Microsoft Azure AI Fundamentals Practice Test (AI-900)

Predicting the content category of an image - This is the correct answer. Image classification involves assigning a label or category to an image based on its content. For example, an image classification solution might categorize an image as "cat," "dog," or "car" based on the objects it contains.

Identifying and locating objects within an image - This describes object detection, not image classification. Object detection involves both identifying objects and locating them within the image (often with bounding boxes), while classification only assigns a category label.

Extracting text from images for text analysis - This is Optical Character Recognition (OCR), which is focused on extracting text from images, not classifying the entire image into categories.

Detecting facial features to analyze emotions - This is facial recognition or emotion detection, which focuses on detecting and analyzing facial features, often for applications like identifying emotions or individuals, but it is not image classification.

Facial Analysis solutions are designed to detect human faces in images and extract detailed attributes like estimated age, gender, and emotions. This allows the marketing team to gather valuable demographic data from the photos.

Image Classification categorizes entire images based on their content but doesn't provide specific details about faces.

Object Detection identifies and locates objects within an image but doesn't offer detailed analysis of facial features.

**Optical Character Recognition (OCR) extracts text from images and is not applicable for analyzing facial attributes.

Azure Automated Machine Learning is designed to automate the process of selecting the most appropriate algorithms and tuning hyperparameters for your dataset and problem type. It iteratively trains models with different algorithms and parameters to find the best performing model.

Azure Machine Learning Designer provides a drag-and-drop interface for building models but requires you to select algorithms and parameters manually.

Azure Machine Learning Studio Notebooks offer a coding environment for custom model development, which may not save time for those with limited expertise.

Azure Cognitive Services provide pre-built AI services but are not used for custom model training.

Generative AI models are designed to create new data that is similar to the data they were trained on. They learn the underlying patterns and structures in the training data to produce original outputs.

Unlike models that classify or compress data, generative models focus on data creation rather than just analysis or reduction.

Discovering customer segments with similar purchasing behaviors for marketing purposes - This is the correct answer. The scenario where a machine learning technique finds patterns in data without relying on labeled outcomes is best addressed by clustering. In this case, the goal is to segment customers based on their purchasing behaviors, which is an unsupervised learning task that does not require labeled data.

Predicting housing prices based on features like size and location - This task typically requires supervised learning, where the model learns from labeled data for example using historical housing prices to make predictions on new data.

Determining if a transaction is fraudulent based on past labeled data - Fraud detection is a supervised learning task where labeled data (fraudulent vs. legitimate transactions) is used to train a model to classify future transactions.

Forecasting future stock prices based on historical data - While stock price forecasting may involve time series analysis, it is also typically a supervised learning task that uses historical data with known outcomes to predict future trends.

The Codex model in Azure OpenAI Service is specifically designed for translating natural language input into programming code. It supports multiple programming languages and can interpret user intent to generate accurate code snippets, making it ideal for this scenario.

GPT-3 is capable of understanding and generating human-like text, it is not optimized for code generation tasks.

DALL-E focuses on creating images from textual descriptions.

Embeddings are used for semantic understanding and similarity tasks, not code generation.

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.

Azure AI Vision offers optical character recognition (OCR) features that enable the extraction of text from images and documents, making it ideal for processing printed forms.

Azure AI Face specializes in facial detection and analysis, not text extraction.

Azure AI Text Analytics analyzes text for sentiment and key phrases but does not extract text from images.

Azure AI Language Understanding focuses on interpreting conversational language, not on OCR tasks.

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.

The ability to generate data by learning the data distributions is essential for synthesizing new content. Generative models possess this feature, allowing them to create new data instances similar to the training data.

In contrast, classifying data into predefined classes, predicting continuous values, and identifying clusters involve analyzing existing data without producing new content.

Ability to generate new content based on learned data patterns - This is the correct answer. Generative AI solutions are specifically designed to produce new content by learning and emulating the patterns within the data they are trained on. This can include creating text, images, or other forms of content that resemble the original data, making this feature a primary aspect of generative AI.

Classification of data into predefined categories - Classification is a feature of discriminative models, which focus on identifying which category or label a given input belongs to. Generative AI, by contrast, is about creating new content rather than categorizing it. While classification is important in machine learning, it does not describe generative AI.

Detection of anomalies in real-time data streams - Anomaly detection typically involves identifying data points that do not fit within the expected patterns, often using discriminative models or specific anomaly detection algorithms. Generative AI, however, generates new data rather than identifying irregularities, so this is not its primary function.

Extraction of insights from unstructured text data - While generative AI can work with unstructured data to produce new content, the extraction of insights from text generally refers to natural language processing (NLP) techniques like information retrieval, summarization, or sentiment analysis. Generative AI is focused on content creation, not the extraction of information from text.

In machine learning, the label is the target variable that the model is intended to predict. Here, 'The price of the property' is what the model aims to predict, making it the label.

The other options are input variables, or features, that provide information to help the model make accurate predictions.

It can generate new content based on learned patterns from training data - This is the correct answer. Generative AI models are specifically designed to create new content, such as personalized product descriptions, based on patterns learned from the training data. This makes them highly suitable for automating the generation of unique and relevant product descriptions.

It can classify images into predefined categories - This characteristic is typical of image classification models, which focus on categorizing images, not generating new content.

It can predict numerical values based on historical data - This is the function of predictive models, which are used for tasks like forecasting or regression, rather than generating creative content like product descriptions.

It can recognize speech and convert it to text - This describes speech-to-text models, which convert spoken language into written text. It is not related to generating new content, such as product descriptions, from text inputs.

Azure AI Language service is designed for natural language processing tasks, including sentiment analysis and key phrase extraction. It can process text data to determine sentiment (positive, negative, neutral) and extract important phrases, which are essential for understanding customer feedback.

Azure AI Speech service specializes in speech-related tasks like speech-to-text and text-to-speech conversion.

Azure Cognitive Search adds search capabilities to applications.

Azure Machine Learning service is for building and deploying custom machine learning models.

Use training data that includes a wide range of demographic groups - This is the correct answer. Ensuring that the training data is diverse and includes a wide range of demographic groups helps reduce biases and ensures that the automated loan approval system treats all applicants fairly. This approach ensures that the model learns from a broad spectrum of data, which can help minimize unfairness in the system.

Exclude any features related to personal characteristics from the data - While this may seem like a good way to prevent bias, excluding personal characteristics (such as age, gender, or race) entirely could lead to a model that lacks important context for making fair and informed decisions.

Increase the complexity of the algorithm to improve accuracy - Increasing algorithm complexity might improve accuracy, but it does not directly address fairness.

Expand the dataset by collecting more data of the same type - Expanding the dataset with more of the same type of data might not necessarily improve fairness if the data itself is biased or unrepresentative.