AI-900 Practice Test 43 — 15 Questions

Question 1

A company uses Azure OpenAI Service to generate marketing copy. They notice that sometimes the generated text contains repetitive phrases or gets stuck in loops. They want to reduce this behavior without changing the overall creativity of the model. Which parameter should they adjust?

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

Accepted Answer

Increase the frequency_penalty parameter.. Increasing the frequency_penalty parameter reduces the likelihood of the model repeating the same phrases by penalizing tokens that have already appeared in the generated text. This directly addresses the repetitive loops without altering the overall creativity, as frequency_penalty specifically targets token frequency rather than randomness or diversity.

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Decrease the temperature parameter.

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Increase the presence_penalty parameter.

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Decrease the top_p parameter.

Question 2

A travel booking website wants to automatically identify famous landmarks (e.g., Eiffel Tower, Taj Mahal) in photos uploaded by users. They want to use a prebuilt Azure Computer Vision feature without custom training. Which capability should they use?

Accepted Answer

Domain-specific models (Landmark detection). Azure Computer Vision includes prebuilt domain-specific models for landmark detection that can identify famous landmarks like the Eiffel Tower or Taj Mahal without any custom training. This capability is specifically designed to recognize well-known structures from user-uploaded photos, making it the ideal choice for the travel booking website's requirement.

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Image classification

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Optical character recognition (OCR)

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Object detection

Question 3

What is 'zero-shot prompting' and how does it work?

Accepted Answer

Asking the model to perform a task without any examples, relying on pre-trained knowledge. Zero-shot prompting refers to instructing a generative AI model to perform a task without providing any examples in the prompt. The model relies entirely on its pre-trained knowledge—gained from vast datasets during training—to interpret the instruction and generate a relevant response. This is a core capability of large language models (LLMs) like GPT-4, enabling them to generalize to unseen tasks without task-specific fine-tuning.

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Running the model for zero seconds to test if the API connection works

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Prompting the model to generate a response with zero errors or hallucinations

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A technique that removes all instructions from the prompt to test raw model behaviour

Question 4

A real estate company trains a model to predict house prices. They evaluate it on a test set of 100 houses. The model predictions have a mean absolute error (MAE) of $5,000 and a root mean squared error (RMSE) of $20,000. What does the large difference between MAE and RMSE indicate about the model's errors?

Accepted Answer

The model has many small errors and a few large errors.. The mean absolute error (MAE) of $5,000 and root mean squared error (RMSE) of $20,000 show a large discrepancy because RMSE squares errors before averaging, which heavily penalizes large deviations. Since RMSE is four times larger than MAE, this indicates that while most predictions are close (small errors), there are a few predictions with very large errors that inflate the RMSE. This pattern is classic for a model that performs well on most houses but fails badly on a few outliers.

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The model consistently overestimates prices.

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The model has a high bias and low variance.

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The model is perfectly accurate.

Question 5

A travel agency wants to build a chatbot that can automatically answer customer questions about flight status by extracting answers from a PDF document containing FAQs. Which Azure AI Language feature should they use to directly query this content?

Accepted Answer

Question Answering. Question Answering, is correct because it is specifically designed to extract answers directly from a provided document (such as a PDF FAQ) by using a pre-built or custom knowledge base. The travel agency can upload the PDF, and the service will return precise answers to user queries without requiring intent classification or entity extraction, which is exactly what is needed for querying flight status from a static FAQ document.

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Conversational Language Understanding (CLU)

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Text Analytics for health

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Translator

Question 6

A real estate company has a dataset containing square footage, number of bedrooms, and location for 10,000 houses, along with their sale prices. They want to train a model that predicts the sale price of a new house based on these features. Which type of machine learning should they use?

Accepted Answer

Supervised regression. The goal is to predict a continuous numeric value (sale price) from input features (square footage, bedrooms, location). This is a classic supervised regression problem because the training data includes labeled target values (prices) and the output is a real number, not a category.

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Supervised classification

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Unsupervised clustering

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Reinforcement learning

Question 7

A retail company wants to automatically analyze in-store video footage to count the number of customers entering and exiting through different doors. Which Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct capability because it can identify and locate multiple instances of people within a video frame, drawing bounding boxes around each person. This allows the system to track individuals across frames and count them as they cross virtual lines at doorways, distinguishing between entering and exiting movements. Optical Character Recognition (OCR), image classification, and face detection lack the spatial localization and multi-instance tracking required for this specific counting task.

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Optical Character Recognition (OCR)

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Image classification

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Face detection

Question 8

What is opinion mining (also called aspect-based sentiment analysis) in Azure AI Language?

Accepted Answer

Identifying sentiment toward specific aspects or topics mentioned in text. Opinion mining, also known as aspect-based sentiment analysis, in Azure AI Language goes beyond general sentiment to identify sentiment (positive, negative, neutral, or mixed) toward specific aspects or topics mentioned in the text. For example, in a product review like 'The battery life is great but the screen is too dim,' it would detect positive sentiment toward 'battery life' and negative sentiment toward 'screen.' Option B correctly captures this core functionality.

