Microsoft Azure AI Fundamentals Practice Test (AI-900)

Fairness - This is the correct answer. To address the issue of the recruitment system consistently selecting candidates from a single demographic group, the development team should focus on fairness. Fairness ensures that the AI system treats all candidates equitably, regardless of their demographic group, and reduces bias in decision-making.

Inclusiveness is about ensuring that diverse perspectives are considered during the development process and that AI systems are accessible to all groups. While important, it is a broader concept and does not directly address the specific issue of bias in selection leading to a lack of diversity.

Transparency refers to making the AI model’s decision-making process understandable and visible to stakeholders. While transparency is important, fairness is the key principle for addressing the lack of diversity in candidate selection.

Accountability involves ensuring that there is oversight and responsibility for the outcomes of AI systems. While important, accountability alone does not directly address the bias in selection processes; fairness is the principle most relevant to correcting this issue.

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.

Azure AI Language service provides capabilities for natural language processing tasks such as sentiment analysis, key phrase extraction, and named entity recognition. This makes it the appropriate choice for analyzing text data to gain insights from customer reviews.

Azure AI Speech service is designed for speech recognition and synthesis, which are not applicable to analyzing written text.

Azure Cognitive Search is used for indexing and searching over structured and unstructured data but does not perform text analytics tasks like sentiment analysis.

Azure Bot Service helps create conversational experiences and does not offer text analysis features.

Implementing robust encryption techniques for data at rest and in transit - This is the correct answer. To address concerns about privacy and security, the company should implement strong encryption techniques to protect user data both when it is stored at rest and when it is transmitted in transit. This ensures that sensitive data is secure and reduces the risk of unauthorized access.

Collecting as many data points as possible to improve recommendations - While collecting data may help improve recommendations, it raises privacy concerns if personal data is not properly protected or managed. This approach does not directly address the need for secure handling of user data.

Giving developers access to user data for debugging purposes - Giving developers access to user data can create significant privacy and security risks. It is important to ensure that user data is only accessible to those who have a legitimate need and that proper safeguards are in place.

Storing user data on shared servers to reduce costs - Storing user data on shared servers without adequate security measures can expose the data to higher risks of breaches and unauthorized access. It is essential to store user data securely, even if it means higher costs for private or dedicated infrastructure.

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.

Language Modeling is the natural processing language (NLP) technique that enables prediction of the next word or sequence of words in a text, making it ideal for features like text prediction and auto-completion. It learns patterns from large amounts of text data to anticipate user input.

Sentiment analysis identifies the emotional tone of text, key phrase extraction finds important phrases, and entity recognition detects and classifies entities like names and places; none of these are used for predicting text input.

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.

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.

Object Detection is the appropriate solution because it not only recognizes objects within an image but also determines their locations by drawing bounding boxes around them. This allows the system to both identify damaged packages and pinpoint where they are in each image.

Image Classification could determine if an image contains a damaged package but cannot locate it within the image.

Optical Character Recognition (OCR) is used for extracting and interpreting text from images, which is not applicable in this scenario.

Facial Detection focuses on identifying human faces and would not be useful for detecting packages.

The correct answer is Optical Character Recognition (OCR). OCR is the technology used to extract printed or handwritten text from images, such as scanned documents like invoices. This allows the text to be converted into a machine-readable format, which is essential for automating data entry. Object detection is used to identify the location of objects in an image. Image classification is used to categorize an entire image. Facial detection is used to locate human faces in an image.

Object Detection solution - This is the correct answer. Object detection is the most appropriate computer vision solution for monitoring warehouse shelves using video feeds. It can identify and locate multiple types of products within the images or video frames, detecting and counting the products based on their types and locations on the shelves.

Image Classification solution - Image classification would only categorize the entire image into predefined groups for example "shelf full" or "shelf empty" without detecting and locating individual products. Object detection is better suited for counting and identifying specific items.

