Microsoft Azure AI-102 Azure AI Solution Practice Tests 2021

Microsoft Azure AI-102 Azure AI Solution Practice Tests 2021

AI-102 Practice Tests | ✅ Updated Jun 2021 ✅ | 4 Practice Tests | 163 Questions ✅ | Detailed Explanation with Refer URLs

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This practice test course contains 4 complete timed practice tests. Each test contains 40 questions, that’s 160+ unique questions to test how well prepared you are for the real exam. These tests also has case studies.

This practice test course is designed to cover every topic, with a difficulty level like a real exam.

Every question has a detailed answer with the links back to the official Microsoft docs.

This course offers the following features:

  • The practice tests are constructed to enhance your confidence to sit for real exam as you will be testing your knowledge and skills for the above-mentioned topics.

  • The practice tests are accurate, proven correct by test-takers and will get you maximum score in the exam yet it is highly important to use our practice tests as a resource, not just the only resource.

  • Prepared by an Microsoft Certified : Designing and Implementing an Azure AI Solution who has actually passed the actual AI-102 exam

  • Answer keys at the end of each set have full and detailed explanations along with complete reference links so you can check and verify yourself that the answers are correct

  • Upon enrollment, you will receive unlimited access to the tests as well as the regular updates.

Candidates for this exam should have subject matter expertise using cognitive services, machine learning, and knowledge mining to architect and implement Microsoft AI solutions involving natural language processing, speech, computer vision, and conversational AI.

Responsibilities for an Azure AI Engineer include analyzing requirements for AI solutions, recommending the appropriate tools and technologies, and designing and implementing AI solutions that meet scalability and performance requirements.

Azure AI Engineers translate the vision from solution architects and work with data scientists, data engineers, IoT specialists, and software developers to build complete end-to-end solutions.

A candidate for this exam should have knowledge and experience designing and implementing AI apps and agents that use Microsoft Azure Cognitive Services, Azure Bot Service, Azure Cognitive Search, and data storage in Azure. In addition, a candidate should be able to recommend solutions that use open source technologies, understand the components that make up the Azure AI portfolio and the available data storage options, and understand when a custom API should be developed to meet specific requirements.


  • 160 Plus PRACTICE QUESTIONS: 4 sets of Practice Exams  and 3 case study available on Udemy to assess your exam readiness.

  • EXAM SIMULATION: All Practice Tests are timed and scored (passing score is 70%) mimicking the real exam environment

  • DETAILED EXPLANATIONS: Every question includes a detailed explanation that explains why each answer is correct or incorrect

  • PREMIUM-QUALITY: These practice questions are free from typos and technical errors which makes your learning experience much more pleasant

  • ALWAYS UP TO DATE: Our question bank is constantly updated based on student feedback from the real exam. New questions are added on a regular basis growing our pool of questions

  • ACTIVE Q&A FORUM: In this discussion board, students ask questions and share their recent exam experience offering feedback on which topics were covered.

  • RESPONSIVE SUPPORT: Our team of Azure experts respond to all of your questions, concerns or feedback.

  • Each question has detailed explanations at the end of each set that will help you gain a deeper understanding of the Azure services.

  • MOBILE-COMPATIBLE – so you can conveniently review everywhere, anytime with your smartphone!

  • Plus a 30 DAY MONEY BACK GUARANTEE if you’re not satisfied for any reason.

Question type : Multiple choice, Drag and drop question, Yes/no question

Total Questions: 40-60 Questions

Exam Time: 180 Minutes(3 hrs)

Passing Score: 700 out of 1000

Free Retake : No

Before exam: Practice this test until you score 100%, so that you will be confident in the official exam.

After exam: Once you are cleared the exam, digital badge and certification will be available in the Microsoft Certification Dashboard.

Skills Measured:

NOTE: The bullets that appear below each of the skills measured are intended to illustrate how

we are assessing that skill. This list is not definitive or exhaustive.

Skills Measured NOTE:

The bullets that appear below each of the skills measured are intended to illustrate how we are assessing that skill. This list is not definitive or exhaustive.

NOTE: In most cases, exams do NOT cover preview features, and some features will only be added to an exam when they are GA (General Availability).

Analyze solution requirements (25-30%)

Recommend Azure Cognitive Services APIs to meet business requirements

  • select the processing architecture for a solution

  • select the appropriate data processing technologies

  • select the appropriate AI models and services

  • identify components and technologies required to connect service endpoints

  • identify automation requirements

Map security requirements to tools, technologies, and processes

  • identify processes and regulations needed to conform with data privacy, protection, and regulatory requirements

  • identify which users and groups have access to information and interfaces

  • identify appropriate tools for a solution

  • identify auditing requirements

Select the software, services, and storage required to support a solution

  • identify appropriate services and tools for a solution 

  • identify integration points with other Microsoft services

  • identify storage required to store logging, bot state data, and Azure Cognitive Services output

Design AI solutions (40-45%)

Design solutions that include one or more pipelines

  • define an AI application workflow process

  • design a strategy for ingest and egress data

  • design the integration point between multiple workflows and pipelines

  • design pipelines that use AI apps

  • design pipelines that call Azure Machine Learning models

  • select an AI solution that meet cost constraints

Design solutions that uses Cognitive Services

  • design solutions that use vision, speech, language, knowledge, search, and anomaly detection APIs

Design solutions that implement the Microsoft Bot Framework

  • integrate bots and AI solutions

  • design bot services that use Language Understanding (LUIS)

  • design bots that integrate with channels

  • integrate bots with Azure app services and Azure Application Insights

Design the compute infrastructure to support a solution

  • identify whether to create a GPU, FPGA, or CPU-based solution

  • identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure

  • select a compute solution that meets cost constraints

Design for data governance, compliance, integrity, and security

  • define how users and applications will authenticate to AI services

  • design a content moderation strategy for data usage within an AI solution

  • ensure that data adheres to compliance requirements defined by your organization 

  • ensure appropriate governance of data

  • design strategies to ensure that the solution meets data privacy regulations and industry standards

Implement and monitor AI solutions (25-30%)

Implement an AI workflow

  • develop AI pipelines

  • manage the flow of data through the solution components

  • implement data logging processes

  • define and construct interfaces for custom AI services

  • create solution endpoints

  • develop streaming solutions

Integrate AI services and solution components

  • configure prerequisite components and input datasets to allow the consumption of Azure Cognitive Services APIs

  • configure integration with Azure Cognitive Services

  • configure prerequisite components to allow connectivity to the Microsoft Bot Framework

  • implement Azure Cognitive Search in a solution

Monitor and evaluate the AI environment

  • identify the differences between KPIs, reported metrics, and root causes of the differences

  • identify the differences between expected and actual workflow throughput 

  • maintain an AI solution for continuous improvement

  • monitor AI components for availability

  • recommend changes to an AI solution based on performance data

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