Module 01: Plan and manage an Azure AI solution
Choose the appropriate Foundry services for generative AI and agents
- Choose an appropriate model for each task, including large language models (LLMs), small language models, multimodal models, and Foundry Tools
- Choose the appropriate Foundry services for generative tasks, grounding, vector search, agent workflows, or multimodal processing
- Choose an appropriate method for retrieval and indexing
- Choose appropriate memory, tool, and knowledge integration services for agent solutions
Set up AI solutions in Foundry
- Design Azure infrastructure for AI apps and agent-based solutions
- Choose appropriate deployment options
- Configure model and agent deployments
- Integrate Foundry projects with continuous integration and continuous deployment (CI/CD) pipelines
Manage, monitor, and secure AI systems
- Manage quotas, scaling, rate limits, and cost footprints for model and agent workloads
- Monitor model performance, drift, safety events, and grounding quality
- Monitor data ingestion quality, search index health, and relevance performance
- Configure security, including managed identity, private networking, keyless credentials, and role policies
Implement responsible AI across generative AI and agentic systems
- Configure safety filters, guardrails, risk detection, and content moderation
- Apply responsible AI instrumentation, including evaluators, safety evaluations, and explanation tooling
- Implement auditing through trace logging, provenance metadata, and approval workflows
- Govern agent behavior with oversight modes, constraints, and tool-access controls
Module 02: Implement generative AI and agentic solutions
Build generative applications by using Foundry
- Deploy and consume LLMs, small models, code models, and multimodal models
- Implement retrieval-augmented generation (RAG) in an application
- Design workflows, tool-augmented flows, and multistep reasoning pipelines
- Evaluate models and apps, including detecting fabrications, relevance, quality, and safety
- Integrate generative workflows into applications by using Foundry SDKs and connectors
- Configure an application to connect to a Foundry project
Build agents by using Foundry
- Define agent roles, goals, conversation-tracking approach, and tool schemas
- Build agents that integrate retrieval, function-calling, and conversation memory
- Integrate agent tools, including APIs, knowledge stores, search, content understanding, and custom functions
- Implement orchestrated multi-agent solutions
- Build autonomous or semiautonomous workflows with safeguards and approval flow controls
- Integrate monitoring into deployed agents, evaluate agent behavior, and perform error analysis
Optimize and operationalize generative AI systems
- Tune generation behavior, such as prompt engineering and adjusting model parameters
- Implement model reflection, chain-of-thought evaluations, and self-critique loops
- Set up observability by implementing tracing, token analytics, safety signals, and latency breakdowns
- Orchestrate multiple models, flows, or hybrid LLM and rules engines
Module 03: Implement computer vision solutions
Design and implement image- and video-generation solutions
- Implement a solution that generates images from text prompts and reference media
- Implement a solution that generates videos from text prompts and reference media
- Configure image-editing workflows, including inpainting, mask‑based edits, and prompt‑driven modifications
- Implement workflows to edit generated videos
- Select and apply appropriate generation and editing controls provided by the platform
Design and implement multimodal understanding workflows
- Build a solution that analyzes visual context by using multimodal models
- Configure apps to produce concise or detailed captions for single or multiple images
- Implement a solution that enables question‑answering grounded in visual evidence
- Configure generation of alt‑text and extended image descriptions aligned to accessibility guidelines
- Implement visual understanding by configuring Azure Content Understanding in Foundry Tools to extract visual characteristics
- Implement video analysis workflows to process and interpret video segments
- Configure single‑task and pro‑mode Content Understanding pipelines
- Implement solutions that identify objects, components, or regions within images or video
Implement responsible AI for multimodal content
- Implement filters to classify unsafe or disallowed visual content
- Detect and mitigate indirect prompt injection by using embedded text in images
- Enforce visual policy rules, such as applying watermarks, flagging prohibited symbols, upholding brand usage requirements, and detecting potentially inappropriate content
Module 04: Implement text analysis solutions
Apply language model text analysis
- Implement filters to classify unsafe or disallowed visual content
- Detect and mitigate indirect prompt injection by using embedded text in images
- Enforce visual policy rules, such as applying watermarks, flagging prohibited symbols, upholding brand usage requirements, and detecting potentially inappropriate content
Implement speech solutions
- Implement workflows to convert speech to text and text to speech for agentic interactions
- Integrate speech as an agent modality, including custom speech models
- Enable multimodal reasoning from audio inputs
- Translate speech into other languages by using language models and Foundry Tools
Module 05: Implement information extraction solutions
Build retrieval and grounding pipelines
- Ingest and index content, such as documents, images, audio, and video
- Configure semantic search, hybrid search, and vector search for grounding
- Implement enrichment by using custom or built-in skills for text, images, and layout
- Configure RAG ingestion flow, including documents and using optical character recognition (OCR)
- Connect retrieval pipelines directly to workflows and agent tools
Extract content from documents
- Extract information by using multimodal pipelines that combine OCR, layout analysis, and field extraction
- Produce clean, grounded representations to use with agents and RAG by using Content Understanding
- Implement analyzers for generating structured or markdown outputs for downstream reasoning by using Content Understanding
- Extract information by using multimodal pipelines that combine OCR, layout analysis, and field extraction
- Produce clean, grounded representations to use with agents and RAG by using Content Understanding
- Implement analyzers for generating structured or markdown outputs for downstream reasoning by using Content Understanding
Best Online AI-103 Azure AI Apps and Agents Developer Associate Training in Pakistan
AI-103 is Microsoft’s current certification for engineers who build AI applications and agents on Azure, and it succeeds AI-102. If you have been considering AI-102, this is the exam to take instead, because search demand, employer recognition and Microsoft’s own curriculum are all moving here.
