AI-900 Practice Test 2 — 10 Questions

Question 1

What is 'temperature' in the context of generative AI model parameters?

Accepted Answer

A parameter controlling the randomness and creativity of model outputs. Temperature is a hyperparameter in generative AI models (such as GPT) that controls the randomness of token sampling during text generation. A higher temperature (e.g., 1.0) increases creativity by making less probable tokens more likely to be chosen, while a lower temperature (e.g., 0.1) makes the output more deterministic and focused on the most probable tokens. This directly affects the diversity and novelty of the generated content.

Answer

The operating temperature of the GPU hardware running the model

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The time required to generate a response

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The minimum confidence threshold for a response

Question 2

What is the difference between Azure OpenAI Service and the public OpenAI API?

Accepted Answer

Azure OpenAI adds enterprise security, compliance, private networking, and Azure integration. Azure OpenAI Service is a Microsoft Azure-based offering that wraps the same underlying OpenAI models (GPT-4, GPT-3.5, etc.) with enterprise-grade features such as Azure Active Directory authentication, private endpoints via Azure Virtual Network, compliance certifications (e.g., ISO 27001, SOC 2), and seamless integration with other Azure services like Cognitive Search and Logic Apps. The public OpenAI API lacks these enterprise controls, making Azure OpenAI the preferred choice for organizations that require data residency, network isolation, and managed identity access.

Answer

Azure OpenAI has different models that perform better than OpenAI

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Azure OpenAI is only available for government customers

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Azure OpenAI does not support GPT-4 models

Question 3

What is a confusion matrix's 'false positive' in medical screening?

Accepted Answer

A patient predicted to have a disease who is actually healthy. In a confusion matrix, a false positive occurs when the model predicts a positive outcome (e.g., disease present) but the actual ground truth is negative (healthy). This is a Type I error, and in medical screening it represents a healthy patient incorrectly flagged as having the disease, leading to unnecessary follow-up tests and anxiety.

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A patient who tests positive and actually has the disease

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A patient who tests negative but actually has the disease

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A patient correctly identified as healthy by the model

Question 4

What is 'Azure Machine Learning notebooks' and who typically uses them?

Accepted Answer

Interactive Jupyter notebook environments for data exploration and model prototyping by data scientists. Azure Machine Learning notebooks are interactive Jupyter notebook environments hosted within Azure Machine Learning studio. They allow data scientists to write and execute Python code for data exploration, visualization, and model prototyping directly in the cloud, with built-in access to compute instances and datasets. Option B correctly identifies both the technology (Jupyter notebooks) and the primary user role (data scientists).

Answer

Digital note-taking applications for recording meeting minutes during ML project planning

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Automated logging notebooks that record all model training metrics without code

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Read-only document viewers for reviewing completed ML experiment results

Question 5

What is the 'Azure AI Language SDK' and what programming languages does it support?

Accepted Answer

Client libraries for Python, .NET, Java, and JavaScript for programmatic access to Azure AI Language. The Azure AI Language SDK is a set of client libraries that allow developers to integrate Azure AI Language capabilities—such as sentiment analysis, key phrase extraction, and language understanding—directly into their applications. It supports Python, .NET, Java, and JavaScript, enabling programmatic access to the service via RESTful APIs or native SDK methods.

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A new programming language created by Microsoft for building NLP applications

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A software development kit for building physical language translation devices

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An IDE plugin that adds Azure AI Language auto-complete to code editors

Question 6

A company wants to build a chatbot that can answer customer questions about their product return policy, shipping times, and warranty information. They have a structured document with these questions and answers. Which Azure AI Language feature should they use to create this chatbot without writing custom code?

Accepted Answer

Custom Question Answering. Custom Question Answering (C) is the correct choice because it is specifically designed to create a chatbot that answers questions based on a structured document (e.g., FAQ, product manual) without writing custom code. It uses a predefined knowledge base of question-answer pairs and provides a built-in orchestration for bot integration, making it ideal for this scenario.

Answer

Conversational Language Understanding (CLU)

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Custom Text Classification

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Language Detection

Question 7

What is 'image generation quality' evaluation — how do you measure if a generated image is good?

Accepted Answer

Metrics like FID (image distribution similarity) and CLIP score (prompt adherence), plus human evaluation. Image generation quality is evaluated using a combination of automated metrics and human judgment. FID (Fréchet Inception Distance) measures how similar the distribution of generated images is to real images, while CLIP score assesses how well the image aligns with the given text prompt. Human evaluation is also critical to capture perceptual quality that automated metrics may miss, such as aesthetic appeal and contextual coherence.

Answer

Only image resolution and file size — higher resolution means better quality

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Simply asking the model what score it gives its own output

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Counting the number of objects correctly included vs. missing from the prompt

Question 8

What is 'Azure AI Language's text analytics for health' (TA4H) and who uses it?

Accepted Answer

A pre-built NLP service for extracting medical entities from clinical text, linked to standard terminologies. Azure AI Language's text analytics for health (TA4H) is a pre-built natural language processing (NLP) service specifically designed to extract medical entities—such as diagnoses, medications, symptoms, and procedures—from unstructured clinical text. It links these entities to standard medical terminologies like SNOMED CT, ICD-10-CM, and RxNorm, enabling structured analysis of health records without requiring custom model training.

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A health monitoring system that analyses patient wearable data for anomalies

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A service for doctors to receive AI-generated medical advice based on their queries

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A healthcare compliance tool that checks medical records for documentation errors

Question 9

What is 'coreference resolution' in natural language processing?

Accepted Answer

Identifying which words or phrases in a text refer to the same real-world entity. Coreference resolution is the NLP task of identifying when two or more expressions in a text refer to the same real-world entity. For example, in 'Alice said she would come,' the pronoun 'she' corefers to 'Alice.' This is fundamental for tasks like document summarization and question answering, where maintaining entity consistency is critical.

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Checking whether a document's references (citations) are correctly formatted

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Resolving conflicts when multiple languages are mixed in the same document

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Matching database foreign keys to their referenced primary keys

Question 10

What is the purpose of Azure AI Vision's 'product recognition' feature?

Accepted Answer

Identifying retail products in images to match them to a product catalog without barcodes. Azure AI Vision's 'product recognition' feature is designed to identify retail products in images and match them to a product catalog without relying on barcodes. It uses computer vision models trained on product images to detect and recognize items based on visual features like packaging, logos, and shape, enabling inventory management and checkout automation in retail scenarios.

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Recognizing counterfeit products in supply chain images

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Recognizing products mentioned in customer text reviews

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Detecting product defects in manufacturing quality control