AI-901 Practice Test

Azure AI Face is correct because it provides detailed face detection capabilities including locating faces and returning attributes such as age, gender, and emotion from images.

To represent data as numerical vectors is correct because embeddings transform complex data such as text, images, and audio into numerical vectors that can be easily processed by machine learning algorithms.

Foundry Tools provide prebuilt APIs and models for common AI capabilities. is correct because foundry Tools provide prebuilt APIs and models for common AI capabilities, reducing development time.

Azure Active Directory (AAD) for identity and Access Control (RBAC), and Azure Key Vault for encryption is correct because azure Active Directory (AAD) provides robust identity and access management through role-based access control (RBAC), and Azure Key Vault offers comprehensive encryption solutions for data both at rest and in transit.

Clustering is correct because groups similar data points into clusters where intra-cluster similarity is high and inter-cluster similarity is low, reflecting the unsupervised nature of identifying patterns in unlabeled data.

Named entity recognition is correct because it identifies and categorizes people, organizations, locations, and other entities mentioned in text.

To retrieve relevant information quickly is correct because vector search directly retrieves relevant information quickly from an index based on similarity to improve generated content quality.

Azure Stream Analytics is correct because it is specifically designed for real-time processing and analysis of large data streams, making it well suited for detecting anomalies in sensor data.

Implement robust data encryption and access controls is correct because implementing robust data encryption and access controls ensures that sensitive customer data remains confidential and secure from unauthorized access.

The F1 Score is the harmonic mean of precision and recall. is correct because the F1 Score accurately represents the harmonic mean of precision and recall, offering a balanced measure that considers both metrics.