By: Daniel Calbimonte | Updated: 2024-03-01 | Comments | Related: More > Professional Development Certifications
Problem
Azure is providing several services related to artificial intelligence (AI). Is there a certification to create AI solutions?
Solution
The exam for this is AI-102 Exam: Designing and Implementing a Microsoft Azure AI Solution. This tip will provide tips, books, courses, and links to pass the AI-102 exam. This exam is used to certify that the user can design and implement AI solutions in Azure.
What is the AI-102 Exam?
This Microsoft official exam was created to certify the knowledge to design and implement Microsoft Azure AI solutions.
Is this Exam Difficult?
If you already have experience creating most of the AI solutions in Azure, it is recommended that you study for a few months to pass this exam successfully. However, if you do not have experience with AI solutions in Azure, or if you do not have any experience with Azure or REST API, Python, and C#, it will take approximately six months to one year to prepare for the exam.
What Score is Required to Pass this Exam?
The passing score is 700/1000 approximately.
What Books are Recommended to Prepare for this Exam?
The following books will be helpful for the exam:
- AI-102: Designing and Implementing a Microsoft Azure AI Solution: Study Guide with Practice Questions and Labs - First Edition
- AI-102: Designing and Implementing a Microsoft Azure AI Solution: Exam Cram Notes
- Microsoft Certified Exam Guide - Azure AI Engineer Associate (AI-102): A Comprehensive Guide to Azure AI Engineer Associate (AI-102) Microsoft Certification Kindle
- MICROSOFT AZURE AI ENGINEER ASSOCIATE | MASTER THE EXAM (AI-102): 10 PRACTICE TESTS, 500 RIGOROUS QUESTIONS, GAIN WEALTH OF INSIGHTS, EXPERT EXPLANATIONS AND ONE ULTIMATE GOAL Kindle
- AI-102: Designing and Implementing a Microsoft Azure AI Solution - Practice exams: 110 practice questions with explanations
Are There Links Available for Studying for the Exam?
Yes. The following links can be helpful for the exam:
Plan and manage an Azure AI solution
Strategize and Oversee the Management of an Azure AI Solution
- Choose the suitable Azure AI service for computer vision solution
- Choose the suitable Azure AI service for a natural language processing solution.
- Choose the suitable Azure AI service for a decision support solution.
- Choose the suitable Azure AI service for a Computer vision Solution
- Choose the suitable Azure AI service for a speech solution.
- Choose the suitable Azure AI service for a document intelligence solution.
- Choose the suitable Azure AI service for a mining solution
Devise, Generate, and Deploy an Azure AI Service
- Develop a plan aligning with Responsible AI principles.
- Generate an Azure AI resource.
- Establish a default endpoint for a service.
- Incorporate Azure AI services into a continuous integration and continuous delivery (CI/CD) pipeline.
- Strategize and execute container deployment.
Supervise, Oversee, and Safeguard an Azure AI Service
- Set up diagnostic logging configurations.
- Supervise an Azure AI resource.
- Administer expenses for Azure AI services.
- Supervise account keys.
- Safeguard account keys using Azure Key Vault.
- Oversee authentication for an Azure AI Service resource.
- Handle confidential communications within the Azure AI environment.
Implement decision support solutions
Devise Decision-Support Solutions for Data Monitoring and Content Delivery
- Execute a data monitoring solution with Azure AI Metrics Advisor.
- Execute a text moderation solution with Azure AI Content Safety.
- Execute an image moderation solution with Azure AI Content Safety.
Implement computer vision solutions
Analyze Different Images
- Choose visual features to fulfill image processing requirements.
- Identify objects in images and generate image tags.
- Incorporate image analysis features into an image processing request. Interpret image processing responses.
- Extract text from images using Azure AI Vision.
- Transform handwritten text using Azure AI Vision.
Execute Custom Computer Vision Models Using Azure AI Vision
- Select between image classification and object detection models.
- Annotate images.
- Train a customized image model, including image classification and object detection.
- Assess metrics for custom vision models.
