Microsoft AI-102 Practice Test Questions, Microsoft AI-102 Exam Dumps
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The Microsoft AI-102 exam can only be taken in English and contains about 40-60 questions. Various question formats that may be administered in any delivery of the certification test include active screen, multiple choice, build list, drag and drop, hot areas, and fill-in-the-blank. The required fee for passing the exam is $165, which applies to one delivery of the test. The candidates who do not achieve the passing score of 700 points will be required to retake the exam to qualify for the certification. To avoid this process, the applicants must adequately prepare for the test. They can find various resources to prepare for the Microsoft AI-102 test on the official website.
Microsoft offers both the instructor-led training course and free learning paths to equip the students with the skills and knowledge required for success in the exam. The skills gathered in the course will also improve their performance in the work environment. The recommended training option for the AI-102 test is Designing and Implementing a Microsoft Azure AI Solution. It provides the individuals with the skills in the development of AI-infused applications. They will also develop more competence in the use of the Python or C# programming language while going through the course. Additionally, the vendor offers six free learning paths that cover different domains of the exam syllabus. You can also use the official practice test to evaluate your readiness for the actual exam.
Your understanding of the exam topics is critical to a good performance in the certification test. The candidates must review the exam content and gain a thorough insight of different areas covered in it before they attempt the test. The details of the Microsoft AI-102 exam domains are enumerated as follows:
Planning & Management of Azure Cognitive Service Solutions: 15-20%
- Choose the relevant resource for Cognitive Services: It covers the ability to choose the relevant cognitive services for vision solutions, decision support solutions, language, analysis solutions, and speech solutions.
- Map and construct security for Cognitive Services solutions: It requires one’s competence in managing account keys for Cognitive Services and managing authentication for resources. The candidates should also have the ability to plan for the solutions that fulfill responsible artificial intelligence principles and competence in securing Cognitive Services through the use of Azure Virtual Network.
- Create a resource for Cognitive Services: It includes your skills in monitoring cognitive services, managing Cognitive Services costs, and implementing privacy policies within Cognitive Services, among others.
- Plan and implement containers for Cognitive Services: The test takers should have the skills in containerizing Cognitive Services and establishing the appropriate time to deploy to the container.
Implementation of Computer Vision Solutions: 20-25%
- Evaluate images with the use of Computer Vision APIs: The skills to be measured in this domain include the ability to recover image tags and description through Computer Vision API and moderate content within images with the use of Computer Vision API.
- Pull out text from an image: The examinees should be able to use OCR API and Read API to extract text from an image or PDF.
- Pull out facial information from an image: It requires the individuals’ competence in detecting faces within an image with the use of Face API and identifying faces in images with Face API, among others.
- Implement image classification with Custom Vision Service: The applicants need to possess the competence in labeling images with Computer Vision Portal, measuring classification model metrics, managing model iterations, and publishing trained iterations of models, among others.
- Analyze videos with Video Indexer: It requires your competence in processing videos, moderating content within videos, and extracting insights from videos, among others.
Implementation of Natural Language Processing Solution: 20-25%
- Evaluate text with a Text Analytics service: It covers the learners’ skills in retrieving & processing sentiment, entity information, and key phrases.
- Translate a language: The students should demonstrate their expertise in translating text using a Translator service as well as translating speech-to-text & speech-to-speech with a Speech service.
- Manage a speech with a Speech service: It includes the examinees’ competence in implementing and customizing text-to-speech as well as implementing & improving speech-to-text and its accuracy.
- Manage the LUIS model: The test takers should have the relevant skills in managing collaborators and versioning, exporting & deploying LUIS packages to containers, and publishing models in a container or through the portal.
Implementation of Knowledge Mining Solutions: 15-20%
- Implement Cognitive Search Solutions: It requires one’s competence in querying an index, creating data sources, implementing synonyms, creating & running indexers, defining an index, as well as boosting results according to relevance.
- Implement knowledge store: It covers the applicants’ skills in querying projections, defining table projections, object projections, and file projections.
- Manage indexing: This subtopic focuses on the expertise in managing re-indexing, scheduling and monitoring indexing, pushing data to indexes, rebuilding indexes, and implementing incremental indexing.
Implementation of Conversational AI Solutions: 15-20%
- Use QnA Maker to create the knowledge base: It includes creating the knowledge base and QnA Maker service; importing, training & testing, and publishing the knowledge base; exporting and adding active learning to the knowledge base.
- Design and implement a conversation flow: It requires your skills in designing and implementing adaptive cards and designing conversation logic for the bot.
- Use Bot Framework SDK to create a Bot: It requires the competence in maintaining state; implementing dialogs, logging for bot conversations, and prompts for user input; publishing a bot; managing identity & authentication, among others.
- Use Bot Framework Composer to create a Bot: It covers the students’ skills in implementing dialogs, troubleshooting a conversational Bot, and utilizing the Bot Framework Emulator to test a Bot.
- Incorporate Cognitive Services within a Bot: It requires the examinees’ skills in integrating a LUIS service, dispatch for multi-language models, QnA Maker service, and Speech service.
The candidates who get the Microsoft Certified: Azure AI Engineer Associate certification after passing the Microsoft AI-102 exam can build a career in the domain of software engineering. Some of the job roles they can explore include a Cloud Operations Engineer, a Senior Business Intelligence Engineer, a Cloud System Verification Engineer, a Data Engineer, a Software Engineer, a Technology Implementation Engineer, and a Data Scientist, among others. The median remuneration for these positions is $172,000 per annum.
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