Azure AI Search (formerly Azure Cognitive Search) is a cloud-based search service that indexes diverse content (text, files, structured data) and enables intelligent information retrieval at scale via APIs, applications, or AI-driven agents.
Microsoft AI-3022 training with Azure AI Search provides you knowledge on a really modern fully managed search engine that supports both traditional search and modern AI-powered search scenarios (including retrieval-augmented generation for chatbots). The platform integrates natively with Azure’s AI ecosystem (such as Azure OpenAI, Azure AI Foundry, and Azure Machine Learning) and also allows incorporating custom or third-party AI models. In essence, Azure AI Search offloads the heavy lifting of indexing and querying data onto an Azure service, offering enterprise-grade scalability, security, and relevance tuning for building search-rich applications.
Key capabilities of Azure AI Search include:
- Full-Text Search (Keyword Search): Azure AI Search provides robust full-text search over indexed content, using proven information retrieval algorithms (e.g. BM25 for relevance scoring). It performs linguistic analysis (tokenization, stemming, etc.) to match keywords and supports features like filters, facets, fuzzy search, autosuggest, and synonym mapping out-of-the-box. This capability is ideal for exact term matches and traditional search experiences.
- Vector Similarity Search: The service supports AI-based vector search for semantic similarity matching. Content can be indexed as vector embeddings (numerical representations from AI models), and Azure AI Search uses k-nearest neighbors (KNN) algorithms (including approximate methods like HNSW) to find semantically relevant documents even when exact keywords aren’t present. This allows natural language queries to retrieve conceptually related information based on meaning rather than exact terms.
- Hybrid Search: Azure AI Search can combine keyword and vector search in a single query (a hybrid search). In this mode, a query is executed against both the full-text index and the vector index, and results are merged using techniques like Reciprocal Rank Fusion (RRF) to blend semantic relevance with keyword precision. Hybrid search leverages the strengths of both approaches – for example, finding documents that are topically relevant to a natural language question via vector search, while also ensuring exact matches (like names or codes) via keyword search. This is especially useful in retrieval-augmented generation scenarios where both context and specific facts matter.
- AI Enrichment (Cognitive Skills): A defining feature of Azure AI Search is its built-in cognitive skill pipeline for content enrichment. During indexing, you can attach an AI skillset that applies AI models to each item – for example, OCR for images, entity recognition and key phrase extraction for text, language translation, sentiment analysis, or even custom ML models. These skills enrich the content by generating additional structured information or summaries, which can then be indexed. This allows knowledge mining – extracting insights from unstructured data. For instance, Azure AI Search can ingest raw documents (PDFs, images, etc.) and use AI skills to find text in images, summarize long passages, detect language, and produce searchable text or vectors from the content. The enriched data can also be stored in a knowledge store (persisting intermediate outputs in Azure storage) for further analysis or downstream use. Essentially, cognitive skills turn Azure AI Search into an AI-powered indexing pipeline, so applications can search on both original content and AI-extracted insights.
- Semantic Ranking: Azure AI Search includes an optional semantic reranking feature (also known as “semantic search”) that uses advanced NLP models to improve result relevance. After the initial search (keyword or vector) retrieves a set of candidates, the semantic ranker (a large language model trained for search relevance) can re-rank the top results by understanding context and meaning rather than just keyword frequency. This L2 reranking step can boost documents that are a better semantic fit to the query’s intent. The outcome is “smart results” – search results ordered by a deeper understanding of the query and content, often improving relevance for natural language queries.
- Integration with Azure OpenAI (Generative AI): Azure AI Search is designed to integrate with Azure OpenAI Service to enable retrieval-augmented generation (RAG) and conversational AI scenarios. Developers can use Azure AI Search as the knowledge base for OpenAI’s GPT models – for example, searching the index for relevant documents and using those results as grounded context for GPT-based Q&A or chat completions. Azure AI Search provides the “grounding” data, while Azure OpenAI generates the answer, ensuring responses are accurate and based on the organization’s content. Moreover, Azure AI Search now offers built-in vectorization capabilities (using OpenAI or Azure AI models) to automatically generate embeddings from content, simplifying the creation of AI-ready indexes. In short, Azure AI Search + Azure OpenAI together form a foundation for building enterprise chatbots and copilots that can reason over private data.
