AI-900 Practice Test 47 — 15 Questions

Question 1

A healthcare company deploys an AI system to assist doctors in diagnosing skin conditions from images. The system is a deep neural network that does not provide explanations for its predictions. The company implements a process where every AI recommendation is logged, and a medical team reviews any adverse outcomes to determine if the system or a human made an error. The company also clearly assigns responsibility for the system's outputs to a specific clinical oversight committee. Which Microsoft responsible AI principle is most directly being implemented by these actions?

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

Accepted Answer

Accountability. The actions described—logging AI recommendations, reviewing adverse outcomes to determine error source, and assigning a clinical oversight committee—directly implement the Accountability principle. Accountability requires that organizations clearly assign responsibility for AI system outputs and have processes to review and address impacts, which is exactly what the company does by creating a human review loop and a designated committee.

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Fairness

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Reliability and safety

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Transparency

Question 2

What is 'AI inclusiveness' in Microsoft's Responsible AI principles?

Accepted Answer

Ensuring AI systems empower and benefit all people including those with disabilities and diverse demographics. Microsoft's Responsible AI principle of inclusiveness requires that AI systems are designed to empower and benefit all people, including those with disabilities and diverse demographics. This ensures that AI technologies do not discriminate or exclude groups based on ability, culture, or socioeconomic status, aligning with Microsoft's commitment to fairness and accessibility in AI.

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Including all team members in the AI development process regardless of technical skill

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Making AI models available to all organisations regardless of their budget

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Including diverse training data sources to improve model accuracy

Question 3

What is 'AI in HR' (Human Resources) and what specific applications does it enable?

Accepted Answer

CV screening, employee sentiment, attrition prediction, skills analysis, and learning recommendations. AI in HR leverages machine learning and natural language processing to automate and enhance tasks like CV screening (e.g., parsing resumes for relevant skills), employee sentiment analysis (e.g., using NLP on survey responses), attrition prediction (e.g., classification models on historical data), skills gap analysis, and personalized learning recommendations. These applications augment human decision-making rather than replacing it, aligning with common AI workloads in the HR domain.

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Using AI to fully automate all HR decisions without human involvement

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Managing employee payroll and benefits calculations using traditional database systems

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Ensuring HR documents comply with employment law using rule-based systems

Question 4

A retail company uses an AI system to predict customer churn based on demographic and behavioral data. The team discovers that the model gives disproportionately higher churn predictions for customers from a particular zip code, even when their behavior is similar to others. Which Microsoft responsible AI principle is most directly relevant to addressing this issue?

Accepted Answer

Fairness. The model's disproportionate churn predictions for a specific zip code, despite similar behavior, indicates a bias that unfairly impacts that group. Microsoft's Fairness principle directly addresses this by requiring AI systems to treat all groups equitably and avoid discrimination based on sensitive attributes like location. Ensuring fairness involves auditing training data and model outputs for such disparities and applying mitigation techniques.

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Transparency

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Reliability and Safety

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Privacy and Security

Question 5

What is 'orchestration workflow' in Azure AI Language for multi-skill scenarios?

Accepted Answer

A single entry point that routes queries to the appropriate language skill based on intent. In Azure AI Language, the orchestration workflow is a feature that allows you to connect multiple language skills (such as custom question answering, conversational language understanding, and LUIS) into a single endpoint. It acts as a router that analyzes the user's query intent and then directs the request to the most appropriate skill for processing, enabling multi-skill scenarios without requiring separate endpoints or manual routing logic.

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Scheduling language model inference jobs to run at off-peak hours

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Automating the data labelling workflow for custom NLP model training

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Managing the sequence of preprocessing steps applied to text before model inference

Question 6

A data scientist is developing a classification model to detect fraudulent transactions. The dataset is split into training and test sets. The data scientist repeatedly tunes the model's hyperparameters and evaluates performance on the test set until the test accuracy reaches 95%. However, when the model is deployed on new, unseen data, its accuracy drops to 70%. Which concept best explains this performance degradation?

Accepted Answer

Overfitting to the test set. The data scientist repeatedly tuned hyperparameters based on test set performance, effectively using the test set as part of the training process. This causes the model to become specialized to the test set's specific patterns and noise, so it fails to generalize to new, unseen data. This phenomenon is known as overfitting to the test set, where the test set no longer provides an unbiased estimate of real-world performance.

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Overfitting to the training data

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Data leakage from the training set to the test set

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Underfitting the training data

Question 7

A hospital uses an AI system to recommend patient treatment plans. A doctor questions why the system recommended a specific treatment for a particular patient. Which Microsoft responsible AI principle is most directly relevant to providing the answer?

