Designing and Implementing a Microsoft Azure AI Solution
Last Update December 25, 2024
Total Questions : 321
Our Azure AI Engineer Associate AI-102 exam questions and answers cover all the topics of the latest Designing and Implementing a Microsoft Azure AI Solution exam, See the topics listed below. We also provide Microsoft AI-102 exam dumps with accurate exam content to help you prepare for the exam quickly and easily. Additionally, we offer a range of Microsoft AI-102 resources to help you understand the topics covered in the exam, such as Azure AI Engineer Associate video tutorials, AI-102 study guides, and AI-102 practice exams. With these resources, you can develop a better understanding of the topics covered in the exam and be better prepared for success.
Exam Name | Designing and Implementing a Microsoft Azure AI Solution |
Exam Code | AI-102 |
Actual Exam Duration | The duration of the Microsoft AI-102 exam is 180 minutes (3 hours). |
What exam is all about | Microsoft AI-102 is an exam that tests the candidate's knowledge and skills in designing and implementing AI solutions using Microsoft Azure technologies. The exam covers various topics such as natural language processing, computer vision, speech recognition, and machine learning. The exam is intended for professionals who have experience in developing AI solutions and want to validate their skills and knowledge in this area. Passing the Microsoft AI-102 exam leads to the Microsoft Certified: Azure AI Engineer Associate certification. |
Passing Score required | The passing score required in the Microsoft AI-102 exam is 700 out of 1000. This means that you need to answer at least 70% of the questions correctly to pass the exam. The actual passing score may vary depending on the difficulty level of the exam and the number of questions included in it. It is important to note that the passing score is subject to change without prior notice, so it is best to check the official Microsoft website for the latest information. |
Competency Level required | Based on the official Microsoft documentation, the AI-102 exam is designed for Azure AI Engineers who have intermediate to advanced knowledge and experience in designing and implementing AI solutions using Azure services. Candidates should have a good understanding of machine learning models, natural language processing, computer vision, and conversational AI. They should also be proficient in programming languages such as Python and have experience working with Azure Machine Learning, Azure Cognitive Services, and Azure Bot Service. Additionally, candidates should have experience in deploying and managing AI solutions on Azure. |
Questions Format | Based on the exam objectives and previous Microsoft certification exams, the AI-102 exam may include multiple-choice questions, drag-and-drop questions, and scenario-based questions that require candidates to analyze and solve real-world problems using AI technologies. The exam may also include simulations or practical tasks that test candidates' ability to implement and deploy AI solutions using Microsoft Azure services. |
Delivery of Exam | The Microsoft AI-102 exam is a computer-based exam that is delivered online through the Microsoft Learning platform. It is a timed exam that consists of multiple-choice questions and scenario-based questions. The exam is designed to test the candidate's knowledge and skills in designing and implementing AI solutions using Microsoft Azure services. The exam duration is 180 minutes, and the passing score is 700 out of 1000. |
Language offered | English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil), Arabic (Saudi Arabia), Russian, Chinese (Traditional), Italian, Indonesian (Indonesia) |
Cost of exam | $165 USD |
Target Audience | The target audience for Microsoft AI-102 certification exam includes: 1. AI developers who want to enhance their skills in designing and implementing AI solutions using Microsoft Azure services. 2. Data scientists who want to learn how to use Azure AI services to build intelligent applications. 3. Solution architects who want to design and implement AI solutions using Azure services. 4. Technical leads who want to lead AI projects and teams using Azure services. 5. Business analysts who want to understand how AI can be used to solve business problems using Azure services. 6. IT professionals who want to learn how to integrate AI solutions with existing systems and infrastructure using Azure services. 7. Developers who want to learn how to build intelligent applications using Azure services. 8. Anyone who wants to learn about AI and its applications in the Microsoft Azure ecosystem. |
Average Salary in Market | According to Payscale, the average salary for a Microsoft Certified Azure AI Engineer Associate is around $120,000 per year in the United States. This may vary depending on factors such as location, experience, and industry. |
Testing Provider | You can visit the official Microsoft website to register for the exam and get more information about it. Additionally, you can also check out various online platforms that offer practice tests and study materials for the AI-102 exam. |
Recommended Experience | based on the information available, the recommended experience for the Microsoft AI-102 exam includes: 1. Experience with Azure Machine Learning and Azure Cognitive Services. 2. Knowledge of data science and machine learning concepts, including supervised and unsupervised learning, feature engineering, and model evaluation. 3. Familiarity with programming languages such as Python and R. 4. Understanding of natural language processing (NLP) and computer vision (CV) concepts. 5. Experience with deploying and managing machine learning models in production environments. 6. Knowledge of Azure services such as Azure Functions, Azure Logic Apps, and Azure Event Grid. 7. Familiarity with DevOps practices and tools for machine learning, such as Azure DevOps and GitHub. 8. Understanding of ethical and responsible AI practices, including fairness, transparency, and privacy. It is important to note that these are only recommendations, and the actual experience required may vary depending on the individual's background and expertise. |
Prerequisite | According to Microsoft's official website, the prerequisites for the AI-102 exam are: 1. A fundamental understanding of Azure services, including Azure Cognitive Services, Azure Machine Learning, and Azure Bot Service. 2. Experience in developing solutions that use Azure services. 3. Knowledge of Python programming language and machine learning concepts. 4. Familiarity with data science and data engineering concepts. 5. Understanding of DevOps practices and principles. It is recommended that candidates have at least one year of experience in developing AI solutions using Azure services before taking the AI-102 exam. |
Retirement (If Applicable) | Microsoft usually provides advance notice before retiring any exam, and candidates are encouraged to check the Microsoft certification website for updates on exam retirements. |
Certification Track (RoadMap): | The Microsoft AI-102 exam is a certification exam that focuses on designing and implementing AI solutions using Microsoft Azure technologies. The certification track/roadmap for the AI-102 exam includes the following steps: 1. Learn the basics of AI and machine learning concepts. 2. Gain knowledge of Azure services and tools for AI development. 3. Develop skills in designing and implementing AI solutions using Azure services. 4. Prepare for the AI-102 exam by studying the exam objectives and taking practice tests. 5. Pass the AI-102 exam to earn the Microsoft Certified: Azure AI Engineer Associate certification. 6. Continue learning and staying up-to-date with the latest AI technologies and trends to maintain the certification. |
Official Information | https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102 |
See Expected Questions | Microsoft AI-102 Expected Questions in Actual Exam |
Take Self-Assessment | Use Microsoft AI-102 Practice Test to Assess your preparation - Save Time and Reduce Chances of Failure |
Section | Weight | Objectives |
---|---|---|
Plan and Manage an Azure Cognitive Services Solution | 15-20% | Select the appropriate Cognitive Services resource
|
Implement Computer Vision Solutions | 20-25% | Analyze images by using the Computer Vision API
|
Implement Natural Language Processing Solutions | 20-25% | Analyze text by using the Text Analytics service
Manage a LUIS model
|
Implement Knowledge Mining Solutions | 15-20% | Implement a Cognitive Search solution
|
Implement Conversational AI Solutions | 15-20% | Create a knowledge base by using QnA Maker
Integrate Cognitive Services into a bot
|