- Enterprise Scale and Security: As an Azure service, Azure AI Search offers enterprise-grade scalability (handling large indexes and high query volumes) and security features. You can control access to search results at the document level using role-based access control and filters. The service supports indexing data from many sources: it has built-in indexers for Azure data stores (Azure Blob Storage, Azure SQL DB, Cosmos DB, etc.) and can ingest external data via Azure Data Factory or Logic Apps connectors, or through push APIs. This flexibility means you can consolidate heterogeneous content into a single searchable index under your control. Azure AI Search also integrates with other Azure services (like monitoring, and Azure AI services for custom skills), making it a comprehensive platform for intelligent search solutions.
(In summary, Azure AI Search enables developers to easily add rich search functionality to apps – from simple text search to advanced AI-driven search – without having to build or manage search infrastructure themselves. Its key features like full-text, vector, and hybrid search, combined with cognitive enrichment and integration with Azure’s AI, make it a powerful foundation for modern search and knowledge discovery applications.)
Course AI-3022 – Implement AI Skills in Azure AI Search: Overview and Modules
Azure AI-3022: Implement AI Skills in Azure AI Search is a one-day instructor-led training (ILT) course held by Dynamics Edge with a September 2025 date potentially available focused on building intelligent search solutions using Azure AI Search. It was released in late 2024 as part of Microsoft’s Data & AI training offerings. The course teaches participants how to create and configure an Azure AI Search service, enrich indexes with AI skills, and integrate search results into applications. In other words, it covers the full end-to-end process of implementing a knowledge mining solution: from ingesting and enriching data, to querying and refining search results, including the use of custom AI skills and advanced search features.
According to Microsoft’s description, AI-3022 is intended for developers or AI engineers who already understand the basics of search solutions and have experience with Azure and programming (C# or Python). The training is hands-on and scenario-driven – students create their own search index, implement custom skills for enrichment, and build a client app to query the index. Upon completion, learners are prepared to tackle real-world search projects with Azure AI Search.
Course Content and Modules: The course is structured into a series of modules that align with key aspects of an Azure AI Search implementation. Microsoft’s official learning path “Implement knowledge mining with Azure AI Search” contains eight modules that correspond closely to the AI-3022 course topics. These modules and their focus areas are outlined below:
| Module | Key Topics and Skills Covered |
|---|---|
| 1. Create an Azure AI Search solution | Provisioning an Azure AI Search service and understanding its components. Index creation and data ingestion (uploading or connecting to data sources). Implementing a basic search index and querying it from an application. Managing service capacity (choosing tiers, replicas/partitions) and learning how the indexing process works (e.g. how documents are tokenized and stored). Includes a hands-on exercise to build a simple end-to-end search solution. |
| 2. Create a custom skill for Azure AI Search | Extending the indexing pipeline with custom AI skills beyond the built-in cognitive skills. Defining a custom skill interface (skill manifest/schema) and registering it. Implementing a custom skill (for example, calling an external API or Azure Function during indexing). Scenarios include a custom text classification skill and a custom machine learning skill, illustrating how to integrate custom AI/ML models into Azure AI Search’s enrichment pipeline. This module solidifies how to enrich content with domain-specific logic by writing your own skill. |
| 3. Create a knowledge store with Azure AI Search | Configuring a knowledge store to persist the output of an enrichment pipeline. After applying cognitive skills to enrich data, a knowledge store allows saving projections of that enriched data into Azure Storage (tables or blobs) for further analysis or use. This module covers defining projections in a skillset (shaping the enriched data), enabling the knowledge store, and viewing the stored results. Learners see how knowledge stores facilitate additional insights (for example, Power BI reports or custom analytics on the extracted information). |
| 4. Implement advanced search features in Azure AI Search | Enhancing search relevance and the user search experience using Azure AI Search’s advanced capabilities. Topics include boosting document relevance with term boosting, adding scoring profiles to influence rank (e.g. recent items higher), using custom analyzers/tokenizers to control text processing, and supporting multiple languages in one index. Also covered is geo-spatial search, such as ordering results by distance from a location (geographical filtering/ranking). By the end of this module, students learn how to fine-tune search results and implement features like multilingual search and location-based search. |
