Do you also want to establish your career as an Azure ai engineer? In order to embrace the AI revolution, there is a high demand for professionals who can assist create, integrating, designing, and deploying AI solutions on multiple tech platforms. To combat the evolving AI sector, one needs to be updated and have relevant certifications and skills to mark their presence.
The topics covered in this blog are:
- AI-102 Certification Overview
- Who is the Azure AI Engineer Associate?
- Why should you learn AI?
- Benefits of AI-102 Certification
- Who is this Certification for?
- AI-102 Exam Details
- Exam AI-102 Skills Measured
- How to Register for Azure AI 102 Exam
- Pre-requisites for AI-102 Certification
- AI 102 Study Guide
- AI-102 Exam Retake Policy
- Conclusion
- FAQs
AI-102 Certification Overview
This exam assesses your ability to plan and manage an Azure Cognitive Services solution, implement Computer Vision solutions, natural language processing solutions, knowledge mining solutions, and conversational AI solutions.
AI-102 students should have knowledge of developing, managing and implementing AI systems. They should be versed in Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework.
Are you new to Azure Cloud? Do check out our blog post on the Microsoft Azure Certification Path 2023Â and choose the best certification for you.
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Who is the Azure AI Engineer Associate?
- As an Azure AI engineer, you create, manage, and deploy AI solutions using Azure Cognitive Search, Azure Cognitive Services, and Microsoft Bot Framework.
- From requirement conception and design to development, maintenance, deployment, monitoring, and performance tweaking, they are involved in all aspects of AI development.
- Solution architects are among your coworkers, and Azure ai engineer associate translate their vision with your abilities.
- They need to collaborate with IoT experts, data scientists, engineers, and AI developers to design end-to-end AI solutions.
- They develop natural language processing, computer vision, knowledge mining, and conversational AI solutions using REST-based APIs and Software Development Kits (SDKs).
Why Should You Learn AI?
According to research, the global Artificial Intelligence (AI) industry is anticipated to achieve $250 billion or more by 2027. It highly indicates the world is prepping for an AI-driven future.
Many companies hunt for Azure ai engineer associate who can build, integrate, design, and implement AI solutions on various tech platforms to embrace the AI revolution. As a result, people must have the necessary IT certifications and understanding in the AI sector to demonstrate their abilities and competence to potential employers.
Benefits of AI-102 Certification
- By pursuing the certification, you’ll learn how to plan, construct, and manage knowledge mining, conversational AI, computer vision, and NLP systems on Azure.
- Students will collaborate with data scientists, solution architects, AI developers, IoT specialists, and data engineers to design end-to-end AI solutions.
- People will be able to demonstrate to the company your competence in developing AI solutions on Azure by acquiring the AI 102 certification.
- This program will assist Microsoft certified azure ai engineer associate in obtaining high-paying jobs.
- 26% of technical professionals have reported job advancements as a result of acquiring an AI-102 certification, and 35% of technical professionals reported compensation or wage increases as a result of getting certified.
- You can update your resume with an AI-102 certificate will help you develop your career and increase your chances of being hired.
Checkout: Azure Data Factory Interview Questions
Who is this Certification For?
AI-102 certification is for all those:
- Students with a keen interest in AI, Machine Learning, and Data Science.
- Data science, database engineering, and business intelligence professionals.
- IT specialists with a thorough understanding of SQL, Python, or Scala languages.
- On Azure, you’ll find experts in computer vision, natural language processing, knowledge mining, and conversational AI solutions.
AI-102 Exam Details
Exam Name Exam AI-102: Designing and Implementing an Microsoft Azure AI Solution Exam Duration 180 Minutes Exam Type Multiple Choice Examination Number of Questions 40 - 60 Questions Exam Fee $165 Eligibility/Pre-Requisite None Exam validity 2 years Exam Languages English, Japanese, Korean, and Simplified Chinese
Exam AI-102 Skills Measured
Plan and manage an Azure Cognitive Services solution 15–20% Implement Computer Vision solutions 20-25% Implement natural language processing solutions 20-25% Implement knowledge mining solutions 15–20% Implement conversational AI solutions 15-20%
How to Register for Azure AI-102 Exam
You can register for the Microsoft Azure AI Engineer Associate Exam (AI-102) by going to the Official Microsoft Page.
