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AI-900: Microsoft Azure AI Fundamentals
Sample Questions
Last updated: 1/19/
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User Guide
These sample questions are intended to provide an overview of the style, wording, and difficulty of the questions that you are likely to experience on this exam. These questions are not the same as what you will see on the exam nor is this document illustrative of the length of the exam or its complexity (e.g., you may see additional question types, multiple case studies, and possibly labs). These questions are examples only to provide insight into what to expect on the exam and help you determine if additional preparation is required.
In the first section, you will find the questions without answers so that you can test your knowledge. In the second section, the answer, a rationale, and a URL that will link you to additional information is provided immediately below each question.
Contents Questions-------------------------------------------------------------------------------------------------------------------------- 3
Question # 1 (Multiple Choice) -------------------------------------------------------------------------------------------- 3 Question # 2 (Matching)----------------------------------------------------------------------------------------------------- 3 Question # 3 (Sentence completion) ------------------------------------------------------------------------------------- 3 Question # 4 (Sentence completion) ------------------------------------------------------------------------------------- 3 Question # 5 (Multiple Choice) -------------------------------------------------------------------------------------------- 4 Question # 6 (Matching)----------------------------------------------------------------------------------------------------- 4 Question # 7 (Sentence completion) ------------------------------------------------------------------------------------- 4 Question # 8 (Multiple Choice) -------------------------------------------------------------------------------------------- 4 Question # 9 (Sentence completion) ------------------------------------------------------------------------------------- 5 Question # 10 (Multiple Choice)------------------------------------------------------------------------------------------- 5 Question # 11 (Multiple Choice)------------------------------------------------------------------------------------------- 5 Question # 12 (Sentence completion) ----------------------------------------------------------------------------------- 6 Question # 13 (Multiple Choice)------------------------------------------------------------------------------------------- 6 Question # 14 (Multiple Choice)------------------------------------------------------------------------------------------- 6 Question # 15 (Matching) --------------------------------------------------------------------------------------------------- 6
- Question # 16 (Multiple Choice)-------------------------------------------------------------------------------------------
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- Question # 18 (Multiple Choice)-------------------------------------------------------------------------------------------
- Question # 19 (Sentence completion) -----------------------------------------------------------------------------------
- Question # 20 (Multiple Choice)-------------------------------------------------------------------------------------------
- Questions and Answers ------------------------------------------------------------------------------------------------------
- Question # 1 (Multiple Choice) --------------------------------------------------------------------------------------------
- Question # 2 (Matching)-----------------------------------------------------------------------------------------------------
- Question # 3 (Sentence completion) ------------------------------------------------------------------------------------
- Question # 4 (Sentence completion) ------------------------------------------------------------------------------------
- Question # 5 (Multiple Choice) -------------------------------------------------------------------------------------------
- Question # 6 (Matching)----------------------------------------------------------------------------------------------------
- Question # 7 (Sentence completion) ------------------------------------------------------------------------------------
- Question # 8 (Multiple Choice) -------------------------------------------------------------------------------------------
- Question # 9 (Sentence completion) ------------------------------------------------------------------------------------
- Question # 10 (Multiple Choice)------------------------------------------------------------------------------------------
- Question # 11 (Multiple Choice)------------------------------------------------------------------------------------------
- Question # 12 (Sentence completion) ----------------------------------------------------------------------------------
- Question # 13 (Multiple Choice)------------------------------------------------------------------------------------------
- Question # 14 (Multiple Choice)------------------------------------------------------------------------------------------
- Question # 15 (Matching) --------------------------------------------------------------------------------------------------
- Question # 16 (Multiple Choice)------------------------------------------------------------------------------------------
- Question # 17 (Multiple Choice)------------------------------------------------------------------------------------------
- Question # 18 (Multiple Choice)------------------------------------------------------------------------------------------
- Question # 19 (Sentence completion) ----------------------------------------------------------------------------------
- Question # 20 (Multiple Choice)------------------------------------------------------------------------------------------
D. Accountability
Question # 5 (Multiple Choice)
You need to identify numerical values that represent the probability of dogs becoming ill based on their age and body fat percentage.
Which type of machine learning model should you use?
