AI-900 Exam Prep Free – 50 Practice Questions to Get You Ready for Exam Day
Getting ready for the AI-900 certification? Our AI-900 Exam Prep Free resource includes 50 exam-style questions designed to help you practice effectively and feel confident on test day
Effective AI-900 exam prep free is the key to success. With our free practice questions, you can:
Get familiar with exam format and question style
Identify which topics you’ve mastered—and which need more review
Boost your confidence and reduce exam anxiety
Below, you will find 50 realistic AI-900 Exam Prep Free questions that cover key exam topics. These questions are designed to reflect the structure and challenge level of the actual exam, making them perfect for your study routine.
You need to predict the sea level in meters for the next 10 years.
Which type of machine learning should you use?
A. classification
B. regression
C. clustering
Suggested Answer: B
In the most basic sense, regression refers to prediction of a numeric target.
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression
DRAG DROP -
Match the types of machine learning to the appropriate scenarios.
To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Select and Place:
Suggested Answer:
Box 1: Regression –
In the most basic sense, regression refers to prediction of a numeric target.
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
Box 2: Clustering –
Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation.
Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment.
Box 3: Classification –
Two-class classification provides the answer to simple two-choice questions such as Yes/No or True/False.
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression
DRAG DROP
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Match the services to the appropriate descriptions.
To answer, drag the appropriate service from the column on the left to its description on the right. Each service may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
HOTSPOT -
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:
HOTSPOT -
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:
Suggested Answer:
Box 1: Yes –
Azure bot service can be integrated with the powerful AI capabilities with Azure Cognitive Services.
Box 2: Yes –
Azure bot service engages with customers in a conversational manner.
Box 3: No –
The QnA Maker service creates knowledge base, not question and answers sets.
Note: You can use the QnA Maker service and a knowledge base to add question-and-answer support to your bot. When you create your knowledge base, you seed it with questions and answers.
Reference: https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-tutorial-add-qna
You use natural language processing to process text from a Microsoft news story.
You receive the output shown in the following exhibit.
Which type of natural languages processing was performed?
You are building a Language Understanding model for an e-commerce business.
You need to ensure that the model detects when utterances are outside the intended scope of the model.
What should you do?
HOTSPOT -
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:
HOTSPOT -
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:
Suggested Answer:
Box 1: Yes –
Content Moderator is part of Microsoft Cognitive Services allowing businesses to use machine assisted moderation of text, images, and videos that augment human review.
The text moderation capability now includes a new machine-learning based text classification feature which uses a trained model to identify possible abusive, derogatory or discriminatory language such as slang, abbreviated words, offensive, and intentionally misspelled words for review.
Box 2: No –
Azure’s Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual features you’re interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.
Box 3: Yes –
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Reference: https://azure.microsoft.com/es-es/blog/machine-assisted-text-classification-on-content-moderator-public-preview/ https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
HOTSPOT -
Select the answer that correctly completes the sentence.
Hot Area:
Suggested Answer:
Fairness is a core ethical principle that all humans aim to understand and apply. This principle is even more important when AI systems are being developed. Key checks and balances need to make sure that the system’s decisions don’t discriminate or run a gender, race, sexual orientation, or religion bias toward a group or individual.
Reference: https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
You are developing a chatbot solution in Azure.
Which service should you use to determine a user's intent?
A. Translator
B. QnA Maker
C. Speech
D. Language Understanding (LUIS)
Suggested Answer: D
Language Understanding (LUIS) is a cloud-based API service that applies custom machine-learning intelligence to a user’s conversational, natural language text to predict overall meaning, and pull out relevant, detailed information.
Design your LUIS model with categories of user intentions called intents. Each intent needs examples of user utterances. Each utterance can provide data that needs to be extracted with machine-learning entities.
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/luis/what-is-luis
DRAG DROP -
You need to scan the news for articles about your customers and alert employees when there is a negative article. Positive articles must be added to a press book.
Which natural language processing tasks should you use to complete the process? To answer, drag the appropriate tasks to the correct locations. Each task may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Select and Place:
Suggested Answer:
Box 1: Entity recognition –
the Named Entity Recognition module in Machine Learning Studio (classic), to identify the names of things, such as people, companies, or locations in a column of text.
Named entity recognition is an important area of research in machine learning and natural language processing (NLP), because it can be used to answer many real-world questions, such as:
✑ Which companies were mentioned in a news article?
