AI-900 Practice Test 18 — 15 Questions

Question 1

What is the primary benefit of using pre-built AI services (like Azure AI Vision or Azure AI Language) versus building custom ML models?

Accepted Answer

Rapid deployment with no ML expertise required — accessible via REST API. Pre-built AI services like Azure AI Vision and Azure AI Language are designed for rapid deployment without requiring machine learning expertise. They provide REST API endpoints that developers can call directly to integrate capabilities like image analysis or sentiment analysis into applications, eliminating the need to train, manage, or deploy custom models.

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Pre-built services always outperform custom models for any task

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Pre-built services are always free, unlike custom ML models

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Pre-built services work offline without internet connectivity

Question 2

A developer uses the Azure OpenAI Service to generate product descriptions for an e-commerce catalog. The developer notices that the generated text is often too long, exceeding the desired word count. Which parameter should the developer set in the API request to strictly limit the length of the generated output?

Accepted Answer

Max_tokens. (max_tokens) is correct because it directly controls the maximum number of tokens (words or subwords) the model can generate in a single response. By setting this parameter to a specific value, the developer enforces a hard limit on output length, preventing the generated product descriptions from exceeding the desired word count.

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Temperature

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Top_p

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Frequency_penalty

Question 3

What is 'agentic AI' and how does it differ from a simple chatbot?

Accepted Answer

AI that autonomously plans and executes multi-step workflows using tools to accomplish complex goals. Agentic AI refers to AI systems that can autonomously plan and execute multi-step workflows by using external tools, APIs, or data sources to achieve complex goals. This differs from a simple chatbot, which typically responds to user prompts in a single turn without independent goal-setting or tool orchestration. In generative AI workloads on Azure, agentic AI might leverage Azure AI Agent Service or Semantic Kernel to chain together calls to Azure Cognitive Search, Azure Functions, or external APIs, enabling tasks like automated report generation or multi-step data analysis.

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AI that represents a company as a legal agent for contractual purposes

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Chatbots that can respond on behalf of a company's customer service team

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AI models that were trained by multiple agents working simultaneously in parallel

Question 4

A bank deploys an AI system to approve personal loan applications. After six months, an audit reveals that applicants from certain postal codes receive significantly lower approval rates than applicants from other postal codes, even when their income and credit scores are comparable. Which Microsoft responsible AI principle is most directly violated by this outcome?

Accepted Answer

Fairness. The AI system's approval decisions produce systematically different outcomes for applicants from different postal codes despite comparable income and credit scores, which directly violates the Fairness principle. Fairness requires that AI systems treat all individuals and groups equitably, avoiding discrimination based on sensitive attributes like location. The audit evidence shows the model has learned spurious correlations between postal code and loan risk, leading to biased approval rates.

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Transparency

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Inclusiveness

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Reliability and safety

Question 5

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 6

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 7

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 8

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 9

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 10

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 11

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 12

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 13

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 14

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 15

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