| 5. Search data outside Azure using Azure Data Factory | Indexing data that resides outside of Azure or in external sources. This module introduces using Azure Data Factory to connect to on-premises or third-party data and pipe it into Azure AI Search indexes. It covers configuring Data Factory pipelines to pull data from external repositories and load it into the search index. Additionally, it explores the Azure AI Search push API as an alternative for indexing data from any source (bypassing built-in indexers). Learners practice bringing in external data and making it searchable, which is crucial for hybrid cloud scenarios. |
| 6. Maintain an Azure AI Search solution | Operational considerations for a production search solution. Students learn strategies to secure their search service (managing API keys, role-based access, and index-level security filters). They learn to optimize performance and manage costs (choosing appropriate replicas/partitions, scaling indexing throughput). The module covers improving reliability (high availability, query latencies) and monitoring the service’s health. It also includes debugging search issues using the Azure portal’s search explorer and diagnostics logs. By covering monitoring and maintenance, this module ensures learners can keep an Azure AI Search deployment running efficiently and troubleshoot problems. |
| 7. Perform search reranking with semantic ranking | Applying Azure AI Search’s semantic ranker to improve search result quality. This part explains what semantic ranking is and how Azure’s two-stage ranking process works. Students learn to enable semantic ranking on an index and configure queries to use the semantic ranker (which reorders the top results using an AI model). An exercise walks through using the semantic ranking feature on a sample index to see how it improves relevance for natural language queries. This module highlights how AI can be leveraged for smarter search results. |
| 8. Perform vector search and retrieval | Using vector search capabilities in Azure AI Search for semantic retrieval. The module introduces the concept of embeddings (vector representations of text/images) and how Azure AI Search uses them for similarity search. Learners practice adding vector content to an index (e.g. generating embeddings for documents using Azure OpenAI or other models) and executing vector queries via the REST API or SDK. It demonstrates end-to-end how to incorporate AI embeddings into search: from preparing the data and index for vectors, to issuing a vector query and interpreting results. This is critical for building modern AI-powered search applications where users ask questions in natural language and retrieve conceptually relevant information. |
Each of these modules includes lectures, demonstrations, and hands-on exercises/labs to reinforce the skills. The flow of the course is designed to mirror a real implementation project: starting from creating a search service, then enriching data with AI (custom skills, knowledge store), then improving search relevance (advanced features, semantic rerank, vectors), and finally learning to integrate external data and maintain the solution. Microsoft’s official learning path confirms these eight core modules as the roadmap to gaining expertise in Azure AI Search. By the end of AI-3022, attendees have built a functioning Azure AI Search solution with AI enrichments, and they understand how to apply Azure AI Search features to their own data and use cases.
(It’s worth noting that the hands-on exercises in AI-3022 are based on Microsoft Learn modules, meaning that learners can practice the same skills on their own. In fact, the “Implement knowledge mining with Azure AI Search” learning path on Microsoft Learn covers these topics in a self-paced format. The course essentially guides students through that content with an instructor and additional insights.)
Microsoft Learn Paths and Free Resources for Azure AI Search
Microsoft provides a number of free learning resources to support AI-3022 and Azure AI Search, ensuring that individuals can learn and practice these skills at no cost:
- Implement knowledge mining with Azure AI Search – Learning Path: This is an official, free self-paced learning path on Microsoft Learn that corresponds to the AI-3022 course. It consists of eight modules covering all key aspects of Azure AI Search (from creating a search service and enriching data, to advanced search features, vector search, and semantic reranking). Each module includes interactive content, step-by-step tutorials, and knowledge checks. Learners can progress through “Implement knowledge mining with Azure AI Search” to gain the same skills taught in the AI-3022 ILT, complete with hands-on exercises in the Azure sandbox. This path is a primary resource for anyone who wants to build Azure AI Search skills independently.
