Online Access Free AI-900 Exam Questions

When you design an AI system to assess whether loans should be approved, the factors used to make the decision should be explainable.
This is an example of which Microsoft guiding principle for responsible AI?

• A.privacy and security
• B.inclusiveness
• C.fairness
• D.transparency

You have the following apps:
* App1: Understands the public perception of a brand or topic
* App2: Applies profanity filters to speech-to-text
What does each app use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:

App1: "Understands the public perception of a brand or topic" # Sentiment analysis According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn's Natural Language Processing (NLP) documentation, Sentiment analysis is a feature of the Azure AI Language Service that determines the emotional tone or attitude expressed in text. It classifies text as positive, negative, neutral, or mixed, which makes it ideal for analyzing customer opinions, brand perception, or product feedback.
For example, an organization can use sentiment analysis to process customer reviews or social media posts to determine how people feel about a particular brand or topic. This insight helps companies assess customer satisfaction, public perception, and marketing impact.
App2: "Applies profanity filters to speech-to-text" # Language detection The task of applying profanity filters occurs during or after speech-to-text transcription, which involves identifying the language used so that the correct filter can be applied. Language detection is an NLP feature that determines which language is being spoken or written. Once the language is detected, appropriate profanity filtering rules are automatically applied to remove or mask offensive words from transcribed text.
Other options such as Captioning or Named Entity Recognition (NER) are not relevant:
* Captioning describes images or videos, not speech filtering.
* NER identifies people, locations, or organizations but does not handle profanity or language detection.
Therefore, based on Azure AI NLP features:
* App1 uses Sentiment analysis
* App2 uses Language detection

You need to provide content for a business chatbot that will help answer simple user queries.
What are three ways to create question and answer text by using QnA Maker? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

• A.Manually enter the questions and answers.
• B.Import chit-chat content from a predefined data source.
• C.Generate the questions and answers from an existing webpage.
• D.Connect the bot to the Cortana channel and ask questions by using Cortana.
• E.Use automated machine learning to train a model based on a file that contains the questions.

Select the answer that correctly completes the sentence.

Correct Answer:

Explanation:

During model training, a portion of the dataset (commonly 70-80%) is used to teach the machine learning algorithm to identify patterns and relationships between input features and the output label. The remaining data (usually 20-30%) is held back to evaluate the model's performance and verify its accuracy on unseen data. This ensures the model is not overfitted (too tightly fitted to training data) and can generalize well to new inputs.
Key steps highlighted in Microsoft Learn materials:
* Model Training: Use the training data to fit the model - the algorithm learns relationships between input features and labels.
* Model Evaluation: Use the test or validation data to assess the accuracy, precision, recall, or other metrics of the trained model.
* Model Deployment: Once validated, the model is deployed to make real-world predictions.
Other options explained:
* Feature engineering: Involves preparing and transforming input data, not splitting datasets for training and testing.
* Time constraints: Not a machine learning process step.
* Feature stripping: Not a recognized ML concept.
* MLflow models: Refers to an open-source tool for tracking and managing models, not dataset splitting or training.
Thus, when you use a portion of the dataset to prepare and train a machine learning model, and retain the rest to verify results, the process is known as model training.

Match the Azure Cognitive Services to the appropriate Al 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.

Correct Answer:

Explanation:

The correct matches are Custom Vision, Form Recognizer, and Face - each corresponding to a distinct capability under Azure Cognitive Services as described in the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn modules on Computer Vision workloads.
* Custom Vision # Identify objects in an imageThe Custom Vision service is part of the Azure Cognitive Services suite that enables developers to train custom image classification and object detection models.
Unlike the prebuilt Computer Vision API, Custom Vision allows users to upload their own labeled images and teach the model to recognize specific objects relevant to their business context. The AI-900 syllabus explains that Custom Vision is ideal for tasks such as identifying products on a shelf, categorizing images, or detecting defects in manufacturing.
* Form Recognizer # Automatically import data from an invoice to a databaseForm Recognizer is a document processing AI service that extracts structured data from forms, receipts, and invoices. It uses optical character recognition (OCR) combined with layout and key-value pair detection to automatically capture information such as invoice numbers, amounts, and vendor names. The AI-900 study materials highlight this service under the Document Intelligence category, emphasizing its ability to streamline data entry and business automation workflows by importing extracted data directly into databases or applications.
* Face # Identify people in an imageThe Face service provides advanced facial detection and recognition capabilities. It can locate faces in images, compare similarities between faces, identify known individuals, and even detect facial attributes such as age or emotion. The AI-900 course classifies this under Computer Vision services for person identification and security-related use cases such as access control or identity verification.
Thus, each mapping aligns precisely with the AI-900 official learning outcomes on Cognitive Services capabilities:
* Custom Vision # Object recognition
* Form Recognizer # Data extraction from forms
* Face # People identification
# Final verified configuration:
* Custom Vision # Identify objects in an image
* Form Recognizer # Automatically import data from an invoice to a database
* Face # Identify people in an image