AI-900 Practice Test 42 — 15 Questions

Question 1

A financial institution uses an AI system to recommend credit limits for new customers. When a customer is declined for a credit limit increase, the customer asks why, but the institution cannot provide any explanation because the model is a complex deep neural network and the decision-making process is opaque. Which Microsoft responsible AI principle is most directly violated?

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

Accepted Answer

Transparency. Transparency. The scenario describes a deep neural network that cannot explain its decision to deny a credit limit increase, which directly violates the transparency principle. Microsoft's responsible AI principle of transparency requires that AI systems be understandable and that their decisions can be explained to users, especially when those decisions have significant impact on individuals.

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Reliability and safety

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Fairness

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Privacy and security

Question 2

A manufacturer trains a model to detect defective parts on an assembly line. Only 2% of parts are defective. The model predicts 'non-defective' for all parts and achieves 98% accuracy. Which metric best reveals the model's inability to identify defective parts?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual defective parts correctly identified by the model. With 98% accuracy but zero true positives (since the model labels everything as non-defective), recall is 0%, which directly exposes the model's failure to detect any defective parts despite high accuracy.

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Accuracy

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Precision

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F1 Score

Question 3

A hospital deploys an AI system to assist doctors in interpreting MRI scans. The system highlights the regions of interest and provides a numeric confidence score for its findings, along with a list of the image features that contributed to the diagnosis. Which responsible AI principle is being applied?

Accepted Answer

Transparency. The system provides a numeric confidence score and a list of image features that contributed to the diagnosis, which directly supports the principle of Transparency. Transparency in responsible AI requires that AI systems are understandable and that their decisions can be explained to users, enabling clinicians to interpret and trust the output.

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Fairness

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Privacy

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Accountability

Question 4

What is 'object detection' in computer vision and how does it differ from image classification?

Accepted Answer

Object detection locates each object with a bounding box and class label; classification labels the whole image. Object detection goes beyond image classification by not only identifying the class of objects present but also localizing each one with a bounding box. In contrast, image classification assigns a single label to the entire image, regardless of how many objects are present. This distinction is fundamental in computer vision workloads on Azure, where Custom Vision and Computer Vision API offer both capabilities.

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Object detection and image classification produce the same output — both label the entire image

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Image classification processes images faster than object detection because it is simpler

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Object detection only works on images with a single object; classification handles multiple objects

Question 5

A development team creates an AI chatbot for a hospital website that answers patient queries. The team scripts the AI to always respond with a disclaimer that it is not a substitute for professional medical advice. Additionally, they include a mechanism for users to report inaccurate responses, which are then reviewed by a human team. Which Microsoft responsible AI principle is most directly being implemented by the reporting and human review mechanism?

Accepted Answer

Accountability. The reporting and human review mechanism directly implements the Accountability principle, which requires that AI systems be designed with clear lines of responsibility and oversight. By allowing users to flag inaccuracies and having a human team review those reports, the organization takes ownership of the system's outputs and ensures corrective actions can be taken. This goes beyond mere transparency or reliability—it establishes a feedback loop where humans remain ultimately responsible for the AI's behavior.

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Fairness

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Reliability and safety

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Transparency

Question 6

An online retailer wants to build a recommendation system that learns from user interactions. The system suggests a product, and if the user clicks it, it receives a positive reward; if ignored, a negative reward. Over time, the system learns to make better suggestions. Which type of machine learning best describes this approach?

Accepted Answer

Reinforcement learning. Reinforcement learning is correct because the system learns by interacting with its environment (user clicks) and receiving rewards (positive for clicks, negative for ignores) to maximize cumulative reward over time. This trial-and-error feedback loop, without explicit labeled data, is the hallmark of reinforcement learning.

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Supervised learning

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Unsupervised learning

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Semi-supervised learning

Question 7

What is 'top_p' (nucleus sampling) in Azure OpenAI and how does it differ from temperature?

Accepted Answer

Restricting token selection to those whose cumulative probability reaches p — an alternative diversity control to temperature. Top_p (nucleus sampling) in Azure OpenAI controls diversity by selecting tokens from the smallest set whose cumulative probability exceeds the threshold p, rather than sampling from the full probability distribution. This differs from temperature, which scales the logits before the softmax to flatten or sharpen the distribution; top_p dynamically cuts off the long tail of low-probability tokens, providing an alternative method to control randomness without affecting the relative ranking of high-probability tokens.

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The maximum percentage of the context window used for generating output

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The probability threshold above which the model considers a response correct

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A parameter setting the minimum confidence before the model outputs a response

Question 8

A restaurant chain wants to build a voice-powered ordering system for its drive-through. The system must understand when a user wants to place an order, modify an existing order, or cancel an order. It also needs to extract specific details like the menu item name and quantity from the user's speech. Which Azure AI Language feature should they use to handle both intent recognition and entity extraction?

