Apply language model text analysis
11 practice questions under this official exam objective (AI-103), each with the answer key, an option-by-option explanation and instant feedback.
Questions under this objective
11 total · page 1 of 1Running Azure AI Language Containers On-Premises
This AI-103 drag-drop tests the hybrid container deployment of Azure AI Language, where analysis runs on-premises while only authentication and usage
What Azure AI Language entity linking returns for each statement
This AI-103 hotspot tests the outputs of Azure AI Language's recognize_linked_entities operation in a reference-finding solution. The verdict: the cod
Return self-harm severity with Content Safety in Python
This AI-103 hotspot tests the Azure AI Content Safety Python workflow for moderating a comment string. The correct flow is AnalyzeTextOptions(text=com
Azure Foundry AI Services for Image Text Extraction and Location Analysis
This scenario tests the selection of Azure Foundry tools for computer vision and natural language processing. The correct solution uses Azure Vision f
Extract Text and Sentiment Analysis in Azure Foundry
Determines the correct Azure Foundry tools for extracting text from PDF press releases and performing sentiment analysis. Identifies Document Intellig
Deploy the Azure AI Language sentiment container with Docker
This AI-103 hotspot asks how to run the Azure AI Language sentiment analysis container on a VM with Docker. The answer is the sentiment image from Mic
Updating Azure AI Custom Vision Classifier for New Products
Learn the correct sequence to update an Azure AI Custom Vision classifier with new product images while minimizing development effort and training tim
How Should Foundry Chat Playground Explain Code at Lowest Cost?
This AI-103 scenario tests how to ask the Foundry Chat playground to explain JavaScript code while minimizing token spend. The correct approach is a b
Object Detection Model Performance Metrics Analysis
This question requires interpreting model performance metrics like precision and recall from a visual exhibit to identify overfitting or underfitting
Azure OpenAI Embeddings for Document Similarity
This question examines the use of Azure OpenAI embeddings to generate numeric vectors for semantic document comparison. It establishes that embeddings
What Does Named Entity Recognition Return for the London Tour Sentence?
This AI-103 item tests named entity recognition in a text-processing function: the London tour sentence yields London and Buckingham Palace as locatio