been free to every builder, with no Skill Builder subscription needed. Pass one and you get a Credly badge, and the result shows in your AWS Certification account under Exam History. AWS suggests more than six months of hands-on experience with the services involved.
How We Ranked Them
AWS publishes no pass rates for microcredentials, so this ranking is our judgment, built from what AWS does publish on each Skill Builder page.
Time limit: AWS sizes each lab's clock to the work in it. The limits run from 90 to 140 minutes.
Time per challenge: seven challenges in 90 minutes leaves about 13 minutes each, while six in 135 minutes leaves over 20. More time per challenge means each challenge is bigger.
Guidance: most labs say they give "necessary guidance but not step-by-step solutions". The two newest, Securing Agent Identities and Databases for Agentic AI, say "procedural guidance but no hints".
How new and how niche the services are: Bedrock AgentCore, pgvector and MCP servers have far less written about them than Lambda, IAM or load balancers.
Your own background matters more than any of these. A data engineer will find the three data labs easier than a security engineer will, and the reverse is true for Incident Response. Like our certification ranking, this one is for a generalist IT professional who has some hands-on AWS experience.
All 10 Microcredentials Ranked, Hardest to Easiest
Lab time is the time you have once the lab starts, as stated on each Skill Builder page. Objectives are the challenges AWS lists for the lab. Pairs with is our suggestion for the certification that covers the same ground; AWS does not map microcredentials to exams. Every name starts with "AWS" and ends with "Demonstrated"; we shortened them to fit.
| Rank | Microcredential | Lab time | Objectives | Pairs with | Our grade |
|---|---|---|---|---|---|
| 1 | Databases for Agentic AI | 140 min | 7 | Generative AI Developer Pro | Hard |
| 2 | Securing Agent Identities | 135 min | 6 | Security Specialty | Hard |
| 3 | MLOps | 140 min | 7 | ML Engineer Associate | Hard |
| 4 | Data Streaming | 140 min | 7 | Data Engineer Associate | Medium-Hard |
| 5 | Agentic AI | 130 min | 6 | Generative AI Developer Pro | Medium-Hard |
| 6 | Serverless | 125 min | 7 | Developer Associate | Medium |
| 7 | Data Lakehouse | 110 min | 7 | Data Engineer Associate | Medium |
| 8 | Data Visualization | 120 min | 7 | Data Engineer Associate | Medium |
| 9 | Application Networking | 90 min | 7 | Solutions Architect Associate | Easier |
| 10 | Incident Response | 90 min | 6 | Security Specialty | Easier |
1. AWS Databases for Agentic AI Demonstrated (Hard)
You get an AI customer-intelligence agent that no longer works, and you fix its data layer. You set up Aurora PostgreSQL and DynamoDB for agent access and memory, configure pgvector and Bedrock Knowledge Bases for document retrieval, set up the Aurora and DynamoDB MCP servers for debugging, and tune the agent's product search queries.
It ranks first because it combines two database engines, a vector search layer and agent tooling in one lab, with the longest clock (140 minutes) and no hints. Launched in September 2026, it is also one of the two newest labs, so little has been written about it yet. AWS suggests preparing with the SimuLearn labs on Aurora vector databases and NoSQL design.
2. AWS Securing Agent Identities Demonstrated (Hard)
A fleet of Amazon Bedrock AgentCore agents has gaps in its identity setup. You provision a workload identity for a self-hosted agent, restrict identity access with tag-based access control, gate inbound access with scoped JWT authorization, set up a SigV4 path for one service, move another agent to JWT-based verification, and detect identity events with CloudTrail and CloudWatch.
The services are familiar (IAM, Cognito, CloudTrail) but the subject is not: identity for AgentCore agents is new, and choosing between SigV4 and JWT for each caller takes a real understanding of both. You get 135 minutes for six challenges and no hints. It suits people who already know IAM well and want to show they can secure AI workloads.
3. AWS MLOps Demonstrated (Hard)
You deploy a SageMaker JumpStart foundation model through the console and SageMaker Studio, troubleshoot a custom inference container, run a batch inference job, set up observability and scaling, connect a generative AI application, run a processing job and a fine-tuning training job, then evaluate the model package.
Seven challenges in 140 minutes sounds generous, but SageMaker endpoints and training jobs take minutes to start and finish, and that waiting comes out of your clock. AWS's own page strongly recommends console familiarity before you begin. If you are preparing for the ML Engineer Associate exam, this lab tests much of the same deployment and monitoring work hands-on.
4. AWS Data Streaming Demonstrated (Medium-Hard)
You diagnose and repair a streaming platform: scale Kinesis Data Streams ingestion for peak load, make a Managed Service for Apache Flink application survive restarts, build a Parquet archive for audits, clear a poison-pill message stalling a notification pipeline, and fix partitioning and delivery problems in Amazon Data Firehose.
Streaming bugs are hard to see, because data keeps flowing while something downstream quietly breaks. The lab mixes Kinesis, MSK, Flink, Firehose and Glue, so a gap in any one of them costs time. It gets 140 minutes for seven challenges.
