AI-900 Practice Test 18 — 15 Questions

Question 1

What is a system prompt in an Azure OpenAI deployment?

Accepted Answer

An instruction that defines the model's behavior, persona, and constraints for a session. A system prompt in Azure OpenAI is a foundational instruction set that defines the model's behavior, persona, and constraints for a session. It acts as a persistent directive that guides the model's responses throughout the conversation, ensuring alignment with specific use cases like tone, safety, or domain focus.

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A technical error message returned when the model fails

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A user's first message to start a conversation

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A command to restart the AI model instance

Question 2

What is 'text analytics' in Azure AI Language?

Accepted Answer

AI-powered extraction of insights (sentiment, key phrases, entities) from unstructured text. Text analytics in Azure AI Language is an AI-powered service that extracts structured insights from unstructured text. It uses pre-built models to identify sentiment (positive/negative/neutral), key phrases, named entities (people, places, organizations), and language detection, enabling automated analysis of large volumes of text data.

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A tool for counting the number of words and characters in a document

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A spreadsheet function for analysing numerical data in text cells

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Encrypting sensitive text data before storing it in the cloud

Question 3

What is the ethical concern with using AI for 'predictive policing'?

Accepted Answer

Potential to perpetuate racial bias, undermine due process, and create discriminatory self-fulfilling prophecies. Predictive policing AI systems often rely on historical crime data, which can contain inherent biases from over-policing in minority communities. This can lead to a feedback loop where the AI predicts more crime in those areas, prompting more police presence, which in turn generates more arrests and reinforces the original bias. Such systems also risk undermining due process by making decisions based on statistical correlations rather than individual evidence, and can create self-fulfilling prophecies where predicted crime hotspots become actual crime hotspots due to increased enforcement.

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Predictive policing AI is too expensive to implement at city scale

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Predictive policing models are too slow to be useful for real-time decisions

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Predictive policing AI might predict crimes in the wrong ZIP code

Question 4

What is 'federated learning' and when is it used for privacy-preserving AI?

Accepted Answer

Distributed training where devices share model updates (not raw data) — enabling privacy-preserving collaborative learning. Federated learning is a distributed machine learning technique where the model is trained across multiple decentralized devices or servers holding local data, without exchanging the raw data itself. Instead, only model updates (e.g., gradients or weights) are shared with a central server, which aggregates them to improve the global model. This approach preserves privacy because sensitive data never leaves the local device, making it ideal for scenarios like healthcare, finance, or mobile keyboard prediction where data cannot be centralized due to regulatory or privacy constraints.

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Training a model using data from multiple countries governed by a federal legal system

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A training approach where a federal government agency controls access to all training data

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Combining predictions from models trained independently at multiple research institutions

Question 5

What is 'Azure Machine Learning designer' and who is it designed for?

Accepted Answer

A drag-and-drop visual interface for building ML pipelines without writing code. Azure Machine Learning designer is a drag-and-drop visual interface that allows users to build, test, and deploy machine learning pipelines without writing code. It is designed for data scientists and developers who prefer a low-code or no-code approach to creating ML workflows, enabling them to focus on model design rather than programming syntax.

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A tool for designing Azure network infrastructure diagrams

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A user interface design tool for building AI-powered mobile applications

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A visualisation tool for exploring and analysing completed model training runs

Question 6

A company is developing an AI system to recommend movies to users. The team wants to ensure that the recommendations do not discriminate based on gender or ethnicity. Which Microsoft responsible AI principle is most directly related to this goal?

Accepted Answer

A) Fairness. Fairness is the Microsoft responsible AI principle that directly addresses the goal of preventing discrimination based on gender or ethnicity in AI recommendations. It requires that AI systems treat all people equitably, avoiding biases that could lead to unfair outcomes, such as recommending different movies to users based on protected attributes rather than their preferences.

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B) Inclusiveness

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C) Reliability and Safety

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D) Transparency

Question 7

What is object detection, and how does it differ from image classification?

Accepted Answer

Object detection identifies and locates multiple objects with bounding boxes; image classification labels the whole image. Object detection goes beyond image classification by not only identifying what objects are present in an image but also localizing each object with a bounding box. Image classification assigns a single label to the entire image, whereas object detection can handle multiple objects of different classes simultaneously. This makes object detection suitable for tasks like counting objects or tracking their positions.

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Object detection identifies what is in an image; image classification also identifies where objects are located

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Object detection and image classification are the same task

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Object detection is used only for face recognition

Question 8

A company uses Azure OpenAI Service to power an AI assistant that helps customers with product troubleshooting. The assistant must maintain the conversation history to provide contextually relevant answers across multiple turns. Which API endpoint should be used for this purpose?

Accepted Answer

Chat Completions API. The Chat Completions API is designed for multi-turn conversational scenarios because it accepts a list of messages with roles (system, user, assistant) that represent the conversation history. This allows the model to maintain context across multiple interactions, making it the correct choice for an AI assistant that needs to provide contextually relevant answers over several turns.

