AI-900 Practice Exam 59 — 20 Questions

Question 1

A news agency wants to build a system that can automatically generate a short headline for each news article. The system should select the most important sentence from the article as the headline. Which Azure AI Language feature should they use?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

B: Extractive summarization. Extractive summarization is the correct Azure AI Language feature because it identifies and extracts the most important sentences from a document, which directly matches the requirement to select the most important sentence as a headline. Unlike key phrase extraction, which returns individual words or short phrases, extractive summarization returns complete sentences ranked by relevance, making it ideal for headline generation.

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A: Key phrase extraction

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C: Entity recognition

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D: Sentiment analysis

Question 2

A company uses Azure OpenAI Service to generate summaries of long technical documents. They notice that the model sometimes produces summaries that sound plausible but contain factual errors contradicting the source document. Which concept describes this type of error in large language models?

Accepted Answer

Hallucination. Hallucination in large language models refers to the generation of content that is factually incorrect or nonsensical but presented with confidence. In this scenario, the model produces summaries that sound plausible yet contain factual errors contradicting the source document, which is the hallmark of hallucination. This occurs because the model generates text based on probabilistic patterns rather than verifying facts against the input.

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Overfitting

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Tokenization

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Bias

Question 3

A healthcare organization plans to use AI to analyze patient records for medical research. They must ensure that patient data is protected from unauthorized access during storage and processing. Which Microsoft responsible AI principle is most directly relevant to this requirement?

Accepted Answer

Privacy and security. The requirement to protect patient data from unauthorized access during storage and processing directly aligns with the Privacy and security principle. This principle mandates that AI systems implement robust data protection mechanisms, such as encryption at rest (e.g., AES-256) and in transit (e.g., TLS 1.2/1.3), access controls (e.g., Azure RBAC), and compliance with regulations like HIPAA. It ensures that sensitive healthcare data remains confidential and secure throughout its lifecycle.

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Fairness

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Inclusiveness

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Reliability and safety

Question 4

A bank uses an AI system to approve personal loans. Some customers whose loans were denied have asked for an explanation of why their application was rejected. Which Microsoft responsible AI principle requires the bank to provide these explanations?

Accepted Answer

Transparency. Transparency is the correct principle because it requires AI systems to be understandable and for decisions to be explainable to users. In this scenario, customers denied loans have a right to know why the AI made that decision, which aligns with Microsoft's principle of transparency—ensuring that AI systems communicate their purpose, limitations, and reasoning in clear, accessible terms.

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Fairness

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Reliability and safety

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Accountability

Question 5

What is 'depth estimation' in computer vision and what are its applications?

Accepted Answer

Inferring the distance of objects from the camera to produce a spatial depth map. Depth estimation is a computer vision technique that infers the distance of objects from the camera, producing a spatial depth map where each pixel represents a distance value. This is commonly achieved using stereo vision (two cameras) or monocular depth estimation (single camera with deep learning models). It is a core feature of Azure Computer Vision's spatial analysis capabilities, enabling applications like augmented reality, autonomous navigation, and 3D scene reconstruction.

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Measuring the depth of colour in an image (number of bits per pixel)

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Analysing how deeply a subject is embedded in a complex background scene

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Determining how much detail is captured in a photograph based on lens quality

Question 6

Which Azure AI service can analyze an image and return a description of its contents in natural language?

Accepted Answer

Azure AI Vision (Computer Vision). Azure AI Vision (Computer Vision) includes an image analysis API that can generate a human-readable description of an image's contents. This feature uses deep learning models to identify objects, actions, and scenes, then produces a natural language caption describing the image. The correct answer is B because this is the specific service designed for image understanding and description generation.

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Azure AI Language

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Azure AI Speech

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Azure Bot Service

Question 7

What is 'reinforcement learning from human feedback' (RLHF) and how is it used in training AI models?

Accepted Answer

Using human preference ratings to train a reward model that guides language model optimisation. Reinforcement learning from human feedback (RLHF) is a technique where human evaluators rank or rate model outputs (e.g., text completions) to create a reward model. This reward model then guides the fine-tuning of a language model using reinforcement learning, typically with Proximal Policy Optimization (PPO), to align outputs with human preferences.

