AWS MODEL APIS / ACCESS CONTROL / KNOWLEDGE APPLICATIONS
Amazon Bedrock Course
Build a model-backed application on AWS with deliberate access, cost and data-handling choices. This Amazon Bedrock Course covers supported model APIs, permissions, knowledge applications, guardrails and evaluation through controlled cloud exercises.
★ 4+ Google rating from 100+ reviews · 9,000+ students trained · 523+ batches completed · Teaching since 2021
Next batch: Monday, 2 November 2026
Course outline8 modules
Practice3 project scenarios
Duration2 months
Course feeOnline ₹20,000
Classroom ₹25,000
About the Amazon Bedrock Course
Amazon Bedrock provides managed access to foundation models and related application capabilities. The course concentrates on cloud implementation: selecting an available model, granting the required permissions, calling the service and measuring the application. Availability, pricing and feature support vary by model and Region, so checking current AWS documentation is part of the workflow.
This is an AWS implementation specialisation, not a replacement for the broad Generative AI course or a complete AWS administrator program. RAG and agents appear in a Bedrock context. The dedicated RAG and agent courses examine those architectures more broadly across tools.
For product-specific terminology and behaviour, use Amazon Bedrock overview. Match the documentation to the environment used in your exercises.
Benefits of Learning Amazon Bedrock
Turn the syllabus into practical advantages you can demonstrate in your own work. These outcomes come from building, checking and explaining the exercises.
Understand managed model accessConnect application needs to the models and features available in the selected environment.
Apply least-privilege accessKeep application permissions explicit instead of relying on broad administrator access.
Compare quality and costEvaluate model choices on the same task while tracking usage and response time.
Build source-aware applicationsConnect permitted knowledge sources and test what happens when evidence is missing.
Treat guardrails as one controlTest their limitations alongside application checks and human review.
Prepare an operational handoverDocument permissions, configuration, monitoring and cleanup so a lab can be reviewed and maintained.
Who Should Join?
Basic Python, API knowledge and introductory AWS concepts are expected. You should understand accounts, Regions and IAM at a basic level. Use an authorised training account, never shared root credentials, and confirm the budget and cleanup responsibilities before starting labs.
Cloud developersAdd model-backed features to AWS applications.
AWS practitionersUnderstand model access, permissions and operating constraints.
AI application engineersDeploy a controlled cloud implementation of a knowledge or tool workflow.
For complementary learning, explore Generative AI Training. Choose the broader program if you need an introduction to AI models, prompting and the wider application landscape before specialising.
Amazon Bedrock Prerequisites and Readiness Checklist
Use these three checks to decide whether you are ready to start or need a short foundation step first. Bring any gaps to your demo discussion.
AWS foundationsUnderstand regions, IAM permissions and the difference between an identity and the resources it may access.
API and JSON basicsRead request and response records and handle errors without exposing credentials.
A controlled AWS labUse an approved practice account, supported region and spending alerts. Confirm model availability and usage charges before starting.
Amazon Bedrock Course Syllabus: 8 Practical Modules
Each module connects a concept to an exercise and an output you can explain. Work through the foundations before attempting the integrated project.
1. Service scope and account preparation
Identify the model and features available in the chosen Region. Set up an authorised lab account, role and budget alerts. Record the resource owner and cleanup plan before making paid service calls.
2. Model APIs and inference requests
Call a supported inference API with a small synthetic prompt. Inspect request parameters, output and usage information. Compare compatible configurations and handle unsupported model or Region choices with a clear error path.
3. IAM and data boundaries
Apply least-privilege access to the lab resources. Separate application roles from administrative access. Discuss where prompts, logs and source documents are stored, and test that an unapproved role cannot access the protected resource.
4. Knowledge applications
Connect an approved document collection using a supported knowledge-base or retrieval approach. Inspect source ingestion and evidence returned to the application. Test stale files and missing answers rather than assuming cloud-managed retrieval guarantees correctness.
5. Tools and agent workflows
Create a bounded workflow that can use a permitted lookup tool. Use mock actions for consequential operations and keep approval in the application design. Inspect the proposed tool arguments before execution.
6. Guardrails and safety tests
Configure available controls appropriate to the lab use case. Test allowed and disallowed inputs, false positives and sensitive-data handling. Explain that guardrails reduce selected risks but do not replace permissions, application validation or qualified review.
7. Evaluation, latency and usage
Build a representative evaluation set and compare outputs with expected evidence. Measure response time and usage under controlled conditions. Handle quotas and throttling without unbounded retries or an uncontrolled spending loop.
