Microsoft Azure AI Fundamentals Practice Test (AI-900)

The validation dataset is used to adjust model hyperparameters and prevent overfitting by evaluating the model's performance during training. It helps fine-tune the model before final testing. The training dataset is used for teaching the model, while the test dataset assesses the final performance on unseen data. Collecting new data is not related to the validation dataset's purpose.

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.

Computer Vision is the appropriate AI workload for analyzing visual data to identify and track objects or patterns within images or video. In this scenario, the team needs to process visual data to recognize different animal species, which is a typical application of Computer Vision.

Natural Language Processing (NLP) deals with understanding and generating human language, which is not relevant here.

Knowledge Mining involves extracting information from large datasets but doesn't specifically handle visual recognition tasks.

Document Intelligence focuses on extracting information from documents, which does not apply to analyzing images or videos.

The Codex code-davinci-002 model in Azure OpenAI Service is specifically designed for code generation tasks. It can understand and generate programming code, providing developers with code completions, suggestions, and even translating natural language descriptions into code.

While GPT-3 models like text-davinci-003 and GPT-3.5 Turbo are powerful for natural language generation, they are not optimized for code generation.

DALL·E 2 is an image generation model and does not assist with code.

Language Modeling - This is the correct answer. Language modeling is primarily used for tasks like predicting the next word or suggesting phrases as a user types. It helps the application understand the context and structure of language to provide relevant suggestions in real-time.

Key Phrase Extraction - Key phrase extraction is used to identify important terms or concepts within a text, but it is not typically used for real-time suggestions or completing sentences as a user types.

Sentiment Analysis - Sentiment analysis is used to determine the emotional tone of text such as positive, negative or neutral. But it is not relevant for generating word or phrase suggestions while typing.

Entity Recognition - Entity recognition identifies and extracts specific entities like names, places or dates from text. It is not focused on generating suggestions for completing sentences or phrases.

Azure AI Vision service provides image analysis features that can generate descriptive captions and extract tags from images. This capability enables the company to automatically process its product images and improve the searchability and organization of its online catalog.

Azure AI Face service focuses on detecting and analyzing human faces, which is not applicable for general product image analysis.

Azure AI Search is a search-as-a-service solution. While it can be integrated with AI skills to enrich an index, it does not natively perform the initial image analysis to generate captions and tags.

Azure AI Document Intelligence is used to extract text, key-value pairs, and structure from documents, not to analyze and caption general images.

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.

The Codex models in Azure OpenAI Service are specifically designed to translate natural language prompts into code across various programming languages, making them ideal for automating code creation tasks.

The GPT-3 models are geared towards generating human-like text but are not specialized for code.

The DALL·E models generate images from textual descriptions, not code.

The Embeddings models are used for finding text similarities and are not suitable for generating code from descriptions.

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.

Leverage the service's ability to generate images from text descriptions - This is the correct answer. Azure OpenAI Service provides models like DALL·E, which can generate images based on descriptive text inputs.

Use code generation features to create image-rendering scripts - This feature is focused on generating code for specific tasks

Apply language translation capabilities to interpret user inputs - Language translation is used for converting text from one language to another

Implement image analysis to extract information from images - Image analysis focuses on understanding and processing images

This statement is False.

Filtering out offensive or explicit material from user-generated content is the function of content moderation systems, not content recommendation systems. Content recommendation systems are used to personalize user experiences by suggesting content based on user preferences and behavior.

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.

Clustering is the most suitable technique for this scenario because it involves grouping data points based on similarities without predefined labels. This unsupervised learning method helps identify natural groupings within the customer data, enabling the retailer to tailor marketing strategies for each segment.

Regression and classification are supervised learning techniques used for prediction and classification tasks with labeled data, while reinforcement learning is used for training agents through rewards and penalties. None of these are appropriate for discovering inherent groupings in unlabeled data.

Designing user interfaces that accommodate various abilities ensures inclusiveness by allowing people with different needs, including those with disabilities, to use the solution effectively.

Focusing solely on personalization tailors the experience but may not address broader accessibility issues. Incorporating complex technical language can alienate users who are not technically proficient. Optimizing for a specific group limits the solution's accessibility to others.

Predicting whether a patient has a disease involves assigning each patient to one of two predefined categories: has the disease or does not have the disease, based on diagnostic tests. This is a classification problem because the model predicts categorical labels. Estimating future sales revenue and forecasting temperature are regression problems since they predict continuous numerical values. Grouping movies based on viewer ratings is a clustering problem, which aims to discover natural groupings in data without predefined categories.