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.
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.
KEY FEATURES OF THESE POPULAR PRACTICE EXAMS
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.
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