8. Deployment and cleanup capstone
Package a small AWS-backed application with configuration, tests and operational notes. Demonstrate a permission failure and a service-limit case. Remove temporary resources when appropriate and document any resources intentionally retained for review.
Discuss Your Learning Goals
Tell the team about your experience and preferred schedule. Book a free demo to discuss the course, lab access and the practical work expected from you.
Practical Skills You Will Develop
Use the exercises to build an explanation as well as a result. You should be able to show what you did, how you checked it and where the limits are.
Cloud model accessSelect supported APIs and models for an authorised environment.
Least-privilege designGrant only the access required by the application.
Knowledge integrationConnect sources while checking ingestion and evidence.
Safety evaluationTest the limits and false positives of configured controls.
Usage managementObserve latency, quotas and cost drivers.
Operational handoverDocument account ownership, resource cleanup and recovery.
Tools and Lab Requirements
Use a personal practice environment with synthetic or openly licensed data. Keep credentials outside notebooks and source control. Confirm software access, hardware and any paid API usage with the course team before enrolling.
AWS training accountAuthorised account access with a defined Region, role and budget.
AWS SDK and PythonA supported SDK configuration without embedded access keys.
Bedrock lab resourcesSelected models and optional knowledge resources; AWS charges are not assumed to be included.
Consult Amazon Bedrock documentation during practice. Use documentation matching the installed version and check applicable permissions and service costs.
Your Learning Roadmap
Build confidence in stages. Practice time and your starting knowledge matter as much as the number of scheduled sessions.
1. Set up and understandCheck the prerequisites and lab access. Complete the first two modules and explain the basic workflow in your own words.
2. Build and investigateWork through the middle modules. Keep notes of errors, what you tried and the evidence that supported a fix.
3. Test and presentComplete the final modules and an integrated scenario. Present your output, validation and a short handover guide.
Your Amazon Bedrock project milestone: Include an architecture diagram, scoped permission explanation, model comparison, safety tests and a resource-cleanup checklist. Use the existing syllabus checklist to track progress; follow the stages during the 2-month course and confirm assignment dates with your trainer.
3 Practical Project Scenarios
These are teaching scenarios using approved lab systems and synthetic data. They are not claims about live client work or previous student results.
Internal documentation assistantUse a fictional operations guide to build a source-backed AWS application. Include missing-answer and stale-document tests.
Structured request processorExtract agreed fields from synthetic requests with validation. Compare output quality and usage across permitted configurations.
Controlled tool workflowConnect a model-backed assistant to a read-only mock inventory lookup. Enforce permissions and test unavailable-tool behaviour.
How the Skills Work in Practice
A fictional team needs an assistant to answer from an internal maintenance guide. Start by deciding who may access the documents and which Region is permitted. Build retrieval and answer checks before adding an attractive chat interface.
Then test a role without source access and a request that exceeds the configured service limits. The application should return an understandable failure without exposing documents or retrying indefinitely. The final report includes usage assumptions and the resources that must be cleaned up.
Why Learn with Brolly Academy?
Choose a course that connects the subject to work you can actually demonstrate. Use a demo to discuss the learning path and decide whether it fits your starting point.
A connected learning pathThe eight-module outline moves from foundations to an integrated task. Each module identifies an output rather than leaving practice as a vague promise.
Discuss your own learning goalShare your experience and preferred format with the course team. Use a demo to assess whether the technical depth, pace and project expectations fit your needs.
Clear course boundariesThe page explains prerequisites, product scope and the difference between training completion and independent certification. You can compare the offer with your actual requirements.
Compare Brolly Academy with Other Training Institutes
Compare the same evidence from each provider: what you will build, what you will test and which services the fee includes. Ask for specific examples rather than relying on broad claims about advanced AI.
What to discuss with Brolly AcademyReview these eight modules and the three project scenarios.
Ask for the assigned trainer profile.
Check the lab version and access period.
Review the fee, schedule and certificate terms.
Use the demo to assess the teaching approach.
What to check with any instituteRequest a detailed outline, not just product names.
Verify the trainer information supplied.
Separate demonstrations from your own lab work.
Check licences, exam costs and refund conditions.
Compare the total commitment, not only the advertised price.
Classroom, Online and Corporate Learning
Discuss the currently available delivery format with the academy before booking. Batch availability and session timings should be agreed in writing.
Classroom learning enquiryConfirm the venue, next available batch and computer requirements before arranging travel.
Live online enquiryCheck the session time zone, remote lab access and arrangements for asking questions or catching up on a missed session.
Corporate team enquiryShare your team size, current tools and learning objectives. Agree a scoped program without sharing confidential business data.
For a team program, explore Brolly Academy corporate training.