✑ Does a tweet contain the name of a person? Does the tweet also provide his current location?
✑ Were specified products mentioned in complaints or reviews?
Box 2: Sentiment Analysis –
The Text Analytics API’s Sentiment Analysis feature provides two ways for detecting positive and negative sentiment. If you send a Sentiment Analysis request, the API will return sentiment labels (such as “negative”, “neutral” and “positive”) and confidence scores at the sentence and document-level.
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/named-entity-recognition https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-sentiment-analysis
HOTSPOT -
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:
Suggested Answer:
Box 1: Yes –
Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.
Box 2: No –
Box 3: Yes –
During training, Azure Machine Learning creates a number of pipelines in parallel that try different algorithms and parameters for you. The service iterates through
ML algorithms paired with feature selections, where each iteration produces a model with a training score. The higher the score, the better the model is considered to “fit” your data. It will stop once it hits the exit criteria defined in the experiment.
Box 4: No –
Apply automated ML when you want Azure Machine Learning to train and tune a model for you using the target metric you specify.
The label is the column you want to predict.
Reference: https://azure.microsoft.com/en-us/services/machine-learning/automatedml/#features
DRAG DROP
-
Match the Azure Cognitive Services to the appropriate AI workloads.
To answer, drag the appropriate service from the column on the left to its workload on the right. Each service may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
You are processing photos of runners in a race.
You need to read the numbers on the runners' shirts to identity the runners in the photos.
Which type of computer vision should you use?
Which AI service can you use to interpret the meaning of a user input such as `Call me back later?`
A. Translator
B. Text Analytics
C. Speech
D. Language Understanding (LUIS)
Suggested Answer: D
Language Understanding (LUIS) is a cloud-based AI service, that applies custom machine-learning intelligence to a user’s conversational, natural language text to predict overall meaning, and pull out relevant, detailed information.
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/luis/what-is-luis
HOTSPOT
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For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
HOTSPOT
-
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
HOTSPOT -
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:
Which service should you use to extract text, key/value pairs, and table data automatically from scanned documents?
A. Form Recognizer
B. Text Analytics
C. Language Understanding
D. Custom Vision
Suggested Answer: A
Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/ value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud. Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.
Reference: https://azure.microsoft.com/en-us/services/cognitive-services/form-recognizer/
DRAG DROP -
Match the types of computer vision workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Select and Place:
Suggested Answer:
Box 1: Facial recognition –
Face detection that perceives faces and attributes in an image; person identification that matches an individual in your private repository of up to 1 million people; perceived emotion recognition that detects a range of facial expressions like happiness, contempt, neutrality, and fear; and recognition and grouping of similar faces in images.
Box 2: OCR –
Box 3: Objection detection –
Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.
The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like “indoor”, which can’t be localized with bounding boxes.
Reference: https://azure.microsoft.com/en-us/services/cognitive-services/face/ https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
In which two scenarios can you use the Form Recognizer service? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Which two components can you drag onto a canvas in Azure Machine Learning designer? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
HOTSPOT
-
You have an app that identifies birds in images. The app performs the following tasks:
• Identifies the location of the birds in the image
• Identifies the species of the birds in the image
Which type of computer vision does each task use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
What are two metrics that you can use to evaluate a regression model? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. coefficient of determination (R2)
B. F1 score
C. root mean squared error (RMSE)
D. area under curve (AUC)
E. balanced accuracy
Suggested Answer: AC
A: R-squared (R2), or Coefficient of determination represents the predictive power of the model as a value between -inf and 1.00. 1.00 means there is a perfect fit, and the fit can be arbitrarily poor so the scores can be negative.
C: RMS-loss or Root Mean Squared Error (RMSE) (also called Root Mean Square Deviation, RMSD), measures the difference between values predicted by a model and the values observed from the environment that is being modeled.
Incorrect Answers:
B: F1 score also known as balanced F-score or F-measure is used to evaluate a classification model.
D: aucROC or area under the curve (AUC) is used to evaluate a classification model.
Reference: https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/metrics
Your website has a chatbot to assist customers.
You need to detect when a customer is upset based on what the customer types in the chatbot.
Which type of AI workload should you use?
You have a knowledge base of frequently asked questions (FAQ).
You create a bot that uses the knowledge base to respond to customer requests.
You need to identify what the bot can perform without adding additional skills.
What should you identify?
