Ready to have a quick revision for the Microsoft Azure AI-100 exam? The Designing and Implementing an Azure AI Solution Cheat Sheet is all you need. This Cheat Sheet covers all the essential details and required resources to crack the exam. It sets you on the right track to achieve this much valued credential and ace the exam. Moreover, it is your information goldmine to pass the exam with flying colours.
Microsoft AI-100 exam is getting retired on June 30, 2021. A new replacement exam Designing and Implementing a Microsoft Azure AI Solution Beta (AI-102) is available.
Microsoft Azure Certifications are a hot cake for companies. They are recognised across the globe. Consequently, Demand for individuals with azure credentials has soared to great heights. Validating your skills through specific credentials for your career growth has become the new-age trend in this virtual world. But before we begin with the revision journey lets have a quick look at the exam details.
Microsoft Azure AI-100 Overview
The Microsoft Azure AI-100 is a certification on Designing and Implementing an Azure AI Solution. This exam has been focused to increase the coverage of Azure technologies. It develops an understanding of machine learning and deploying end to end AI solutions using Azure AI services.
The exam emphasizes how Azure services meet business and technical requirements. Azure offers a wide spread of services intended to enable rapid development of high performance AI solutions. Moreover, this exam will measure and emphasize your ability to accomplish technical tasks such as analysing solution requirements, designing solutions, integrating AI modules and managing solutions.
Knowledge requirement for the exam
Following are the Prerequisites for the exam:
- Proficiency and hands-on experience in developing AI applications and agents utilizing Microsoft Azure Cognitive Services.
- Familiarity with Azure Bot Service, Azure Cognitive Search, and Azure-based data storage.
- Capability to recommend solutions that leverage open-source technologies, comprehending the components encompassing the Azure AI portfolio and the various data storage alternatives accessible within Azure.
- Understanding when a custom API should be developed to meet specific requirements.
Cheat Sheet: Microsoft Azure AI-100
Obtaining a Microsoft-recognized certification from the industry can provide you with a distinct advantage over other candidates. Furthermore, achieving a professional certification boosts your attractiveness to potential employers and showcases your competence. It’s crucial to dedicate adequate time to study and preparation for this exam. This cheat sheet equips you with the appropriate resources and a strategic approach to facilitate your success.
Review the Exam Objectives
To begin with, it’s crucial to establish a solid understanding of the exam curriculum in order to grasp its concepts effectively. Considering the extensive syllabus that this exam encompasses, it is highly advisable to gain complete clarity about the exam curriculum. The Course Outline offers comprehensive information regarding the exam domains. It is essential to thoroughly comprehend these domains and customize your study plan to align with these concepts. The exam domains included in this examination are as follows:
Topic 1: Analyze solution requirements
1.1 Recommending Azure Cognitive Services APIs to meet business requirements
Microsoft Documentation: Azure Cognitive Services
- Firstly, selecting the processing architecture for a solution (Microsoft Documentation: Machine Learning Products)
- Secondly, Choosing the appropriate data processing technologies (Microsoft Documentation: Choosing Data Store)
- Thirdly, selecting the appropriate AI models and services
- Also, identifying components and technologies required to connect service endpoints (Microsoft Documentation: Components of REST)
- Moreover, identify automation requirements (Microsoft Documentation: Azure Automation)
1.2 Mapping security requirements for tools, technologies, and processes
- Additionally, identifying processes and regulations needed to conform to data privacy, protection, and regulatory requirements (Microsoft Documentation: Regulatory compliance)
- Further, identify which users and groups have access to information and interfaces (Microsoft Documentation: Permission, user and groups in Azure)
- Furthermore, locating appropriate tools for a solution (Microsoft Documentation: Azure Security)
- Then, identify auditing requirements (Microsoft Documentation: Azure Auditing)
1.3 Selecting the software, services, and storage required to support a solution
- Subsequently, identifying appropriate services and tools for a solution (Microsoft Documentation: Azure Machine Learning, Microsoft Cognitive Service)
- Likewise, locating integration points with other Microsoft services (Microsoft Documentation: Azure Event Grid, Azure Event Hubs)
- Finally, identify storage required to store logging, bot state data, and Azure Cognitive Services output (Microsoft Documentation: Data Store )
Topic 2: Design AI solutions
2.1 Designing solutions that includes one or more pipelines
- To begin with, defining an AI application workflow process (Microsoft Documentation: Azure Machine Learning Pipeline)
- Then, design a strategy for ingesting and egress data
- Also, designing the integration point between multiple workflows and pipelines
- Moreover, outlining pipelines that use AI apps (Microsoft Documentation: Managing Web Services )
- In addition to, plotting pipelines that call Azure Machine Learning models (Microsoft Documentation: Creating web services endpoints)
- Subsequently, , selecting an AI solution that meets cost constraints
2.2 Designing solutions that uses Cognitive Services
- Also, Planning solutions that use vision, speech, language, knowledge, search, and anomaly detection APIs (Microsoft Documentation: Recognize speech from Microphone, Get intent with REST API, Visualise Anomalies )
2.3 Design solutions that implement the Microsoft Bot Framework
- Moreover, integrating bots and AI solutions
- Further, outlining bot services that use Language Understanding (LUIS) (Microsoft Documentation: Using Web App Bot)
- Furthermore, modeling bots that integrate with channels (Microsoft Documentation: Connect Bot to channels)
- Eventually, integrating bots with Azure app services and Azure Application Insights (Microsoft Documentation: Create Azure Web App Bot)
2.4 Designing the compute infrastructure to support a solution
- Likewise, identifying whether to create a GPU, FPGA or CPU-based solution (Microsoft Documentation: FPGA)
- Additionally, identify if to use a cloud-based, on-premises, or hybrid compute infrastructure