Amazon Bedrock Course Fee and Duration
The Amazon Bedrock Course runs for 2 months. The fee is ₹20,000 for online training or ₹25,000 for classroom (offline) training. Ask for a written quotation confirming taxes, payment and refund terms, lab access and any separate software or API charges.
Classroom training: ₹25,000Learn in the classroom over 2 months. Confirm the venue, batch start date and session timings with the academy before arranging travel.
Online training: ₹20,000Choose online learning over 2 months. Confirm the session time zone, lab access and arrangements for questions or missed sessions before enrolling.
Duration: 2 monthsPlan time for lessons, independent practice and the course project. Ask for the batch calendar, instructor-led hours, assignment deadlines and support period.
Course Completion and Certification Guidance
Brolly Academy course completion recognizes the training and assessment work agreed for your batch. It is separate from a credential awarded by a software vendor or another certification body.
Brolly Academy course completionAsk for the attendance, assignment and assessment requirements, the certificate wording and how completion will be verified.
Independent certificationUse official vendor information to check whether a relevant credential is currently offered and what preparation it requires. No vendor authorization, exam voucher or pass guarantee is implied.
Career Roles and Skill Applications
Match the learning path to a role, then compare its requirements with your existing experience. The course can support skill development; it does not guarantee employment.
AWS AI application developerImplement model-backed cloud application features.
Cloud AI engineerManage access, integration and operational behaviour.
Generative AI solutions practitionerExplain architecture choices and their AWS constraints.
You can also explore MLOps Training. Extend a working prototype into a monitored, repeatable deployment workflow.
Where These Skills Are Used
Managed model access can support enterprise knowledge tools, extraction and controlled workflow assistance. Choosing a cloud service does not remove the need for data governance, access design and evaluation. Costs and capabilities should be checked against the exact workload and Region.
Interview and Portfolio Preparation
Prepare evidence you can discuss clearly. Label practice work as training work and do not present a teaching scenario as paid client experience.
Explain a projectDescribe the requirement, your decisions, the result and one limitation. Be ready to answer what you would change for a real deployment.
Show useful evidenceKeep approved files, test results and a concise README or runbook. Remove secrets and private information before sharing a portfolio.
Discuss support servicesAsk the academy which resume, mock-interview or job-search services are included in your batch. Placement assistance is not a job or salary guarantee.
Placement Preparation and Career Support
Use this checklist during your demo to agree the career-support services included with your batch. The preparation below builds on your course project; recruitment and interview outcomes remain with the employer.
Choose a target roleStart with the responsibilities of a AWS AI application developer. Compare several relevant job descriptions and note the skills you can already prove.
Prepare a project evidence packInclude an architecture diagram, scoped permission explanation, model comparison, safety tests and a resource-cleanup checklist.
Practise a technical discussionHow would you separate an access-denied failure from a model availability or request-format problem? Explain your decision with a test or project example.
Write an honest resume entryState the project goal, your contribution and the checks you ran. Label it as a learning project and report only results you actually measured.
Request focused feedbackAsk whether the batch includes a resume review, portfolio review or mock interview. Agree the format, number of reviews and support period before enrolling.
Plan applications and follow-upKeep a record of role requirements, applications and feedback. Use the next practice session to address a specific gap rather than adding unsupported claims to your profile.
Career Planning and Salary Expectations
Salary depends on the role, prior experience, location and the employer. A tool course alone does not establish a salary band. Compare recent job descriptions, required experience and responsibilities rather than relying on a headline income promise.
Meet Your Trainer: Dinesh
Dinesh is the trainer for the Amazon Bedrock Course. Use a demo to discuss your learning goals, ask about a practical Amazon Bedrock scenario and understand how project feedback is provided.
Relevant experienceAsk for examples of work with Amazon Bedrock that can be discussed without revealing confidential client information.
Practical explanationAsk the trainer to explain a failure scenario and how a learner would investigate it, rather than only showing a finished result.
Feedback and supportDiscuss how assignments are reviewed, how questions are handled and the support period included in the course.
Brolly Academy Learner Feedback
Read available academy feedback and ask whether a review relates to this particular course, trainer and delivery format. General academy reviews should not be mistaken for verified results from this new course.
The excerpts below are published on Brolly Academy's academy reviews page. They describe Data Science and Machine Learning courses, not this new Amazon Bedrock course.
Aditya - Data Science"The instructors provided a clear and practical understanding of data science concepts."
Read the original academy testimonial
Kavita Singh - Data Science"The hands-on projects helped me apply the concepts effectively."
Read the original academy testimonial
Rahul Desai - Machine Learning"The trainers were experienced professionals who explained complex algorithms with ease."