- Release a custom vision model.
- Utilize a custom vision model.
Analyze Videos
- Employ Azure AI Video Indexer to extract insights from a video or live stream.
- Leverage Azure AI Vision Spatial Analysis to identify presence and movement of individuals in a video.
Implement natural language processing solutions
Analyze a Text Using Azure AI Language
- Get key phrases.
- Get entities.
- Determine the sentiment of the text.
- Detect the language used in the text.
- Identify personally identifiable information (PII) in text.
Process Spoken Language with Azure AI Speech
- Develop text-to-speech
- Implement voice-to-text conversion.
- Enhance speech synthesis using Speech Synthesis Markup Language (SSML).
- Develop customized speech solutions.
- Deploy intent recognition systems.
- Deploy keyword recognition systems.
Translate Languages
- Translate different texts and documents using the Azure AI Translator service.
- Develop custom translation, involving training, enhancement, and publication of a bespoke model.
- Translate spoken language using the Azure AI Speech service.
- Translate spoken language to written text using the Azure AI Speech service.
- Perform simultaneous translation into multiple languages.
Implement and Oversee a Language Comprehension Model Using Azure AI Language
- Develop intents and incorporate utterances.
- Establish entities.
- Train, assess, deploy, and test a language comprehension model.
- Optimize a language comprehension model.
- Utilize a language model from a client application.
- Back up and restore language comprehension models.
Create a Question-Answering Solution Using Azure AI Language
- Develop a question-answering project.
- Manually include question-and-answer pairs.
- Import data sources.
- Train and evaluate a knowledge base.
- Release a knowledge base.
- Develop a multi-turn conversation.
- Include alternative phrasing.
- Integrate casual conversation into a knowledge base.
- Export a knowledge base.
- Create a question-answering solution supporting multiple languages.
Implement knowledge mining and document intelligence solutions
Implement an Azure Cognitive Search Solution
- Implement an Azure Cognitive Search Solution
- Allocate resources for Cognitive Search.
- Establish data sources.
- Create an index.
- Understand a skillset.
- Develop custom skills and incorporate them into a skillset.
- Establish and execute an indexer.
- Query indexes, including syntax, sorting, filtering, and wildcards.
- Administer Knowledge Store projections, covering file, object, and table projections.
Implement an Azure AI Document Intelligence Solution
- Allocate resources for Document Intelligence.
- Utilize prebuilt models to glean data from documents.
- Deploy a bespoke document intelligence model.
- Train, evaluate, and release a bespoke document intelligence model.
- Create a composite document intelligence model.
- Deploy a document intelligence model as a custom Azure Cognitive Search skill.
Implement Generative AI Solutions
Leverage Azure OpenAI Service to Produce Content
- Allocate resources for an Azure OpenAI Service.
- Choose and implement an Azure OpenAI model.
- Submit cues to generate natural language.
- Submit cues to generate code.
- Use the DALL-E model to produce images.
- Utilize Azure OpenAI APIs to submit cues and receive responses.
Fine-Tune an Azure OpenAI Model
- Configure parameters to manage generative behavior.
- Apply techniques to enhance responses.
- Utilize personal data with an Azure OpenAI model.
- Fine-tune OpenAI models in Azure
Next Steps
For more information about Microsoft exams, refer to the following links.
- DP 500 Certification Exam Preparation for Microsoft Azure and Power BI
- AI-900: Microsoft Azure AI Fundamentals Certification Exam Tips and Tricks
- DP 500 Certification Exam Preparation for Microsoft Azure and Power BI
About the author
Daniel Calbimonte is a Microsoft SQL Server MVP, Microsoft Certified Trainer and 6-time Microsoft Certified IT Professional. Daniel started his career in 2001 and has worked with SQL Server 6.0 to 2022. Daniel is a DBA as well as specializes in Business Intelligence (SSIS, SSAS, SSRS) technologies.This author pledges the content of this article is based on professional experience and not AI generated.
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Article Last Updated: 2024-03-01