Accepted Answer

Conversational Language Understanding (CLU). Conversational Language Understanding (CLU) is the correct choice because it is specifically designed to handle both intent recognition (e.g., 'place order', 'modify order', 'cancel order') and entity extraction (e.g., menu item name, quantity) from natural language utterances. CLU uses a pre-built or custom model to map user input to intents and extract detailed entities, making it ideal for a voice-powered ordering system that needs to understand complex commands.

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Custom text classification

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Key phrase extraction

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Question answering

Question 9

What is 'text moderation' in Azure AI Content Safety for NLP workloads?

Accepted Answer

AI analysis of text for hate, violence, sexual, and self-harm content with per-category severity scores. Text moderation in Azure AI Content Safety uses trained NLP models to analyze text and assign severity scores (0-7) for four harm categories: hate, violence, sexual, and self-harm. This allows content filtering based on policy thresholds, not simple keyword matching. Option B correctly describes this AI-driven analysis with per-category severity scoring.

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Grammatically correcting and editing text for quality before publishing

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Moderating the volume of text content uploaded by users to prevent spam

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Identifying and removing personally identifiable information from text before processing

Question 10

A retail company wants to use Azure Computer Vision to monitor product availability on shelves. They need to detect the presence and location of any product (e.g., a box, a bottle) on a shelf image, but they do not need to identify the specific product brand or type. Which prebuilt Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct choice because it identifies and locates multiple objects within an image by drawing bounding boxes around each detected item. For monitoring product availability on shelves, the company needs to know both the presence and position of products (e.g., boxes, bottles) without identifying specific brands or types, which aligns exactly with object detection's capability to output class labels (e.g., 'product') and coordinates.

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Image classification

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Optical Character Recognition (OCR)

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Semantic segmentation

Question 11

What is the difference between AI and conventional programming?

Accepted Answer

AI learns rules from data; conventional programming requires explicit rule specification. The fundamental distinction between AI and conventional programming lies in how rules are derived. In conventional programming, developers explicitly code every rule and logic path (e.g., if-then-else statements). In AI, particularly machine learning, the system learns patterns and rules directly from labeled or unlabeled data through training algorithms, without being explicitly programmed for each scenario. This enables AI to handle complex tasks like image recognition or natural language understanding where manual rule specification is impractical.

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AI runs faster than conventional programs

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AI can only work with images; conventional programming works with all data types

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Conventional programming is more accurate than AI

Question 12

What is 'healthcare AI' and what capabilities does Azure provide for it?

Accepted Answer

AI for extracting medical entities, radiology insights, clinical trial matching, and patient analysis. Healthcare AI refers to AI solutions tailored for the healthcare industry, and Azure provides specific capabilities such as extracting medical entities (e.g., symptoms, medications) via Azure Health Bot and Text Analytics for Health, analyzing radiology images with Azure AI Vision, matching patients to clinical trials using Azure Cognitive Services, and performing patient analysis with Azure Machine Learning. These capabilities support clinical decision-making and operational efficiency without replacing doctors.

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AI that gives patients direct medical advice as a substitute for doctors

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A hospital management system for scheduling, billing, and patient record management

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AI that monitors patients' vitals in real time using IoT medical devices

Question 13

A retail chain uses ceiling-mounted cameras to monitor shelf inventory. They need to identify and locate individual products (e.g., a specific brand of cereal) within an image and count how many are present. Which Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct capability because it not only identifies the presence of a specific product (e.g., a brand of cereal) within an image but also localizes each instance by drawing bounding boxes around them, enabling an accurate count. Image classification would only label the entire image as containing cereal without locating individual boxes, while OCR and semantic segmentation serve different purposes (text extraction and pixel-level labeling, respectively).

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Image classification

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Optical character recognition (OCR)

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Semantic segmentation

Question 14

A company uses Azure OpenAI Service to generate creative product descriptions. They want to increase the randomness and variety of the generated outputs to produce more diverse suggestions. Which parameter should they increase?

Accepted Answer

Temperature. Temperature controls the randomness of the model's output by scaling the logits before applying the softmax function. Increasing temperature (e.g., from 0.7 to 1.0) flattens the probability distribution, making lower-probability tokens more likely to be chosen, which increases diversity and creativity in generated text.

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Top_p

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Frequency penalty

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Presence penalty

Question 15

A company builds an AI system to filter job applications and rank candidates. The system is trained on historical hiring data. To reduce potential bias, the company removes protected attributes such as gender and ethnicity from the training data. However, after deployment, the system still shows a statistically significant bias against female candidates. Which Microsoft responsible AI principle most directly requires the company to investigate and address this remaining bias, even when protected attributes are removed?

Accepted Answer

Fairness. The Fairness principle requires AI systems to treat all people fairly and avoid creating or reinforcing discriminatory outcomes. Even when protected attributes like gender are removed from training data, bias can persist through proxy variables (e.g., zip code, education history) that correlate with protected attributes. The company must investigate and mitigate this remaining bias because Fairness mandates proactive assessment and correction of disparate impact, not just removal of obvious features.

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Inclusiveness

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Reliability and safety

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Transparency