Free Microsoft Azure AI Fundamentals AI-900 Practice Test

Natural language processing is used to analyze and understand human language in text form. It enables the extraction of meaningful information from unstructured text data, such as identifying entities like dates, names, and locations. This is a process known as entity recognition.

Computer vision is related to image and video analysis.

Predictive analytics involves forecasting future trends from data.

Anomaly detection focuses on identifying outliers or unusual patterns in datasets.

Entity Recognition is the Azure AI feature that identifies and classifies named entities in text, such as names of people, organizations, locations, and dates. This allows unstructured text data to be converted into structured information that can be analyzed and used for insights.

Sentiment Analysis determines the emotional tone (positive, negative, neutral) expressed in text but does not extract specific entities.

Key Phrase Extraction identifies the main points or key phrases in text but does not categorize them into entity types.

Language Modeling involves understanding and generating human language but is not used for extracting and classifying entities from text.

Clustering is the most appropriate technique for discovering inherent groupings or patterns in data without predefined labels. In this scenario, the retailer aims to segment customers based on similarities to tailor marketing strategies for each group.

Classification involves assigning data points to predefined categories using labeled data, which is not suitable here since the categories are not known beforehand.

Regression is used for predicting continuous numerical values, not for identifying patterns or groupings.

Time Series Analysis focuses on data points collected over time, which does not align with the goal of understanding customer behavior patterns in this context.

Optical Character Recognition (OCR) is the appropriate solution for extracting text from images or scanned documents. OCR technology converts images of text into machine-encoded text, enabling the company to automate data entry from documents.

Image Classification categorizes images based on their overall content but does not extract textual information.

Object Detection identifies and locates objects within an image but does not interpret text.

Facial Analysis focuses on detecting and analyzing human facial features, which is not relevant to extracting text from documents.

Computer vision involves processing and analyzing visual information such as images and videos. For a task that requires analyzing images to identify defects, computer vision is the appropriate AI workload. Natural language processing (NLP) deals with text and speech data, knowledge mining focuses on extracting information from large datasets, and content moderation is used for filtering inappropriate content.

Azure Machine Learning Endpoints allow you to deploy trained machine learning models as web services, enabling real-time predictions through RESTful APIs.

Azure Machine Learning Pipelines are used to build and manage automated workflows.

Azure Machine Learning Compute Instances provide development environments for machine learning tasks.

Azure Blob Storage is a service for storing unstructured data.

Speech Recognition is the capability that converts spoken language into text data. It allows applications to transcribe audio input from users for further processing, such as analysis or storage.

Key Phrase Extraction identifies important phrases in text but does not process audio.

Speech Synthesis converts text into spoken audio, which is the opposite of what is needed.

Sentiment Analysis determines the sentiment in text data but does not handle audio input.

Clustering - This is the correct answer. Clustering is an unsupervised machine learning technique used to group data into segments based on similarities without requiring labeled data. It is ideal for grouping customers into segments based on their behavior.

Regression is used for predicting continuous numerical values based on input features, not for grouping data or segmenting customers.

Classification is a supervised learning technique where the goal is to categorize data into predefined classes. It requires labeled data, which is not available in this case.

Time Series Analysis is used to analyze data that is collected over time (e.g., stock prices, sales data) to identify trends or patterns. It is not focused on segmenting data into groups based on behavior.

This statement is True.

The Azure AI Language service provides sentiment analysis, which assesses the emotional tone of text. It categorizes sentiments as positive, neutral, or negative, allowing organizations to analyze customer feedback, reviews, or social media mentions to gain valuable insights into public opinion.

Estimating the future price of a stock based on historical data involves predicting a continuous numerical value, which is the essence of regression. Regression models are used when the output variable is a real or continuous value. The other options are examples of classification or clustering tasks, not regression.

Sarah should use Automated Machine Learning. This feature in Azure Machine Learning automates the process of model selection and hyperparameter tuning by experimenting with multiple algorithms and configurations to find the best model for her data. It is designed to help users who may not have deep machine learning expertise.

The Azure Machine Learning Designer allows users to build models using a drag-and-drop interface but still requires manual selection and tuning of algorithms.

Azure Data Lake Analytics is used for big data processing and query jobs, not for training machine learning models.

Azure Cognitive Services provide pre-built AI capabilities but do not allow for custom model training specific to Sarah's sales forecasting needs.

Codex, available in Azure OpenAI Service, is designed specifically to convert natural language prompts into code snippets, making it ideal for developers who need to generate code from textual descriptions.

GPT-3 is a powerful language model capable of generating text, but Codex is the model specifically optimized for generating code from natural language prompts.

DALL·E is used to generate images from text descriptions, not for converting natural language into code snippets.

BERT is a language model designed for understanding the context of text, but it is not focused on generating code from natural language prompts.

Classification - This is the correct answer. Fraud detection is a classification task because the model needs to classify each transaction as either fraudulent or legitimate, which involves assigning data points to predefined categories or labels.

Regression is used for predicting continuous numerical values for example sales forecasts or prices, not for classifying transactions into categories such as "fraudulent" or "legitimate."

Clustering is an unsupervised learning technique used to group data based on similarities, but it is not suitable for determining whether a transaction is fraudulent or legitimate, which requires labeled data and a classification approach.

Dimensionality Reduction is used to reduce the number of features in the data, typically for improving performance or visualization, but it is not a task in itself for determining fraud or legitimacy.

Extracting text content from images or documents - This is the correct answer. Optical Character Recognition (OCR) solutions are specifically designed to extract text content from images or scanned documents, such as reading printed or handwritten text and converting it into machine-readable format.

Detecting and classifying objects within an image - This is the functionality of object detection or image classification, not OCR. These solutions identify and classify objects in images, but they do not extract text.

Analyzing facial features to recognize emotions - This refers to facial recognition and facial analysis solutions, which focus on detecting and analyzing facial features to determine emotions, not OCR.

Translating spoken language into text - This is the function of speech-to-text solutions, not OCR. Speech-to-text converts spoken language into written text, while OCR deals with extracting text from images or documents.

Content Synthesis tasks - This is the correct answer. Content synthesis tasks involve generating new content that is similar to the examples the model has been trained on. This is typically seen in generative models, where the system can create new, original content such as text, images, or music based on the patterns it has learned.

Anomaly Detection focuses on identifying outliers or unusual patterns in data, not on generating new content. It is used for detecting abnormal behavior or data points.

Data Classification involves categorizing data into predefined classes or categories, such as labeling emails as spam or not. It does not involve generating new content.

Regression Analysis is used to predict numerical values based on historical data, such as forecasting sales or stock prices. It is not focused on generating new content.