AI-103 — Frequently Asked Questions

While Managed Identity is more secure, it requires additional configuration steps like assigning roles and enabling the identity, which contradicts the requirement to minimize administrative effort.

It is a unique credential provided by the service that allows applications to authenticate and access API endpoints directly without needing complex OAuth flows.

A single-service resource has its own key/endpoint for one API (e.g., only Vision). A multi-service resource ('CognitiveServices') shares one key/endpoint for multiple APIs, simplifying management and billing.

PUT is used to create a new resource or replace an existing one at a specific URI. PATCH is used for updates, and DELETE for removal.

Use Classification when you want to label the entire image content (e.g., is this a cat?). Use Object Detection when you need to locate and count multiple items within an image using bounding boxes.

Binary is for two mutually exclusive outcomes (Yes/No). Multiclass is for three or more distinct categories (Cat/Dog/Bird), even if currently only two exist.

The toolset parameter only makes the tool available to the agent. Without tool_choice, the LLM can still decide not to call it if it believes it knows the answer.

response_format controls how the final answer is structured (e.g., JSON schema), while tool_choice controls which external functions the agent is allowed or forced to execute.

Model Catalog is used for discovering and evaluating public/pre-trained models via leaderboards. Model Registry is for tracking versions of models you have trained or customized.

Serverless deployments abstract the infrastructure, meaning the vendor manages the hardware and storage of model weights, whereas container-based deployments give you control over the storage location.

GPT-4 generates text responses. It does not output the raw numeric vectors required for direct similarity calculations without significant custom post-processing.

They convert text into high-dimensional vectors that capture semantic meaning, enabling efficient similarity searches and clustering.

'LLM' commonly stands for both 'Large Language Model' in tech and 'Master of Laws' in legal education. Without context, the LLM cannot determine which definition the user wants.

Context steering adds explicit constraints to the prompt, guiding the model's attention mechanism to focus on relevant tokens and domain-specific knowledge, thereby reducing irrelevant outputs.

The model name defines the architecture, but the deployment name specifies the exact versioned instance running on the server.

No, the endpoint URL is required to locate the resource, and the deployment name is needed to target the specific model instance.

Azure Machine Learning offers more flexibility but requires significantly more development effort (coding, pipelines) compared to the low-code approach of Custom Vision.

Yes, Custom Vision models can be deployed to endpoints that process images or video frames in real-time to detect objects or classify images.

Because 'required' only forces the use of any tool. If multiple tools exist, the agent could pick the wrong one. The question requires the specific MCP tool.

It means the outcome is predictable and fixed. Using the tool type ensures the exact same tool is called every time, preventing the base model from skipping it.

Bearer tokens are for dynamic tokens (OAuth/JWT). API keys are static strings usually placed in a custom header, best handled by the apiKey type.

No. Defining the parameter only describes the request. You must link it to a security scheme so Foundry knows to fetch the value from your connection.

A model cascade chains multiple language models sequentially, using a small/cheap model for simple queries and escalating complex ones to larger models.

Smaller models may fail to provide accurate responses for complex reasoning tasks, leading to degraded quality despite lower costs.

HTTP 429 means 'Too Many Requests', indicating you have hit a rate limit or resource cap (Provisioned Utilization), not that the model is unavailable.

Check the Request/Response logs to see if the retrieval step failed to find documents or if the model simply chose not to generate an answer.

Use content safety filters, toxic content detection, and input validation pipelines to detect and block unsafe images or prompt injections before they reach the model.

It is a feature that scans model outputs for copyrighted text or code to prevent intellectual property violations, not for input security.

A RAG evaluator assesses the entire pipeline including retrieval quality. Groundedness specifically checks if the text output is faithful to the context, which is the exact requirement here.

No. Guardrails typically filter input/output for safety or policy violations before generation or block unsafe queries, rather than evaluating the factual correctness of the generated response.

Few-shot examples guide style and reasoning patterns but cannot reliably enforce negative constraints or prevent off-topic hallucinations.

No, they control randomness and creativity. They do not restrict the factual domain or ensure the model stays on topic.

Max_tokens limits length, not selection. If the model chooses to omit content, more tokens won't force it back.

Improve prompts, add explicit instructions to include all clauses, or use structured output schemas to enforce completeness.

Read API extracts linear text but loses layout structure. Article layout interpretation would require significant custom coding, violating the 'minimize development effort' constraint.

Document Intelligence extracts and structures text/content from documents. Image Analysis identifies visual objects, tags, and descriptions, not textual content.