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Identifying who expressed an opinion in a text

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Translating opinions from one language to another

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Detecting politically biased content in news articles

Question 9

What does the responsible AI principle of 'human in the loop' refer to?

Accepted Answer

Maintaining human oversight and the ability to review or override consequential AI decisions. The 'human in the loop' principle ensures that humans maintain meaningful oversight over AI systems, particularly for high-stakes or consequential decisions. This means humans can review, override, or intervene in AI-generated outputs, preventing fully automated decision-making in critical scenarios such as medical diagnosis, loan approvals, or criminal justice. It is a core component of responsible AI, balancing automation with accountability.

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A requirement for humans to manually enter all data into AI systems

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Training AI models using feedback from human labelers only

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Requiring users to prove they are human before using AI services

Question 10

What is the difference between Azure AI Vision and Azure AI Custom Vision?

Accepted Answer

Azure AI Vision offers pre-built models; Custom Vision trains custom models on your labeled images. Azure AI Vision provides pre-built, ready-to-use models for common computer vision tasks like image analysis, OCR, and facial recognition, requiring no training data. Azure AI Custom Vision, on the other hand, allows you to train custom models using your own labeled images to solve specific classification or object detection problems. This distinction makes option B correct because it accurately captures the core difference between a pre-built service and a customizable training platform.

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Azure AI Vision is faster; Custom Vision is more accurate

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Azure AI Vision analyzes only photos; Custom Vision analyzes documents

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They are different names for the same service

Question 11

What is 'Microsoft Copilot Studio' and what is it used for?

Accepted Answer

A low-code platform for building custom AI bots and agents integrated with Microsoft 365. Microsoft Copilot Studio is a low-code platform that allows users to build custom AI-powered bots and agents that integrate seamlessly with Microsoft 365 services. It extends the capabilities of Microsoft Copilot by enabling tailored conversational experiences, such as automating workflows, answering queries, and handling tasks within the Microsoft ecosystem without requiring extensive coding.

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A professional audio/video editing suite powered by AI for content creators

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An IDE for enterprise developers building high-performance LLM applications in C#

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A tool for generating Copilot-branded marketing content for Microsoft partners

Question 12

What is 'intent recognition' in the context of Azure AI Language and conversational AI?

Accepted Answer

Determining the user's goal or purpose from their natural language input to route conversation logic. Intent recognition in Azure AI Language and conversational AI is the process of mapping a user's natural language input to a specific goal or purpose, such as 'book a flight' or 'check weather'. This allows the system to route the conversation logic to the appropriate handler or dialog flow. Option B correctly defines this core function, distinguishing it from simpler pattern matching or sentiment analysis.

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Recognising when a user intends to cancel their subscription during a chat session

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Detecting the emotional intention behind a user's message for sentiment classification

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Verifying that the user's stated intent matches their historical behaviour in the application

Question 13

A marketing team wants to use Azure OpenAI to generate blog posts. They require the output to avoid toxic language and adhere to their brand safety guidelines. Which Azure OpenAI feature should they configure to automatically block harmful content?

Accepted Answer

Content filters. Content filters in Azure OpenAI are designed to automatically detect and block harmful content, including toxic language, hate speech, and violence, based on configurable severity levels. This feature directly addresses the marketing team's requirement to enforce brand safety guidelines by filtering out undesirable outputs before they are returned to the user.

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Grounding

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Temperature

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Few-shot learning

Question 14

What is 'AI reliability and safety' in Microsoft's Responsible AI principles?

Accepted Answer

AI performing consistently and safely across diverse conditions, with fail-safes and human oversight. B is correct because 'AI reliability and safety' in Microsoft's Responsible AI principles focuses on ensuring AI systems perform consistently and safely across diverse conditions, with built-in fail-safes and human oversight. This principle addresses the need for AI to handle edge cases, adversarial inputs, and unexpected scenarios without causing harm, aligning with Microsoft's commitment to trustworthy AI.

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Ensuring Azure AI infrastructure has 99.9% uptime SLA guarantees

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Using safety-certified AI models that have passed ISO security standards

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AI that passes software quality assurance testing before being deployed

Question 15

A security system uses cameras to detect whether a person is present at a restricted door. Which Azure Computer Vision capability should they use to detect the presence of human faces in the camera images?

Accepted Answer

Face Detection. Face Detection is the correct choice because it is specifically designed to locate and identify human faces in images, returning bounding box coordinates for each detected face. This capability directly addresses the requirement to detect whether a person is present at a restricted door by identifying faces in camera images, without needing to recognize who the person is.

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Optical Character Recognition (OCR)

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Object Detection

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Image Classification