Microsoft Azure AI Fundamentals Practice Test (AI-900)

Azure AI Speech supports:

• Converting spoken language into text (speech-to-text).
• Generating natural-sounding speech from text (text-to-speech).
• Verifying or identifying speakers by voice (speaker recognition).

Extracting entities from written text is part of Azure AI Language, not Azure AI Speech. Therefore, the correct choice is the entity-extraction option.

This statement is correct. To ensure ongoing reliability and safety, an AI solution requires regular updates and maintenance. This is because the data and environment in which the model operates can change over time, a phenomenon known as 'data drift' or 'model drift'. These changes can degrade the model's performance and accuracy. Continuous monitoring and updating help address these shifts, fix any emerging issues, and maintain the system's effectiveness and safety. Reducing development costs or changing the fundamental architecture are not the primary goals of post-deployment maintenance.

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.

Azure OpenAI Service supports code generation, which enables developers to create software components based on natural language descriptions. Using models like Codex, developers can input text prompts describing the desired functionality, and the service generates the corresponding code. This greatly simplifies coding tasks and accelerates development.

Image synthesis refers to generating images from text descriptions, not software components. Azure OpenAI Service offers code generation but does not synthesize images for creating software components; image synthesis is a separate capability found in tools like DALL-E.

Data analysis involves examining and interpreting data to derive insights, which is unrelated to creating software components from text descriptions. While Azure OpenAI can assist in querying data, the service’s code generation capability specifically translates natural language into code rather than performing data analysis.

Speech-to-text conversion transcribes spoken language into text, which does not assist in generating software components. Azure offers separate speech services for voice transcription, while code generation is a distinct feature that translates text-based prompts into code outputs.

This statement is True.

Generative AI models, such as those used in image generation, can create realistic images from text prompts. This is a common application of generative AI, exemplified by models like DALL·E and Stable Diffusion, which translate textual descriptions into visual representations.

Language Modeling is the appropriate AI capability for this scenario because it can generate and predict text based on context, allowing the application to suggest probable sentence completions.

Sentiment analysis determines the emotional tone of text, entity recognition identifies and classifies entities within text, and key phrase extraction identifies the main topics or themes. These capabilities do not generate text predictions.

Regression - This is the correct answer. Regression is the appropriate machine learning technique for predicting continuous numerical values, such as the delivery duration for packages. The company can use regression to model the relationship between the input factors (distance, traffic conditions, weather) and the target variable (delivery duration).

Classification is used to predict categorical outcomes, not continuous values like delivery duration. Since the goal is to predict a numeric value, regression is the more suitable approach.

Clustering is an unsupervised learning technique that groups data based on similarities, but it is not used for predicting specific outcomes like delivery duration. It's useful for finding patterns, but not for making specific numerical predictions.

Reinforcement Learning is typically used for decision-making tasks where an agent learns by interacting with an environment for example optimizing actions over time. It is not the best choice for predicting continuous values like delivery duration, which is more suited to regression.

Image Classification assigns a single label to an entire image by analyzing its overall content. This differs from object detection, which identifies and locates individual objects within an image, and image segmentation, which classifies each pixel to understand the image at a granular level.

In machine learning, the label is the variable that represents the output or the value the model is trying to predict. In this scenario, the model aims to predict the 'Final Selling Price' of products, making it the label.

The other variables such as 'Production Cost', 'Marketing Budget' and 'Competitor Prices' are features that the model uses to make the prediction.

Generating synthetic data to augment datasets - This is the correct answer. Generative AI is commonly used to create synthetic data, which can help augment existing datasets.

Image recognition in security systems - This is a task for computer vision, not generative AI.

Categorizing customer feedback into topics - This task is handled by classification or clustering models, not generative AI.

Predicting stock prices using historical data - This is a predictive modeling task, not generative AI.

Regression is suitable for predicting continuous numerical values such as sales revenue.

Classification is used for predicting categorical outcomes

Clustering is used for grouping similar data points without prior labels.

Anomaly Detection identifies unusual data points that differ significantly from the majority of the data.

Entity Recognition identifies and classifies entities like names, organizations, dates and locations in text, converting unstructured data into structured information.

Sentiment Analysis assesses the emotional tone of the text.

**Key Phrase Extraction identifies important terms and phrases.

**Language Modeling predicts word sequences or understands language structure.

The Azure AI Vision service provides a comprehensive set of features for image analysis, including object detection, optical character recognition (OCR) for text extraction, and face detection.

The Azure AI Face detection service specializes in facial detection and analysis but does not offer object detection or text extraction capabilities.

Azure Cognitive Search is designed for indexing and querying searchable content.

Azure AI Text Analytics service focuses on analyzing text data, not images.

Key Phrase Extraction is the process of automatically identifying the most significant and relevant phrases within a text. This technique helps in summarizing the main topics or issues discussed, enabling the company to quickly understand customer concerns.

Sentiment analysis identifies the emotional tone behind words, language detection identifies the language of the text, and entity recognition identifies and classifies named entities, such as names of people or organizations, which may not provide the overall themes or issues.

Automated Machine Learning is the correct answer. It allows users to automatically train and tune machine learning models by selecting the best algorithms and hyperparameters for a given dataset and problem type, reducing the need for manual intervention.

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

Custom model training with the Azure ML SDK involves writing code and manually handling the training process, which can be resource-intensive.

Using pre-trained models from Azure Cognitive Services offers models for specific tasks but doesn't allow customization for the company's sales forecasting data.