Pre-requisites for AI-102 Certification
The Candidates taking up this exam should be proficient in languages ​​such as::
- Python
- Javascript
- C#
AI-102 Study Guide
Plan and manage an Azure Cognitive Services solution (15-20%)
Select the appropriate Cognitive Services resource
- Select the appropriate cognitive service for a vision solution
- Select the appropriate cognitive service for a language analysis solution
- Select the appropriate cognitive Service for a decision support solution
- Select the appropriate cognitive service for a speech solution
Plan and configure security for a Cognitive Services solution
- Manage Cognitive Services account keys
- Manage authentication for a resource
- Secure Cognitive Services by using Azure Virtual Network
- Plan for a solution that meets responsible AI principles
Create a Cognitive Services resource
- Create a Cognitive Services resource
- Configure diagnostic logging for a Cognitive Services resource
- Manage Cognitive Services costs
- Monitor a cognitive service
- Implement a privacy policy in Cognitive Services
Plan and implement Cognitive Services containers
- Identify when to deploy to a container
- Containerize Cognitive Services (including Computer Vision API, Face API, Languages, Speech, Form Recognizer)
- Containerize other Cognitive Services
Implement Computer Vision solutions (20-25%)
Analyze images by using the Computer Vision API
- Retrieve image descriptions and tags by using the Computer Vision API
- Identify landmarks and celebrities by using the Computer Vision API
- Detect brands in images by using the Computer Vision API
- Moderate content in images by using the Computer Vision API
- Generate thumbnails by using the Computer Vision API
Extract text from images
- Extract text from images by using the OCR API
- Extract text from images or PDFs by using the Read API
- Convert handwritten text by using Ink Recognizer
- Extract information from forms or receipts by using the pre-built receipt model in Form Recognizer
- Build and optimize a custom model for Form Recognizer
Extract facial information from images
- Detect faces in an image by using the Face API
- Recognize faces in an image by using the Face API
- Use the Face client library
- Configure persons and person groups
- Analyze facial attributes by using the Face API
- Match similar faces by using the Face API
Implement image classification by using the Custom Vision service
- Label images by using the Computer Vision Portal
- Train a custom image classification model in the Custom Vision Portal
- Train a custom image classification model by using the SDK
- Manage model iterations
- Evaluate classification model metrics
- Publish a trained iteration of a model
- Export a model in an appropriate format for a specific target
- Consume a classification model from a client application
- Deploy image classification custom models to containers
Implement an object detection solution by using the Custom Vision service
- Label images with bounding boxes by using the Computer Vision Portal
- Train a custom object detection model by using the Custom Vision Portal
- Train a custom object detection model by using the SDK
- Manage model iterations
- Evaluate object detection model metrics
- Publish a trained iteration of a model
- Consume an object detection model from a client application
- Deploy custom object detection models to containers
Analyze video by using Azure Video Analyzer for Media (formerly Video Indexer)
- Process a video
- Extract insights from a video
- Moderate content in a video
- Customize the Brands model used by Video Indexer
- Customize the Language model used by Video Indexer by using the Custom Speech service
- Customize the Person model used by Video Indexer
- Extract insights from a live stream of video data
Implement natural language processing solutions (20-25%)
Analyze text by using the Language service
- Retrieve and process key phrases
- Retrieve and process entity information (people, places, urls, etc.)