- Also, selecting a compute solution that meets cost constraints (Microsoft Documentation: Choosing Azure Compute Service)
2.5 Model data governance, compliance, integrity, and security
- Moreover, defining how users and applications will authenticate to AI services (Microsoft Documentation: Authenticate request to Azure Cognitive Service)
- Further, designing a content moderation strategy for data usage within an AI solution (Microsoft Documentation: Azure Content Moderator)
- Furthermore, ensuring that data adhere to compliance requirements defined by your organization (Microsoft Documentation: Get compliance data of Azure Resources, Microsoft Compliance Manager)
- Then, ensuring appropriate governance of data (Microsoft Documentation: Azure Governance)
- Lastly, designing strategies to ensure that the solution meets data privacy regulations and industry standards (Microsoft Documentation: Data collection, retention, and storage in application )
Topic 3: Implement and monitor AI solutions
3.1 Implementing an AI workflow
- Firstly, developing AI pipelines (Microsoft Documentation: Azure Machine Learning Pipelines)
- Secondly, managing the flow of data through the solution components (Microsoft Documentation: Azure IoT, Advanced Analytics Architecture)
- Thirdly, implementing data logging processes (Microsoft Documentation: Diagnostic Logging)
- Also, defining and construct interfaces for custom AI services (Microsoft Documentation: Configure Bing Custom Search)
- Further, creating solution endpoints (Microsoft Documentation: Using Azure Events Hub)
- Moreover, developing streaming solutions (Microsoft Documentation: Azure Stream Analytics Solutions)
3.2 Integrating AI services and solution components
- To begin with, configuring prerequisite components and input datasets to allow the consumption of Azure Cognitive Services APIs (Microsoft Documentation: Building training data set)
- Then, configuring integration with Azure Cognitive Services (Microsoft Documentation: Configuring apps to expose web APIs)
- Also, configure prerequisite components to allow connectivity to the Microsoft Bot Framework (Microsoft Documentation: Create Bot with Azure Bot Service)
- Additionally, implementing Azure Cognitive Search in a solution (Microsoft Documentation: Search Web using Bing Web Search REST APIs and C#)
3.3 Monitoring and evaluating the AI environment
- Firstly, identifying the differences between KPIs reported metrics and root causes of the differences (Microsoft Documentation: Create custom KPI dashboard)
- Secondly, locating the differences between expected and actual workflow throughput (Microsoft Documentation: Scalability and Performance, Monitor and collect data from ML web services endpoints)
- Thirdly, maintaining an AI solution for continuous improvement (Microsoft Documentation: Create CI/CD pipelines using Azure pipelines, docker and Kubernetes)
- Subsequently, monitoring AI components for availability (Microsoft Documentation: Application insights telemetry data model, Collect Azure Platform logs in logs analytics workspace in Azure Monitor)
- Finally, recommending changes to an AI solution based on performance data
Quick links for Resources to enhance your learning
The demand for Microsoft Azure AI professionals has accelerated and so there are abundant resources in the marketplace to prepare for the Microsoft AI-100 exam. It is necessary to choose the ones that best suit you and are authentic. Here are some learning resources for you to explore-
Microsoft Learning Platform
A valuable resource for exam preparation is the Microsoft learning platform. However, it is essential to navigate through Microsoft’s official website. For the AI-100 exam, it is advisable to begin by exploring the official Microsoft website to access authentic information regarding the examination. You can readily find the AI-100 page, where you can review all the essential details about the AI-100 exam.
- Evaluate text with Azure Cognitive Language Services
- Process and Translate Speech with Azure Cognitive Speech Services
- Create Intelligent Bots with the Azure Bot Service
Microsoft Docs
After that, you can move on to Microsoft Documentation where you can easily understand the Microsoft AI solutions and Machine learning concepts. Moreover, you also get to know the different scales of different Azure services. Microsoft Docs consists of modules that will help you gain a lot of knowledge about AI and the different services in a sequence.
Instructor-led Training
Microsoft offers its own instructor-led training. This training will help you gain knowledge designing Azure AI solutions by building customer support chat Bot using artificial intelligence from the Microsoft Azure platform including language understanding and pre-built AI functionality in the Azure Cognitive Services. However, this training is designed for Cloud Solution Architects, Azure artificial intelligence designers, and AI developers. The training programme offered by Microsoft is:
Reference Books
Those who are dedicated to passing the exam know the importance of books during the time of preparation. However, while revising for the exam books can be really helpful to understand the core of the topics. You must refer books that match your level of understanding and offer Real life business examples to make it easier for you. You can refer the following book to ace your revisions.
- Secrets of AI-100 Designing and Implementing an Azure AI for your success by Nikhil J
Online Tutorials
Online Tutorials serve as valuable resources to enhance your knowledge and gain an in-depth understanding of exam concepts. They also comprehensively cover exam details and policies. Hence, engaging with online tutorials will significantly boost your exam preparation.
Practice Tests to self evaluate
Errors are a natural part of the learning process, but they can certainly be minimized. In the context of exams, practice papers prove immensely valuable in reducing errors. Additionally, they play a crucial role in training the brain. Practice papers offer a simulation that allows the brain to acclimate to the conditions of the actual exam.
Consequently, engaging with practice tests aids in identifying your strengths and addressing your weaknesses. They provide insights into areas where improvement is needed. It is crucial to emphasize that practice tests should be undertaken after covering the entire syllabus. Furthermore, attempting multiple practice tests enhances your confidence levels, motivating you to continually surpass your own performance with each successive test. Get Ready to Self Evaluate your preparations with Practice Tests!