A. Linear regression B. Multiple linear regression C. Logistic regression D. Hierarchical clustering
Question # 6 (Matching)
Match the machine learning algorithms on the left to the correct descriptions on the right.
Machine learning algorithms
Descriptions
A. Clustering B. Regression C. Classification
_____ 1. Predict a numeric label based on an item’s features. _____ 2. Group similar items based on their features. _____ 3. Assign items into a set of predefined categories.
Question # 7 (Sentence completion)
Select the answer that correctly completes the sentence.
You plan to use machine learning to predict how ill dogs become based on their age and body fat percentage.
The model should include ___________.
A. two features and one label B. two labels and one feature C. three labels D. three features
Question # 8 (Multiple Choice)
You create a multiclass classification model.
You need to evaluate the model.
Which two evaluation metrics can you use? Each correct answer presents a complete solution.
A. F1 score B. Accuracy C. Rand index D. Mean Squared Error (MSE)
Question # 9 (Sentence completion)
Select the answer that correctly completes the sentence.
You train an Azure Machine Learning model and plan to deploy the model as a predictive service in a production environment.
You must create an inference cluster before you deploy the model to _______________.
A. Azure Kubernetes Service B. Azure Container Instance C. an Azure Function D. an Azure Logic Apps app
Question # 10 (Multiple Choice)
You plan to build and deploy a predictive model by using AutoML UI in Azure Machine Learning.
Which three machine learning tasks are supported? Each correct answer presents a complete solution.
A. Classification B. Regression C. Forecasting D. Clustering E. Reinforcement learning
Question # 11 (Multiple Choice)
Which technique serves as the basis for modern image classification solutions?
A. Deep learning B. Anomaly detection C. Linear regression D. Multiple linear regression
Question # 16 (Multiple Choice)
You need to collect the names of people, organizations, and events from a set of PDF documents.
Which natural language processing feature should you use?
A. Extractive summarization B. Sentiment analysis C. Named entity recognition D. Key phrase extraction
Question # 17 (Multiple Choice)
Which three capabilities does Azure Text Analytics service support? Each correct answer presents a complete solution.
A. Unlimited document size B. All world languages C. Chatbot integration D. Multilingual content E. Confidence scoring
Question # 18 (Multiple Choice)
You need to identify users based on their voice.
Which Azure Speech service feature should you use?
A. Conversation transcription
B. Pronunciation assessment
C. Language Understanding Intelligent Service (LUIS)
D. Speaker recognition
Question # 19 (Sentence completion)
Select the answer that correctly completes the sentence.
You can exchange chatbot activities with other services by implementing ________________.
A. cards B. channels C. dialog D. turns
Question # 20 (Multiple Choice)
You develop a chatbot by using QnA Maker.
You need to add a personality to the chatbot.
What should you do?
A. Provide a default answer. B. Add chit-chat to the knowledge base. C. Increase the Cognitive Search resource pricing tier limit. D. Add hero cards to the chatbot.
identify text in images. Image classification allows you to differentiate between different types of objects in images. URL: https://docs.microsoft.com/en-us/learn/modules/get- started-ai-fundamentals/4-understand-computer-vision
Question # 3 (Sentence completion)
Select the answer that correctly completes the sentence.
The principle that describes raising awareness of the limitations of responsible AI-based solutions is called: ________________.
A. Privacy and security B. Reliability and safety C. Transparency D. Accountability
Answer: C. Transparency Objective: 1.2 Identify guiding principles for responsible AI Rationale: Transparency provides clarity regarding the purpose of AI solutions, the way they work, as well as their limitations. Other principles of responsible AI are meant to apply to any AI solution, regardless of their limitations. URL: Identify principles and practices for responsible AI - Learn | Microsoft Docs
Question # 4 (Sentence completion)
Select the answer that correctly completes the sentence.
The principle of providing the benefits of responsible AI systems to all parts of society regardless of their gender or ethnicity is called: ____________.