5. AWS Agentic AI Demonstrated (Medium-Hard)
AWS rebuilt this lab in May 2026 around a migration to Amazon Bedrock AgentCore. You configure OAuth 2.0 authentication, troubleshoot AgentCore Gateway tool routing, deploy a knowledge base agent runtime, add AgentCore Memory, apply Bedrock Guardrails, and secure agent runtimes with VPC networking.
It ranks below the two September labs because AgentCore now has more learning material behind it, and this lab gives "necessary guidance" on each challenge, while the two September labs give procedural guidance but no hints. You get 130 minutes for six challenges. If you passed the earlier version, AWS says that credential remains valid.
6. AWS Serverless Demonstrated (Medium)
Built around a shipment-tracking app, this lab has you configure an API, ingest carrier events with EventBridge, route exceptions through SQS with a dead-letter queue, connect a handler to Step Functions, troubleshoot an Amazon Bedrock call and an IAM permission, and speed up a read path.
Its service list is among the longest of the ten, but these are services serverless developers use every day. You get 125 minutes for seven challenges. If you write Lambda code for a living, this is the microcredential to start with, and it covers much of the Developer Associate exam.
7. AWS Data Lakehouse Demonstrated (Medium)
You fix a broken lakehouse: remove duplicate records from Glue ingestion, resolve a schema mismatch, configure a file-processing pipeline, get Athena queries under a five-second target, and set up Lake Formation tag-based access and data cell filters. One challenge troubleshoots Firehose upserts into Apache Iceberg tables.
Lake Formation permissions are where people tend to lose time, because a grant that looks right can still hide data from the role you are testing. You get 110 minutes for seven challenges.
8. AWS Data Visualization Demonstrated (Medium)
You build the analytics layer for a streaming platform in Amazon Quick Sight: a dashboard over Redshift Serverless, calculated metrics with ML-powered forecasting, row- and column-level security for partner data, reusable Athena views, a fix for a SPICE ingestion that failed after a schema change, and an executive dataset that mixes custom SQL with direct query.
Most of the work happens in one console, which makes it more forgiving than the multi-service labs above. You get 120 minutes for seven challenges. BI analysts will find it the most natural of the three data labs.
9. AWS Application Networking Demonstrated (Easier)
You fix Application Load Balancer health checks and scale an ECS service, tighten security groups between tiers, set up a VPC Lattice service network, route API Gateway traffic through it, prepare a CloudFront distribution for production, add CloudWatch alarms and turn on load balancer access logs.
Seven challenges in 90 minutes is the least time per challenge of the ten, which tells you each one is small. VPC Lattice is the only service many candidates will not have used before. It pairs well with Solutions Architect Associate. If you were weighing Advanced Networking Specialty instead, note that AWS is retiring that exam on December 31, 2026.
10. AWS Incident Response Demonstrated (Easier)
You respond to a multi-stage attack on an AWS account: find and fix compromised resources, remove backdoor access and apply least privilege, block data exfiltration, segment the network, secure an RDS database while preserving forensic evidence, and add audit and compliance controls.
It ranks easiest because the services are the core ones every AWS engineer meets early: IAM, EC2, S3, RDS, CloudTrail and AWS Config. The difficulty is knowing where an attacker hides access, not learning a new service. You get 90 minutes for six challenges, slightly more time per challenge than Application Networking, but here familiarity with the services outweighs the clock. It pairs naturally with Security Specialty.
Microcredential vs Certification
A microcredential does not replace a certification. A certification tests broad knowledge of a role; a microcredential tests one skill area hands-on. They work well together: the certification shows breadth, and a microcredential on the same subject shows you can do the work.
| Microcredential | AWS Certification | |
|---|---|---|
| Format | Timed lab in a live AWS account | Question-based exam, mostly multiple choice |
| Cost | Free | $100 to $300 USD |
| Where | AWS Skill Builder | Pearson VUE test center or online proctored |
| Time | 90 to 140 minutes | 90 to 180 minutes |
| Wait after a fail | 25 days | 14 days |
| Valid for | 12 months | 3 years |
Rules to Know Before You Start
- You cannot pause or restart a lab. If you leave, you start again from the beginning.
- If you fail, you wait 25 days before your next attempt.
- A pass is valid for 12 months. You can renew from 9 months after passing.
- Challenges come in random order and may not follow the order of a real project.
- Read each challenge in full before you touch the console. AWS says most apparent faults come from a missed configuration step rather than a broken lab.
Which One Should You Take First?
Start with the one closest to your day job, not the easiest on this list. A lab you pass in a field you work in says more about you than an easy badge in an unrelated area.
If nothing pulls you one way, Incident Response and Serverless make good first labs: the services are core AWS, and the skills carry over to almost any role. Leave the two September labs, Securing Agent Identities and Databases for Agentic AI, until you have built and secured an agent on Amazon Bedrock.