A. Register customer purchases.
B. Register customer complaints.
C. Answer questions from multiple users simultaneously.
D. Provide customers with return materials authorization (RMA) numbers.
Which two scenarios are examples of a natural language processing workload? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. monitoring the temperature of machinery to turn on a fan when the temperature reaches a specific threshold
B. a smart device in the home that responds to questions such as, “What will the weather be like today?”
C. a website that uses a knowledge base to interactively respond to users’ questions
D. assembly line machinery that autonomously inserts headlamps into cars
You need to determine the location of cars in an image so that you can estimate the distance between the cars.
Which type of computer vision should you use?
A. optical character recognition (OCR)
B. object detection
C. image classification
D. face detection
Suggested Answer: B
Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.
The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like “indoor”, which can’t be localized with bounding boxes.
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
You need to make the written press releases of your company available in a range of languages.
Which service should you use?
A. Speech
B. Language
C. Translator
D. Personalizer
Suggested Answer: C
Translator, an AI service for real-time document and text translation.
Translate text instantly or in batches across more than 100 languages, powered by the latest innovations in machine translation. Support a wide range of use cases, such as translation for call centers, multilingual conversational agents, or in-app communication.
Reference: https://azure.microsoft.com/en-us/services/cognitive-services/translator/4
HOTSPOT -
Select the answer that correctly completes the sentence.
Hot Area:
Suggested Answer:
Handwriting OCR (optical character recognition) is the process of automatically extracting handwritten information from paper, scans and other low-quality digital documents.
Reference: https://vidado.ai/handwriting-ocr
HOTSPOT -
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:
Suggested Answer:
Box 1: Yes –
Azure Cognitive Service for Language provides features including:
* Language detection: This pre-configured feature evaluates text, and determines the language it was written in. It returns a language identifier and a score that indicates the strength of the analysis.
Box 2: No –
Handwritten detection is part of OCR (Optical Character Recognition).
Box 3: Yes –
Azure Cognitive Service for Language provides features including:
* Named Entity Recognition (NER): This pre-configured feature identifies entities in text across several pre-defined categories.
Note: Named entity recognition is a natural language processing technique that can automatically scan entire articles and pull out some fundamental entities in a text and classify them into predefined categories. Entities may be,
Organizations,
Quantities,
Monetary values,
Percentages, and more.
People’s names –
Company names –
Geographic locations (Both physical and political)
Product names –
Dates and times –
Amounts of money –
Names of events –
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/language-service/overview
You have a webchat bot that provides responses from a QnA Maker knowledge base.
You need to ensure that the bot uses user feedback to improve the relevance of the responses over time.
What should you use?
You need to convert receipts into transactions in a spreadsheet. The spreadsheet must include the date of the transaction, the merchant, the total spent, and any taxes paid.
Which Azure AI service should you use?
HOTSPOT -
To complete the sentence, select the appropriate option in the answer area.
Hot Area:
Suggested Answer:
Reliability and safety: To build trust, it’s critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions.
These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation.
Reference: https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
You have an AI solution that provides users with the ability to control smart devices by using verbal commands.
Which two types of natural language processing (NLP) workloads does the solution use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. text-to-speech
B. key phrase extraction
C. speech-to-text
D. language modeling
E. translation
Suggested Answer: BC
Key phrase extraction is one of the features offered by Azure Cognitive Service for Language, a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. Use key phrase extraction to quickly identify the main concepts in text. For example, in the text
“The food was delicious and the staff were wonderful.”, key phrase extraction will return the main topics: “food” and “wonderful staff”.
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/language-service/key-phrase-extraction/overview
You have insurance claim reports that are stored as text.
You need to extract key terms from the reports to generate summaries.
Which type of AI workload should you use?
You have insurance claim reports that are stored as text.
You need to extract key terms from the reports to generate summaries.
Which type of AI workload should you use?
A. anomaly detection
B. natural language processing
C. computer vision
D. knowledge mining
Suggested Answer: B
Key phrase extraction is one of the features offered by Azure Cognitive Service for Language, a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. Use key phrase extraction to quickly identify the main concepts in text. For example, in the text
“The food was delicious and the staff were wonderful.”, key phrase extraction will return the main topics: “food” and “wonderful staff”.
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/language-service/key-phrase-extraction/overview
DRAG DROP -
Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Select and Place:
DRAG DROP -
Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Select and Place:
Which type of natural languages processing was performed?