Accepted Answer

Transparency. Transparency is the responsible AI principle that requires AI systems to be understandable and interpretable by humans. When a doctor questions why a specific treatment was recommended, the system must be able to provide an explanation of its reasoning, such as which patient features (e.g., lab results, medical history) most influenced the recommendation. This aligns with the need for explainability in AI, enabling clinicians to trust and validate the model's output.

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Fairness

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Reliability and Safety

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Privacy and Security

Question 8

What is the Azure AI Custom Vision portal used for?

Accepted Answer

Training and evaluating custom image classification and object detection models without code. The Azure AI Custom Vision portal is a no-code web interface that allows users to upload images, label them, and train custom image classification or object detection models. It abstracts away the underlying machine learning code, making it accessible for non-developers to build and evaluate computer vision models tailored to their specific use cases.

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Managing Azure subscription billing for AI services

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Building chatbots using natural language understanding

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Monitoring the health of deployed AI services

Question 9

A data scientist wants to train a machine learning model to predict the exact market price of a house based on features such as square footage, number of bedrooms, and location. Which type of machine learning task should be used?

Accepted Answer

Regression. Predicting the exact market price of a house is a regression task because the target variable (price) is a continuous numeric value. Regression algorithms, such as linear regression or decision tree regression, learn the relationship between input features (e.g., square footage, bedrooms, location) and a continuous output. In Azure Machine Learning, you would select a regression model from the designer or AutoML to solve this problem.

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Classification

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Clustering

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Anomaly Detection

Question 10

What is 'credit scoring' as an AI workload and what responsible AI concerns does it raise?

Accepted Answer

ML for predicting loan repayment risk — with fairness, bias, and explainability concerns. Credit scoring in AI refers to machine learning models that predict the likelihood of a borrower repaying a loan. This raises responsible AI concerns around fairness (e.g., models may discriminate against protected groups), bias (e.g., training data may reflect historical inequalities), and explainability (e.g., complex models like gradient-boosted trees are often black boxes, making it hard to justify decisions to regulators or customers).

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A system for automatically assigning credit scores to software bugs in a development backlog

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Monitoring whether a customer has used all their credit within an approved limit

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An internal system for scoring the quality of AI projects within an organisation

Question 11

What is 'zero-shot object detection' in computer vision?

Accepted Answer

Detecting objects described in text without any training examples of that specific class. Zero-shot object detection refers to a model's ability to detect objects in images based on a textual description of the target class, without having been trained on any labeled examples of that specific class. This is achieved by leveraging a joint embedding space where visual features and text features are aligned, allowing the model to generalize to unseen categories at inference time.

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Object detection that runs with zero latency for real-time applications

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Detection that works on black and white images (zero colour channels)

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An object detection model with zero false positives on the test set

Question 12

What is the purpose of Azure AI Language's 'personally identifiable information (PII) detection' feature?

Accepted Answer

Identifying and extracting personal information (names, addresses, ID numbers) from text for redaction. Azure AI Language's PII detection feature is designed to identify and extract personally identifiable information such as names, addresses, phone numbers, and ID numbers from unstructured text. This allows organizations to redact or mask sensitive data before further processing or storage, helping to comply with privacy regulations like GDPR and HIPAA.

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Generating fake personal information for testing applications

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Verifying the identity of users accessing AI services

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Encrypting personal information stored in databases

Question 13

What is the role of intents in conversational language understanding (CLU)?

Accepted Answer

Intents represent the user's goal or desired action, determining how the bot should respond. In conversational language understanding (CLU), intents represent the user's goal or desired action, such as booking a flight or checking the weather. They map user utterances to specific tasks the bot should perform, enabling the model to classify input and trigger appropriate responses. This is distinct from entities (which extract data) or responses (which are outputs).

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Intents are the specific pieces of information extracted from user messages (dates, amounts, names)

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Intents are the predefined bot responses stored in a knowledge base

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Intents represent the confidence level of a bot's understanding

Question 14

What is the role of a validation dataset in machine learning?

Accepted Answer

To tune hyperparameters and monitor performance during training without using test data. The validation dataset is used during model training to tune hyperparameters and monitor performance on unseen data, preventing overfitting without contaminating the test set. This allows iterative adjustments to model architecture or learning rate while keeping the test data reserved for final evaluation.

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To provide the primary examples for training the model's weights

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To provide the final, unbiased assessment of model performance

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To store the model's trained weights for later use

Question 15

A retail warehouse uses a camera system to locate and count boxes on shelves. The system needs to output the exact positions of each box by drawing a rectangular frame around it in the image. Which Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct capability because it identifies and localizes multiple objects within an image by drawing bounding boxes around each detected instance. In this scenario, the system needs to locate and count individual boxes on shelves, which requires both classification (what is a box) and localization (where each box is), exactly what object detection provides.

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Image classification

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Semantic segmentation

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Optical Character Recognition (OCR)