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Completions API

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Embeddings API

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Fine-tuning

Question 9

What is 'scene understanding' in Azure AI Vision?

Accepted Answer

Holistic comprehension of an image's full context, relationships, and scene description. Scene understanding in Azure AI Vision goes beyond simple image classification to provide a holistic comprehension of an image's full context, including objects, their relationships, and a descriptive scene summary. This capability leverages deep learning models to analyze the entire visual content and generate human-readable captions that describe what is happening in the image, such as 'a group of people playing soccer in a park.'

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Classifying images by the type of filming location (indoor, outdoor, urban, rural)

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Breaking an image into individual scenes for video timeline analysis

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Determining the camera settings (ISO, aperture) used to capture a photograph

Question 10

What is 'video summarisation' in Azure Video Indexer and how does it work?

Accepted Answer

Automatically creating a highlight reel of the most informative video segments from a longer video. Video summarization in Azure Video Indexer automatically creates a highlight reel by selecting the most informative and visually interesting segments from a longer video. It uses AI models to analyze visual content, audio, and scene dynamics to identify key moments, such as changes in activity, faces, or objects, and then stitches these segments into a concise summary. This is distinct from transcript generation or chapter markers, as it focuses on extracting a condensed video output rather than text or navigation aids.

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Generating a text transcript summary of what was said in the video

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Compressing video file size while maintaining acceptable visual quality

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Adding automatic chapter markers and timestamps to a video for navigation

Question 11

What is 'AI in financial services' and what specific AI capabilities are most commonly applied?

Accepted Answer

Fraud detection, credit scoring, chatbots, KYC, sentiment analysis, and regulatory automation. It accurately lists the most common AI capabilities applied in financial services: fraud detection (using anomaly detection models), credit scoring (via supervised learning on historical data), chatbots (leveraging natural language processing), KYC (using document verification and facial recognition), sentiment analysis (applying NLP to news and social media), and regulatory automation (using rule-based AI and robotic process automation). These represent the broad, practical deployment of AI in finance, not a narrow or unrealistic use case.

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AI that automatically manages investment portfolios without any human involvement

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AI exclusively for high-frequency trading in stock markets

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Using AI to design new financial products like insurance policies and loan products

Question 12

What is 'liveness detection' in Azure AI Face service?

Accepted Answer

Verifying that a face presented to a camera is a real live person, not a photo or video replay. Liveness detection in Azure AI Face service is a security feature that distinguishes between a real, live person and a spoofing attempt such as a printed photo, video replay, or a 3D mask. It analyzes subtle cues like eye blinking, skin texture, and depth to ensure the face presented to the camera is physically present and alive. This prevents unauthorized access in identity verification scenarios.

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Detecting whether a celebrity face in a photograph is still alive or deceased

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Detecting human faces in real-time video streaming from security cameras

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Monitoring whether a face recognition model remains accurate after deployment

Question 13

What is 'responsible AI by design' in the context of building Azure AI applications?

Accepted Answer

Integrating ethical AI principles and safety tools throughout the entire development lifecycle. 'responsible AI by design' means proactively embedding ethical principles—such as fairness, reliability, transparency, privacy, and accountability—into every phase of building an Azure AI application, from problem definition and data collection to deployment and monitoring. This approach aligns with Microsoft's Responsible AI Standard and is operationalized through tools like Fairlearn, Error Analysis, and the Responsible AI dashboard in Azure Machine Learning, ensuring that safety and ethical considerations are not afterthoughts but integral to the development lifecycle.

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Using only Azure-approved AI models to avoid legal liability

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Designing AI systems that only respond to pre-approved questions

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Requiring legal review before every AI model deployment

Question 14

What is 'AI system' vs 'AI model' in the context of responsible AI?

Accepted Answer

An AI model is the prediction function; an AI system includes all surrounding pipelines, interfaces, and human processes. In responsible AI, the distinction is that an AI model is the mathematical prediction function (e.g., a trained neural network or decision tree), while an AI system encompasses the model plus all surrounding components: data ingestion pipelines, inference APIs, user interfaces, monitoring, logging, and human-in-the-loop processes. This broader view is critical for governance, because ethical risks (bias, drift, transparency) often arise from the system's context, not just the model's logic.

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An AI model is software; an AI system includes the hardware it runs on

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AI systems are more accurate than individual models because they combine multiple models

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An AI model runs offline; an AI system requires internet connectivity

Question 15

What is 'content moderation' in the context of Azure OpenAI?

Accepted Answer

Automatically filtering and classifying inputs/outputs for harmful content categories. Content moderation in Azure OpenAI uses AI models to automatically scan both user prompts (inputs) and generated responses (outputs) for harmful content such as hate, violence, sexual material, and self-harm. It applies configurable severity filters (e.g., low, medium, high) to block or flag content that violates Microsoft's Responsible AI policies, ensuring safe deployment of generative AI workloads.

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Controlling how much content a user is allowed to generate per day

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Editing generated text to improve grammar and style

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Optimising prompt length to reduce token costs