- Microsoft Azure AI Fundamentals – Knowledge Mining Module: For those new to the topic, Microsoft offers a fundamentals learning path that introduces knowledge mining in the context of Azure AI. Specifically, the “Microsoft Azure AI Fundamentals: Document Intelligence and Knowledge Mining” path includes a module called Fundamentals of Azure AI Search and knowledge mining, which gives a high-level overview of what Azure Cognitive Search (Azure AI Search) does and when to use it. This is part of the AI-900 Azure AI Fundamentals content. It’s a useful starting point to understand the basic concepts before diving into the more advanced modules. (The fundamentals path also contrasts search vs. document processing, helping learners choose the right service for a given scenario.)
- Azure AI Search Documentation: Microsoft’s official documentation for Azure AI Search is available on learn.microsoft.com and includes conceptual articles, how-to guides, and quickstart tutorials. These docs cover everything from “What is Azure AI Search?” to how to implement specific features (like adding vectors to an index, configuring semantic ranking, setting up indexers and skillsets, etc.). The documentation is an important free resource that complements the training modules with deeper technical details and examples. For instance, developers can follow step-by-step examples to create indexes, call the REST API, use .NET/Python SDKs, and integrate Azure AI Search with other Azure services. Microsoft Learn also provides code samples and references for the Azure AI Search SDKs. All these materials are freely accessible and regularly updated with the latest Azure AI Search features (such as new vector search capabilities or integrations).
- Microsoft Tech Community & Blogs: The Azure AI Search team and Microsoft Learning community periodically publish blog posts with updates, best practices, and learning announcements. For example, the Microsoft Tech Community blog announced the release of course AI-3022 and provided a brief overview of its content and objectives. The Tech Community is also where Microsoft shares news like enhancements to Azure AI Search (e.g. improvements in RAG, hybrid search techniques, etc.) and where instructors or experts post tips. While not structured as a course, these official blog posts are a valuable resource for staying up-to-date on Azure AI Search. They often supplement the formal documentation with insights into real-world use cases or upcoming training opportunities.
- Additional Learning Resources: Microsoft sometimes offers live and recorded training sessions on Azure AI Search. For instance, there have been Learn Live video sessions and Reactor workshops focusing on modules like “Create an Azure AI Search solution” and other topics. Moreover, Microsoft Learn includes an interactive Applied Skills assessment lab (see below) that allows learners to test their Azure AI Search skills in a sandbox environment for free. Lastly, Microsoft has partnered content (like Pluralsight courses on Azure Cognitive Search) which, although not free in all cases, are official learning resources that can reinforce the skills with video instruction.
In summary, even without attending the AI-3022 course, learners can utilize Microsoft’s free Learn modules and documentation to acquire the same knowledge. The official learning path and modules ensure that the content is accessible to everyone, and the Azure AI Search docs and community blogs provide ongoing support and depth. This democratization of learning resources reflects Microsoft’s commitment to helping professionals develop AI search skills at their own pace.
Certification and Credential Opportunities
Completing the AI-3022 course itself does not immediately grant a traditional Microsoft certification, but it is tied to a new form of skills credential – the Microsoft Applied Skills assessment. Specifically, AI-3022 is associated with an Applied Skills credential in “Implementing AI Skills in Azure AI Search”. After the training, learners have the opportunity to take an online lab-based assessment to earn this credential. The Microsoft Applied Skills assessment is essentially a practical exam delivered through Microsoft Learn: it presents a series of real-world tasks and asks the candidate to perform those tasks in a live Azure environment (for example, configuring a service, indexing data, executing queries, etc.). By successfully completing the scenario-based lab, the learner demonstrates hands-on proficiency with Azure AI Search and earns a verifiable digital credential from Microsoft.