- Retrieve and process sentiment
- Detect the language used in text
Manage speech by using the Speech service
- Implement text-to-speech
- Customize text-to-speech
- Implement speech-to-text
- Improve speech-to-text accuracy
Translate language
- Translate text by using the Translator service
- Translate speech-to-speech by using the Speech service
- Translate speech-to-text by using the Speech service
Build a initial language model by using Language Understanding Service (LUIS)
- Create intents and entities based on a schema, and add utterances
- Create complex hierarchical entities
- Use this instead of roles
- Train and deploy a model
Iterate on and optimize a language model by using Language Understanding
- Implement phrase lists
- Implement a model as a feature (i.e. prebuilt entities)
- Manage punctuation and diacritics
- Implement active learning
- Monitor and correct data imbalances
- Implement patterns
Manage a Language Understanding model
- Manage collaborators
- Manage versioning
- Publish a model through the portal or in a container
- Export a LUIS package
- Deploy a LUIS package to a container
- Integrate Bot Framework (LUDown) to run outside of the LUIS portal
Implement knowledge mining solutions (15-20%)
Implement a Cognitive Search solution
- Create data sources
- Define an index
- Create and run an indexer
- Query an index
- Configure an index to support autocomplete and autosuggest
- Boost results based on relevance
- Implement synonyms
Implement an enrichment pipeline
- Attach a Cognitive Services account to a skillset
- Select and include built-in skills for documents
- Implement custom skills and include them in a skillset
Implement a knowledge store
- Define file projections
- Define object projections
- Define table projections
- Query projections
Manage a Cognitive Search solution
- Provision Cognitive Search
- Configure security for Cognitive Search
- Configure scalability for Cognitive Search
Manage indexing
- Manage re-indexing
- Rebuild indexes
- Schedule indexing
- Monitor indexing
- Implement incremental indexing
- Manage concurrency
- Push data to an index
- Troubleshoot indexing for a pipeline
Implement conversational AI solutions (15-20%)
Create a Knowledge Base by Using QnA Maker
- Create a QnA Maker service
- Create a knowledge base
- Import a knowledge base
- Train and test a knowledge base
- Publish a knowledge base
- Create a multi-turn conversation
- Add alternate phrasing
- Add chit-chat to a knowledge base
- Export a knowledge base
- Add active learning to a knowledge base
- Manage collaborators
Design and implement conversation flow
- Design conversation logic for a bot
- Create and evaluate *.chat file conversations by using the Bot Framework Emulator
- Add language generation for a response
- Design and implement adaptive cards
Create a Bot by Using the Bot Framework SDK
- Implement dialogs
- Maintain state
- Implement logging for a bot conversation
- Implement a prompt for user input
- Add and review bot telemetry
- Implement a bot-to-human handoff
- Troubleshoot a conversational bot
- Add a custom middleware for processing user messages
- Manage identity and authentication
- Implement channel-specific logic
- Publish a bot
Create a Bot by Using the Bot Framework Composer
- Implement dialogs
- Maintain state
- Implement logging for a bot conversation
- Implement prompts for user input
- Troubleshoot a conversational bot
- Test a bot by using the Bot Framework Emulator
- Publish a bot
Integrate Cognitive Services into a Bot
- Integrate a QnA Maker service
- Integrate a LUIS service
- Integrate a Speech service
- Integrate Dispatch for multiple language models
- Manage keys in the app settings file
AI-102 Exam Retake Policy
The AI-102 exam retake policy is as follows:
- If a candidate fails on the first attempt, they must wait for 24 hours before retaking the exam.
- If a candidate again fails on the second attempt, then the candidate will have to wait for 14 days.
- A candidate will be given a maximum of five attempts to retake an exam in a year.
Conclusion
Designing and Implementing a Microsoft Azure AI Solution is primarily aimed at applicants who desire to advance their careers in the Microsoft Azure area. The Microsoft Certified – Azure AI Engineer Associate exam certifies that the candidate has the essential knowledge and skills required to Design and Implement a Microsoft Azure AI Solution. It not only adds relevant skills but also you in getting a handsome package.
FAQs
Q1. Is AI-102 certification worth it?
Yes, the AI-102 certification is worth it for individuals who want to demonstrate their expertise in designing and implementing artificial intelligence (AI) solutions on the Microsoft Azure platform. The certification is intended for solution architects and developers who work with AI technologies such as natural language processing, speech, computer vision, and machine learning.
Q2. How difficult is AI-102 exam?
The difficulty level of the AI-102 exam can vary depending on an individual’s experience and familiarity with Azure AI technologies. However, in general, the exam is considered to be of moderate to high difficulty. To pass the exam, individuals must have a thorough understanding of Azure AI services and must be able to design and implement AI solutions that meet specific business requirements.
Q3. What is the salary of an Azure AI engineer?
The salary of an Azure AI engineer can vary depending on factors such as experience, location, company size, and industry. However, according to salary data from Glassdoor, the average base salary for an Azure AI engineer in the United States is around $140,697 per year.
Q4. How long is AI-102 valid for?
AI-102: Designing and Implementing a Microsoft Azure AI Solution Certification will remain valid for one year.
Q5. How long is the AI-102 exam?
AI-102 exam is 120 minutes long.
Q6. How many questions are on the AI-102?
The Designing and Implementing a Microsoft Azure AI Solution AI-102 exam has 40-60 questions.
Q7. What is passing score for AI-102?
The passing score for the Microsoft AI102 exam is 700 out of 1000 marks.
Q8. How much does it cost to take the AI 102 exam?
The AI 102 exam costs $165 USD.
