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Associate [AI-102] Flashcards

7 cards from real AI practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.

Read the first 7 Associate [AI-102] flashcards as text

  1. What is 'grounding' in the context of Retrieval-Augmented Generation (RAG) with Azure OpenAI?

    Answer: Reducing model hallucinations by providing retrieved context as part of the prompt

    Grounding anchors model responses to retrieved external documents, reducing hallucinations by giving the model factual context in the prompt.

  2. In Azure Cognitive Search, which analyzer type should you choose to support language-specific stemming for French documents?

    Answer: Language analyzer (fr.microsoft)

    Language-specific analyzers like fr.microsoft apply stemming, stop-word removal, and tokenization rules tailored to a specific language.

  3. Which AI-102 concept describes splitting a large document into smaller pieces before embedding them for vector search?

    Answer: Chunking

    Chunking divides large documents into manageable segments so each chunk can be embedded and retrieved independently with higher relevance.

  4. You need to identify named entities like people, organizations, and locations from customer feedback. Which Azure AI Language feature should you use?

    Answer: Named Entity Recognition (NER)

    Named Entity Recognition identifies and categorizes entities such as persons, organizations, and locations within unstructured text.

  5. What is the role of an 'enrichment pipeline' in Azure Cognitive Search?

    Answer: It applies AI skills during indexing to extract and transform content

    An enrichment pipeline (skillset) applies AI skills—OCR, entity extraction, translation—to raw content during indexing to augment the searchable index.

  6. Which deployment option allows you to host an Azure AI model locally on a device without requiring internet connectivity?

    Answer: Azure IoT Edge with AI modules

    Azure IoT Edge allows deploying containerized AI models as edge modules that run locally on devices without continuous cloud connectivity.

  7. In Azure Machine Learning, what is the purpose of 'model registration'?

    Answer: Versioning and storing trained models in a central repository for tracking and reuse

    Model registration stores versioned model artifacts in the Azure ML model registry, enabling tracking, auditing, and controlled deployment.