A. Privacy and security B. Reliability and safety C. Inclusiveness D. Accountability
Answer: C. Inclusiveness Objective: 1.2 Identify guiding principles for responsible AI Rationale: Responsible AI systems should empower everyone and engage people. AI should bring benefits to all parts of society, regardless of physical ability, gender, sexual orientation, ethnicity, or other factors ensuring inclusiveness. URL: https://docs.microsoft.com/en-us/learn/modules/get-started-ai- fundamentals/8-understand-responsible-ai
Question # 5 (Multiple Choice)
You need to identify numerical values that represent the probability of dogs becoming ill based on their age and body fat percentage.
Which type of machine learning model should you use?
A. Linear regression B. Multiple linear regression C. Logistic regression D. Hierarchical clustering
Answer: B. Multiple linear regression Objective: 2.1 Identify common machine learning types Rationale: Modeling relationships between several features and a single label is the primary characteristic of multiple linear regression, in contrast with linear regression which uses a single feature. Logistic regression is a classification model and hierarchical clustering is a type of clustering algorithm. URL: https://docs.microsoft.com/en-us/learn/modules/understand-regression- machine-learning/4-multiple-linear-regression https://docs.microsoft.com/en-us/learn/modules/understand-classification- machine-learning/2-what-is-classification https://docs.microsoft.com/en-us/learn/modules/train-evaluate-cluster- models/4-different-types-clustering
Question # 6 (Matching)
Match the machine learning algorithms on the left to the correct descriptions on the right.
Machine learning algorithms
Descriptions
A. Clustering B. Regression C. Classification
_____ 1. Predict a numeric label based on an item’s features. _____ 2. Group similar items based on their features. _____ 3. Assign items into a set of predefined categories. Answer: Regression (B) matches description 1: Predict a numeric label based on an item’s features. Clustering (A) matches description 2: Group similar items based on their features. Classification (C) matches description 3: Assign items into a set of predefined categories. Objective: 2.1 Identify common machine learning types Rationale: The regression technique is used to predict numeric values. Classification predicts the category in which an input value should be categorized. Clustering groups data points that have similar characteristics.
Objective: 2.2 Describe core machine learning concepts Rationale: You can use the F1 score and accuracy metrics for evaluating classification models. The F1 score combines precision and recall for classification evaluation while accuracy evaluates the ratio of correct predictions. Rand index is used for evaluating clustering models. MSE is used for evaluating regression models. URL: https://docs.microsoft.com/en-us/learn/modules/create-classification-model- azure-machine-learning-designer/evaluate-model
Question # 9 (Sentence completion)
Select the answer that correctly completes the sentence.
You train an Azure Machine Learning model and plan to deploy the model as a predictive service in a production environment.
You must create an inference cluster before you deploy the model to _______________.
A. Azure Kubernetes Service B. Azure Container Instance C. an Azure Function D. an Azure Logic Apps app
Answer: A. Azure Kubernetes Service Objective: 2.3 Identify core tasks in creating a machine learning solution
Rationale: In Azure Machine Learning, you have the option of deploying a predictive service to Azure Container Instance (ACI) or Azure Kubernetes Service (AKS). For production scenarios, you should use an AKS deployment, which requires creating an inference cluster compute target. ACI-based deployment is suitable for testing. Azure Machine Learning does not support deployment of predictive services to Azure Functions or Azure Logic Apps. URL: https://docs.microsoft.com/en-us/learn/modules/use-automated-machine- learning/deploy-model
Question # 10 (Multiple Choice)
You plan to build and deploy a predictive model by using AutoML UI in Azure Machine Learning.
Which three machine learning tasks are supported? Each correct answer presents a complete solution.
A. Classification B. Regression
C. Forecasting D. Clustering E. Reinforcement learning
Answer: A. Classification AND B. Regression AND C. Forecasting Objective: 2.4 Describe capabilities of No-Code Machine Learning with Azure Machine Learning studio Rationale: AutoML UI supports classification, regression and forecasting machine learning tasks. Clustering and reinforcement learning are not available on AutoML UI. URL: https://docs.microsoft.com/en-us/learn/modules/use-automated-machine- learning/deploy-model
Question # 11 (Multiple Choice)
Which technique serves as the basis for modern image classification solutions?
