Free Microsoft AI-900 Exam Dumps Questions & Answers

Select the answer that correctly completes the sentence.

Correct Answer:

Explanation:
Clustering.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of common machine learning types", clustering is an unsupervised machine learning technique used to group data points into distinct segments or clusters based on shared characteristics.
Unlike supervised learning (classification or regression), clustering works with unlabeled data, discovering natural groupings without predefined outcomes.
In this question, Recency, Frequency, and Monetary (RFM) values are common marketing metrics used to evaluate customer behavior:
* Recency - how recently a customer made a purchase.
* Frequency - how often they make purchases.
* Monetary - how much money they spend.
Using RFM analysis, a company can segment its customers into groups such as "loyal," "occasional," or "at- risk" buyers. This segmentation process does not rely on predefined labels but rather discovers patterns within the data - which is the defining characteristic of clustering.
In the AI-900 context, clustering is described as a method that "groups items with similar features so that items in the same group are more similar to each other than to those in other groups." Common algorithms used include K-Means, Hierarchical Clustering, and DBSCAN, all available within Azure Machine Learning Designer and other Azure ML environments.
To clarify the incorrect options:
* Classification is supervised learning used to predict discrete categories (e.g., yes/no, spam/not spam).
* Regression predicts continuous numeric values (e.g., house prices).
* Regularization is a model optimization technique, not a type of machine learning.
Therefore, when businesses use RFM values to identify customer segments without labeled outcomes, this is an application of unsupervised learning through clustering.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:

The Translator service, part of Microsoft Azure Cognitive Services, is designed specifically for text translation between multiple languages. It is a cloud-based neural machine translation service that supports more than 100 languages. According to Microsoft Learn's module "Translate text with the Translator service", this service provides two main capabilities: text translation and automatic language detection.
* "You can use the Translator service to translate text between languages." # YesThis statement is true.
The primary purpose of the Translator service is to translate text accurately and efficiently between supported languages, such as English to Spanish or French to Japanese. It maintains contextual meaning using neural machine translation models.
* "You can use the Translator service to detect the language of a given text." # YesThis statement is also true. The Translator service includes automatic language detection, which determines the source language before translation. For instance, if a user submits text in an unknown language, the service can identify it automatically before performing translation.
* "You can use the Translator service to transcribe audible speech into text." # NoThis statement is false.
Transcribing speech (audio) into text is a function of the Azure Speech service, specifically the Speech- to-Text API, not the Translator service.
Therefore, the Translator service is used for text translation and language detection, while speech transcription belongs to the Speech service.

You are building an Al-based loan approval app.
You need to ensure that the app documents why a loan is approved or rejected and makes the report available to the applicant.
This is an example of which Microsoft responsible Al principle?

• A.  accountability
• B.  transparency
• C.  inclusiveness
• D.  fairness

Which Azure OpenAI model should you use to summarize the text from a document?

• A.  GPT
• B.  Codex
• C.  Whisper
• D.  DALL-E

You have the following apps:
* App1: Uses a set of images of tumors to identify whether the tumors are benign or malignant and suggest a treatment
* App2: Uses images from cameras to track individual livestock as they move around a farm
* App3: Identifies brands in photographs of billboards
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:

Let's analyze each application in the context of Microsoft Azure AI Fundamentals (AI-900) and computer vision model types.
* App1 - Uses a set of images of tumors to identify whether the tumors are benign or malignant and suggest a treatment # Image classificationThis application is performing image classification, where each image (of a tumor) is assigned to a single predefined category - benign or malignant. Image classification models learn patterns from labeled training images and predict the correct class for new ones. In this case, the model identifies the type of tumor, a classic binary classification scenario.
* App2 - Uses images from cameras to track individual livestock as they move around a farm # Object detectionThis scenario describes object detection, which not only identifies what objects (in this case, animals) are in an image but also locates them by drawing bounding boxes. Tracking movement requires detecting the position of each animal frame by frame. Object detection models are well-suited for use cases involving counting, tracking, or monitoring objects in a visual scene.
* App3 - Identifies brands in photographs of billboards # Optical character recognition (OCR)This app involves reading and interpreting text (brand names, slogans, or logos) from images of billboards.
Optical Character Recognition (OCR), part of Azure AI Vision, extracts textual information from images or scanned documents. Once extracted, that text can be analyzed to identify brand names or keywords.
Summary:
* App1 # Image classification
* App2 # Object detection
* App3 # Optical character recognition (OCR)