The certification is meaningfully broader than a generative AI credential. It covers planning and managing Azure AI solutions, building generative and agentic systems, and implementing computer vision, text analysis and information extraction. That breadth reflects what an AI engineer in an enterprise actually gets asked to do, which is rarely one thing.
The exam runs 120 minutes and is proctored with interactive components, meaning you perform tasks rather than only answering questions. Our program is built around that format, delivered live online across Pakistan.
Key Benefits of AI-103 Azure AI Apps and Agents Developer Associate
The enterprise AI credential for the platform local enterprises run
Pakistani banks, telecom operators and large corporates run Microsoft. When those organisations build AI capability, they build it on Azure. AI-103 is the credential attached to that spend.
Agentic systems are the current hiring priority
Organisations have moved past experimenting with chat interfaces and are now building agents that take actions inside business systems. This certification covers exactly that work.
Breadth that matches real job descriptions
Computer vision, document and information extraction, and text analysis appear constantly in enterprise requirements. Document processing alone is one of the highest volume AI use cases in banking and insurance here.
It builds on Azure knowledge you may already hold
If you have Azure Administrator AZ-104 or Azure Developer AZ-204, a significant portion of the platform context is already familiar.
Python skills transfer directly
The exam expects Python development experience. Students who need to build or refresh that foundation take Python Basic to Advance first.
Why Choose ItechNets for AI-103 Azure AI Apps and Agents Developer Associate
Microsoft authorised training partner with current curriculum
AI-103 is a new exam and material quality varies widely at this stage. Partner status means official curriculum rather than content assembled from documentation and guesswork.
96% pass rate across 15,000+ professionals trained
8,500+ alumni and 150+ hiring companies in our network. A new exam taught by an established provider is a safer proposition than a new exam taught by an untested one.
Interactive exam format practised in advance
Because AI-103 includes hands on components, our labs put you in the environment performing the tasks rather than reading about them.
Sit AI-103 at our own centre
Our authorised Pearson VUE test centre in DHA Phase IV, Lahore is authorised for Microsoft exams, and we arrange the booking for you.
Frequently Asked Questions About AI-103 Azure AI Apps and Agents Developer Associate
Should I take AI-102 or AI-103?
AI-103. It is the current certification and it succeeds AI-102. Employer recognition and Microsoft’s curriculum focus are both moving to the newer exam.
Do I need to know Python?
Yes. Microsoft expects experience developing applications in Python. This is the one genuine prerequisite, and candidates who skip it struggle regardless of how well they know Azure.
Do I need a machine learning or data science background?
No. This is an applied engineering certification about building solutions with existing Azure AI services, not about training models from scratch.
What exactly does the exam cover?
Five areas: planning and managing an Azure AI solution, implementing generative AI and agentic solutions, implementing computer vision solutions, implementing text analysis solutions, and implementing information extraction solutions.
How long is the exam?
120 minutes, proctored, including interactive components where you complete tasks in a live environment.
Is there demand for Azure AI skills in Pakistan?
Yes, and it is growing quickly in banking, insurance and telecom, where document processing and customer service automation are the leading use cases. Our advisors can share what our hiring partners are currently asking for, and the full catalogue covers related Azure paths.