About the Microsoft Applied Skills Credential: This credential is a relatively new offering from Microsoft aimed at validating specific technical skills through practical labs rather than traditional multiple-choice exams. The process is straightforward: learners prepare using the recommended training (such as the AI-3022 course and its corresponding Learn path) and then take the online, on-demand lab assessment when ready. Upon passing the assessment, Microsoft awards an Applied Skills badge/credential that can be shared on LinkedIn or a resume, signaling proficiency in that niche skill area. In the case of Azure AI Search, the credential confirms that the holder can implement and utilize AI enrichment in Azure AI Search to build an intelligent search solution. The official course announcement explicitly lists the credential as “Applied Skills Assessment”, meaning AI-3022 is part of Microsoft’s Applied Skills program.
It’s important to distinguish this Applied Skills credential from Microsoft’s traditional certifications. For example, Microsoft offers a broader role-based certification “Azure AI Engineer Associate” (earned by passing exam AI-102), which covers a wide range of Azure AI services including Azure AI Search, Azure OpenAI, Cognitive Services, etc. That certification demonstrates end-to-end AI solution expertise. In contrast, the Azure AI Search Applied Skills credential is narrowly focused on the search/knowledge mining scenario – it verifies that you can actually build and deploy an Azure AI Search solution with AI skills. Microsoft positions these Applied Skills credentials as complementary to the larger certifications: they provide proof of specific, in-demand technical abilities, which can be especially useful in rapidly evolving areas like AI. In other words, the Azure AI Search credential showcases your hands-on competency in enterprise search and AI enrichment, while a certification like the AI Engineer Associate showcases your overall proficiency in many Azure AI technologies.
Professional Value: Earning the “Implement AI Skills in Azure AI Search” Applied Skills credential can be valuable for professionals in the data and AI field. It serves as a micro-certification that is verified by Microsoft, giving employers confidence in the holder’s capability with Azure’s search AI tools. As organizations increasingly look to implement intelligent search (for internal knowledge bases, AI chatbots, etc.), having a proven skill in Azure AI Search is a differentiator. The credential effectively says: “I can build a cognitive search solution that uses AI to extract insights and support modern applications.” This is highly relevant in the era of generative AI and enterprise knowledge mining.
Because the Applied Skills assessments are scenario-based, they indicate practical experience – the ability to perform tasks in a live environment – rather than just theoretical knowledge. This aligns well with industry needs for demonstrable skills. Microsoft has noted that during the private preview, companies saw these micro-credentials as an agile way to fill skill gaps on their teams, allowing them to verify that employees or candidates can do the specific job at hand. For the individual professional, it can enhance one’s portfolio by highlighting specialized expertise in Azure AI Search, which might help in roles like AI Engineer, Search Engineer, or Solution Architect focusing on knowledge mining.
That said, the Applied Skills credential is not a full certification and is relatively new, so it may not yet be as widely recognized as long-standing certs. Its value is growing as Microsoft promotes these credentials; they are a great way to showcase cutting-edge skills (for example, vector search and hybrid AI techniques are very current topics in AI engineering). Many professionals might choose to earn the Azure AI Search Applied Skill in addition to a broader certification. For instance, one might obtain the Azure AI Engineer Associate certification for general credibility, and also earn the Azure AI Search Applied Skills badge to emphasize their depth in search technology. Together, these attest to both broad and deep skill sets.
In summary, upon completing AI-3022, you can pursue the Microsoft Applied Skills assessment for Azure AI Search and earn a credential that proves your ability to implement AI-powered search solutions. This credential holds professional value as a mark of practical expertise endorsed by Microsoft. It can bolster your resume and LinkedIn profile with evidence of your Azure AI Search skills, helping you stand out in fields like enterprise search, AI-driven application development, and knowledge management. As organizations adopt Azure AI Search for building retrieval-augmented generation systems and advanced search applications, having this credential demonstrates that you are equipped to deliver those solutions using Microsoft’s latest AI search technology.
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