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A technique where humans physically assist robots in learning physical tasks

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Having human reviewers manually rewrite model outputs to improve training data

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Allowing end users to flag incorrect answers to automatically retrain the model in real time

Question 8

A library wants to automatically generate descriptive alt text for hundreds of historical photographs in their digital archive. For each photo, the system should produce a natural-language description that includes objects present (e.g., 'a horse', 'a carriage'), the action being performed (e.g., 'pulling'), and the scene type (e.g., 'city street'). Which Azure Computer Vision capability should they use?

Accepted Answer

Image Analysis (Describe image). The Image Analysis 'Describe image' capability is designed to generate human-readable captions that summarize the content of an image, including objects, actions, and scene context. This directly matches the library's requirement to produce natural-language descriptions for historical photographs, as it uses a combination of object detection and scene understanding to output a full sentence.

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Optical Character Recognition (OCR)

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Object detection

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Face detection

Question 9

A hotel chain wants to automatically analyze guest reviews to identify the most frequently mentioned aspects of their stay, such as 'cleanliness', 'staff friendliness', or 'location'. They want to use a prebuilt Azure AI Language feature without custom training. Which feature should they use?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction is the correct choice because it is a prebuilt Azure AI Language feature designed to automatically identify and return the most salient words or phrases from unstructured text, such as 'cleanliness', 'staff friendliness', or 'location'. This feature requires no custom training and directly addresses the hotel chain's need to surface frequently mentioned aspects of guest reviews.

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Sentiment Analysis

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Entity Recognition

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Language Detection

Question 10

What is the Responsible AI principle most relevant to Azure AI Face's attribute prediction features?

Accepted Answer

Fairness and privacy — preventing bias across demographic groups and avoiding surveillance misuse. Azure AI Face's attribute prediction features (e.g., age, emotion, hair color) have been restricted or retired due to concerns about demographic bias and potential misuse for surveillance. The Responsible AI principle of Fairness and privacy directly addresses these issues by requiring that AI systems avoid bias across demographic groups and prevent applications like unauthorized tracking or profiling, which is why this principle is most relevant.

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Reliability — ensuring Face API returns consistent results across all images

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Inclusiveness — ensuring Face API works for users of all abilities

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Transparency — documenting how Face API determines attribute values

Question 11

What is 'people counting' in Azure AI Vision spatial analysis?

Accepted Answer

Using video AI to count people in zones for occupancy, footfall, and queue management. People counting in Azure AI Vision spatial analysis uses video AI to detect and track individuals within defined zones, enabling accurate measurement of occupancy, footfall, and queue lengths. This is a core computer vision capability that processes live or recorded video streams to count people in real time, supporting retail, workplace, and public safety scenarios.

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Counting how many different people have used a digital service over a time period

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Identifying and counting employees who have completed mandatory training

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Counting the number of faces detected in a photo album for tagging purposes

Question 12

A financial analyst uses Azure OpenAI Service to generate summaries of quarterly earnings reports. The analyst provides the raw text of the report in the prompt and wants the summary to stick strictly to the facts presented in that text, without adding any external information or speculation. Which technique should the analyst employ to minimize the risk of the model inventing information?

Accepted Answer

Use grounding by including the report text in the prompt and explicitly instructing the model to base the summary only on that text.. Grounding the model with the source text and explicitly instructing it to base the summary solely on that text is the most direct way to reduce hallucination. Azure OpenAI Service relies on the prompt for context; by providing the raw report and a strict instruction, the model is constrained to extract facts from the provided content rather than generating novel information.

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Set the temperature parameter to a high value.

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Set the frequency penalty to the maximum allowed value.

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Set the max_tokens parameter to a very small number.

Question 13

A data scientist has trained a binary classification model to predict whether an email is spam (positive) or not spam (negative). On a test set, the model correctly identifies 90 out of 100 actual spam emails and 80 out of 100 actual non-spam emails. Which metric shows the proportion of actual spam emails that the model correctly predicted?

Accepted Answer

B. Recall. Recall (also known as sensitivity or true positive rate) measures the proportion of actual positive cases that were correctly predicted by the model. In this scenario, the model correctly identified 90 out of 100 actual spam emails, so the recall is 90/100 = 0.9 (90%). This metric directly answers the question about how well the model captures actual spam emails.

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A. Precision

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C. F1 Score

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D. Accuracy

Question 14

What does Azure AI Speech service's text-to-speech (TTS) feature do?