Optical Character Recognition (OCR) solution - OCR is used to extract text from images or documents, which is not applicable to detecting and counting products on shelves, unless the products are labeled with readable text, but it's still not the best solution for this task.

Facial Detection and Analysis solution - Facial detection is used to identify and analyze human faces, which is unrelated to the task of monitoring products on shelves.

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.

Azure AI Language service offers natural language processing capabilities like sentiment analysis to determine the emotions in text and key phrase extraction to identify important topics mentioned. These features help companies analyze customer feedback effectively.

Azure Cognitive Search is used for indexing and searching content.

Azure AI Speech service focuses on processing spoken language.

Azure Bot Service is designed for building conversational bots.

Analyzing images and extracting visual information - This is the correct answer. The Azure AI Vision service provides developers with capabilities to analyze images and extract visual information. This includes tasks such as image classification, object detection, and optical character recognition (OCR), helping developers to gain insights from images.

Transcribing spoken language into text - This is the functionality of speech recognition services, not the Azure AI Vision service. Speech recognition is focused on converting spoken language into written text.

Translating text between different languages - This is the functionality of Azure Translator service, which is designed for translating text between languages, not related to image analysis.

Performing sentiment analysis on text data - This is the functionality of text analytics services, which are used for tasks like sentiment analysis, key phrase extraction, and language detection, but it is not related to image analysis.

The Azure AI Language service is a cloud-based service specifically designed for Natural Language Processing (NLP) tasks. It provides pre-built models and APIs that can analyze text for sentiment analysis and extract key phrases, which represent the main topics discussed.

Azure AI Search is a search-as-a-service solution. While it can be enriched with AI skills like sentiment analysis to improve search results, the Azure AI Language service is the underlying service that provides these analytical capabilities.

Azure AI Speech service focuses on processing spoken language, such as converting speech-to-text or text-to-speech, not analyzing pre-existing text.

Azure Machine Learning is a platform for building, training, and deploying custom machine learning models. It would be used if you needed to create a unique sentiment model from scratch, rather than using a pre-built capability.

The validation dataset is used during model training to evaluate the model's performance and tune hyperparameters, helping to prevent overfitting. It provides feedback on how well the model generalizes to unseen data during training and allows adjustments to improve the model. The other options are incorrect because they describe the roles of the training and test datasets rather than the validation dataset.

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.

Label - This is the correct answer. In supervised machine learning, the label is the target variable that the model aims to predict based on the input variables (features). For example, in a dataset predicting house prices, the house price would be the label.

Feature - A feature is an input variable (predictor) used by the model to make predictions. Features are the independent variables in the dataset that help determine the value of the label.

Parameter - A parameter refers to the internal variables of a model that are learned during training, such as the weights in a neural network. Parameters are not the target variable but the model's learned coefficients.

Hyperparameter - A hyperparameter is a setting that is manually chosen before training a model (such as the learning rate or the number of trees in a random forest) but is not part of the dataset itself. Hyperparameters control the learning process and model configuration.

Azure Cognitive Services Translator is designed specifically for translating text between languages while preserving meaning and context. It provides advanced neural machine translation capabilities that allow applications to programmatically translate text with high accuracy.

Azure Cognitive Services Text Analytics focuses on extracting insights from text, such as sentiment analysis and key phrase extraction, but does not perform translation.

Azure Cognitive Services Language Understanding (LUIS) is used for creating models that understand user intents and entities in natural language but does not translate text.

Azure Cognitive Custom Vision is tailored for image recognition tasks and is not related to text translation.

Natural Language Processing (NLP) is used to analyze and understand human language in text or speech form. Since the company wants to analyze textual customer reviews to determine sentiment, NLP techniques are appropriate for this task. Computer Vision focuses on visual data like images and videos, which doesn't apply to text reviews. Predictive Maintenance and Time Series Forecasting involve predicting equipment failures and future values based on time-series data, respectively, neither of which relate to analyzing text reviews for sentiment.