A. Deep learning B. Anomaly detection C. Linear regression D. Multiple linear regression
Answer: A. Deep learning Objective: 3.1 Identify common types of computer vision solutions Rationale: Modern image classification solutions are based on deep learning techniques that make use of convolutional neural networks (CNNs) to identify patterns in the pixels that comprise an image and map it to a particular class. Anomaly detection is an Artificial Intelligence technique that detects unusual occurrences in data patterns. Both linear and multiple linear regression are regression techniques, rather than classifications. URL: https://docs.microsoft.com/en-us/learn/modules/classify-images-custom- vision/1a-overview-classification
Question # 12 (Sentence completion)
Select the answer that correctly completes the sentence.
You can extract information printed on food product labels by using _____________________.
A. image classification B. natural language processing C. optical character recognition
Rationale: Object detection functionality in Azure Custom Vision can identify logos in images. Image classification functionality in Azure Custom Vision is used for classifying a set of images to groups. Azure Face Service is specifically used for identifying faces. This service cannot identify logos. LUIS is used for understanding natural language. URL: Detect objects in images with the Custom Vision service - Learn | Microsoft Docs
Question # 15 (Matching)
Match the features on the left to the correct descriptions on the right.
Features Descriptions A. Entity linking B. Sentiment analysis C. Key phrase extraction D. Named entity recognition
_____ 1. Evaluate the main points from the text in a document. _____ 2. Determine whether the content of a document is positive or negative. _____ 3. Identify words in documents that represent persons, locations, or organizations. Answer: Key phrase extraction (C) matches description 1: Evaluate the main points from the text in a document. Sentiment analysis (B) matches description 2: Determine whether the content of a document is positive or negative. Named entity recognition (D) matches description 3: Identify words in documents that represent persons, locations, or organizations. Objective: 4.1 Identify features of common NLP Workload Scenarios Rationale: Key phrase extraction scans documents and identifies the main points from the documents. Sentiment analysis scans a document to determine whether the content is positive or negative. Named entity recognition scans documents and identified important entities (objects, nouns) in the document. URL: https://docs.microsoft.com/en-us/learn/modules/extract- insights-text-with-text-analytics-service/
Question # 16 (Multiple Choice)
You need to collect the names of people, organizations, and events from a set of PDF documents.
Which natural language processing feature should you use?
A. Extractive summarization B. Sentiment analysis C. Named entity recognition D. Key phrase extraction
Answer: C. Named entity recognition Objective: 4.1 Identify features of common NLP Workload Scenarios
Rationale: The named entity recognition feature in text analytics identifies a range of prebuilt entities such as people, places, and organizations which can be used for entity identification in PDF documents. URL: https://docs.microsoft.com/en-us/learn/modules/get-started-ai- fundamentals/5-understand-natural-language-process
Question # 17 (Multiple Choice)
Which three capabilities does Azure Text Analytics service support? Each correct answer presents a complete solution.
A. Unlimited document size B. All world languages C. Chatbot integration D. Multilingual content E. Confidence scoring
Answer: C. Chatbot integration AND D. Multilingual content AND E. Confidence scoring Objective: 4.2 Identify Azure tools and services for NLP workloads Rationale: Azure Text Analytics supports chatbot integration, multilingual content, and confidence scoring. It recognizes about 120 languages. Document sizes must be under 5,120 characters. URL: https://docs.microsoft.com/en-us/learn/modules/extract-insights-text-with- text-analytics-service/3-detect-language
Question # 18 (Multiple Choice)
You need to identify users based on their voice.
Which Azure Speech service feature should you use?
A. Conversation transcription
B. Pronunciation assessment
C. Language Understanding Intelligent Service (LUIS)
D. Add hero cards to the chatbot.
Answer: B. Add chit-chat to the knowledge base. Objective: 5.2 Identify Azure services for conversational AI
Rationale: You can add personality to a chatbot by providing answers that use a specific conversational tone. You use the chit-chat feature to add the answers to a chatbot knowledge base. Provide a default answer from settings is incorrect because it is used to provide a pre-set answer from a chatbot. Increasing Cognitive Search resource pricing tier limits only increases the number of concurrent requests that can connect to a chatbot. Hero cards are used for showing media inside a chatbot, not to add a personality. URL: Build a QnA solution with QnA Maker - Learn | Microsoft Docs