Accepted Answer

Converts written text into natural-sounding spoken audio. Azure AI Speech service's text-to-speech (TTS) feature converts written text into natural-sounding spoken audio using neural voice models. It synthesizes speech from input text, enabling applications like voice assistants, audiobooks, and accessibility tools. This is the core function of TTS, distinct from speech-to-text or translation capabilities.

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Converts spoken audio into written text

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Identifies the language of spoken audio

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Translates spoken words from one language to another

Question 15

What is 'cross-lingual transfer learning' in multilingual NLP models?

Accepted Answer

Using shared multilingual representations so knowledge learned in one language transfers to others. Cross-lingual transfer learning leverages shared multilingual representations (e.g., from models like multilingual BERT or XLM-R) that encode multiple languages into a common semantic space. This allows knowledge learned from training data in one language (e.g., English) to improve performance on tasks in other languages without requiring labeled data for each target language. The model transfers understanding of syntax, semantics, and context across languages because it was pre-trained on a diverse corpus of many languages simultaneously.

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Automatically translating training data from English to other languages before fine-tuning

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Using the same model for both NLP and computer vision tasks

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Transferring a model trained in Azure to run on another cloud provider

Question 16

A company implements an AI system to monitor employee productivity by tracking keystrokes and mouse movements. Employees are not informed that this monitoring is taking place, nor did they consent to it. Which Microsoft responsible AI principle is most directly violated?

Accepted Answer

Privacy & Security. The scenario describes monitoring employee keystrokes and mouse movements without their knowledge or consent. This directly violates the Privacy & Security principle, which requires that individuals have control over their personal data and that data collection is transparent and consensual. Microsoft's responsible AI framework mandates that AI systems must respect privacy and obtain informed consent before collecting or using personal data.

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Fairness

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Reliability & Safety

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Inclusiveness

Question 17

What is a 'bot' in Microsoft Azure Bot Service?

Accepted Answer

A conversational AI application that interacts with users through natural language in messaging channels. A 'bot' in Microsoft Azure Bot Service is a conversational AI application designed to interact with users through natural language across various messaging channels like Microsoft Teams, Slack, or Facebook Messenger. It leverages the Bot Framework SDK and Azure AI services (e.g., LUIS, QnA Maker) to understand user intent and provide responses, enabling human-like dialogue without requiring a custom interface for each channel.

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An automated script that performs repetitive tasks in the Azure portal

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A security tool that detects automated bot traffic attempting to misuse an API

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An autonomous AI agent that independently manages Azure cloud infrastructure

Question 18

A company wants to implement an AI solution that treats all users fairly regardless of their background. Which Microsoft responsible AI principle does this requirement primarily address?

Accepted Answer

Fairness. The requirement to treat all users fairly regardless of background directly aligns with the Fairness principle, which mandates that AI systems should allocate outcomes, opportunities, or resources equitably and avoid discrimination based on sensitive attributes such as race, gender, or age. In Azure AI, this is operationalized through tools like Fairlearn and the Responsible AI dashboard, which assess and mitigate bias in model predictions. The other principles address different concerns: Privacy focuses on data protection, Inclusiveness on accessibility for diverse abilities, and Transparency on explainability of decisions.

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Privacy

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Inclusiveness

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Transparency

Question 19

What is 'model confidence score' in Azure Custom Vision predictions?

Accepted Answer

A per-prediction certainty measure indicating how sure the model is about a specific classification. In Azure Custom Vision, the model confidence score is a per-prediction value (ranging from 0 to 1) that quantifies the model's certainty that a given input image belongs to a specific class. It is computed during inference based on the probability distribution output by the trained classifier, not during training. This score helps users decide whether to accept or reject a prediction based on a custom threshold.

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The percentage of training images the model correctly labelled during training

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A rating of the training data quality provided by the annotation team

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Microsoft's certification level for how well a Custom Vision model meets enterprise standards

Question 20

A data scientist is training a regression model to predict energy consumption. The dataset includes features like temperature, humidity, time of day, and day of week. After training, the model performs well on the training set but poorly on new data. Which approach would most likely help reduce this problem?

Accepted Answer

Use a simpler model with fewer parameters.. The model performs well on the training set but poorly on new data, which is classic overfitting. Using a simpler model with fewer parameters reduces the model's capacity to memorize noise and irrelevant patterns, forcing it to learn the underlying generalizable relationships. This directly addresses the variance problem without requiring additional data or computational resources.

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Add more features to the model.

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Increase the number of training epochs.

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Use a more complex model to capture more patterns.