Read the original academy testimonial
Student Community and Continued Learning
Keep learning visible: ask precise questions, share permitted examples and record what changed after feedback. Check with the course team about any batch-specific discussion group or mentor access.
Academy updatesFollow the Brolly Academy WhatsApp channel for public academy updates. A public channel is separate from individual technical support.
Amazon Bedrock peer-practice ideaReview a lab architecture and identify where permissions, spending limits and sensitive-data handling need explicit controls.
Share safely and ask clearlyInclude the tool version, a small permitted example, the expected result and what happened. Remove API keys, employer documents and personal data before asking for help.
For continued technical learning, use Amazon Bedrock overview. Recheck examples against the version you are using.
Download Your Practice Checklists
Use these editable CSV files to organize your learning. They open in common spreadsheet tools and contain real module and project-checklist content.
Download Syllabus (CSV) Project Checklist (CSV)Official Documentation for Further Reading
Use the documentation that matches your product version and environment. These sources support technical learning; linking to them does not imply endorsement of Brolly Academy.
Amazon Bedrock overview
Amazon Bedrock documentation
AWS certification information
Related Courses at Brolly Academy
Choose complementary learning based on your current skill gaps. These are separate courses, not automatically included in this program.
Generative AI TrainingChoose the broader program if you need an introduction to AI models, prompting and the wider application landscape before specialising.
View Generative AI Training
Agentic AI CourseExplore agent architecture and decision-making across a broader range of tools.
View Agentic AI Course
MLOps TrainingExtend a working prototype into a monitored, repeatable deployment workflow.
View MLOps Training
For a deeper understanding of retrieval quality, document grounding and evaluation before choosing a cloud implementation, explore RAG Training.
To practise application-side composition of model calls, tools and retrieval, see the LangChain Course.
For a separate study of application traces, evaluation datasets and regression testing, explore the LangSmith Course.
Talk to the Amazon Bedrock Course Team
Call +91 81868 44555 or send a course enquiry to discuss the syllabus and current batch options. For a classroom visit, confirm the venue and appointment with Brolly Academy before travelling.
Read our contact details for the academy location and enquiry options. Online delivery, session time zones and tool access should be confirmed for your batch.
Call +91 81868 44555Amazon Bedrock Course FAQs
Is Amazon Bedrock an AWS certification?
No. It is an AWS service. This training is not itself an AWS-awarded credential. Any separate certification goal, exam eligibility or exam fee must be checked through the official AWS certification program.
Do I need an AWS account?
Hands-on cloud exercises need authorised access to a suitable account. Confirm whether the academy supplies a lab account or you provide one, and who is responsible for charges and cleanup.
Are all models available in every Region?
No. Model and feature availability vary. The lab configuration should be selected from current AWS documentation and the permissions available in your account.
Does a guardrail guarantee safe output?
No. It applies selected controls with limitations and possible false positives. Permissions, application validation, evaluation and human review still matter.
Are API credits included in the course fee?
No inclusion is assumed here. Ask for a written statement covering model calls, storage, optional services and any resource-retention period.
How is this different from an AWS fundamentals course?
This outline assumes introductory AWS knowledge and focuses on model-backed applications. Networking, infrastructure administration and the wider AWS service catalogue are not taught as a complete foundation program.
How much does the Amazon Bedrock Course cost?
The Amazon Bedrock Course fee is ₹20,000 for online training and ₹25,000 for classroom (offline) training. Confirm taxes, payment and refund terms, lab access and any separate software, API or examination costs in your written quotation.
What is the course duration and batch schedule?
The course duration is 2 months for both online and classroom training. Contact Brolly Academy for the next batch start date, session timings, instructor-led hours and independent practice expectations.
Can I attend a free demo before enrolling?
Use Book Free Demo to open the enquiry form, or contact the team on WhatsApp. Share your experience and learning goal so the counsellor can discuss a suitable session and current availability.
What certificate will I receive?
Discuss the Brolly Academy course-completion requirements for your batch, including attendance, assignments and project assessment. Course completion is separate from any credential issued by a software vendor or independent examination body.
Does the course guarantee a job or salary?
No. The learning path is designed to build demonstrable skills. Ask which portfolio, resume or interview-support services are included. Hiring decisions depend on your wider experience, preparation, location and employer requirements.
How should I compare this course with another institute?
Compare the actual syllabus, your own lab work, trainer experience, project feedback, tool costs and written terms. Ask each provider the same questions and use a demo to assess the teaching style before committing.
Got More Questions About Amazon Bedrock?
Talk to the Brolly Academy team about your starting skills, learning goal and available batch options. Request a free demo before choosing your course.