AI-900 Practice Test 13 — 15 Questions

Question 1

What is 'spatial analysis' in Azure AI Vision?

Accepted Answer

Analysing video to understand people's movements and interactions within physical spaces. Spatial analysis in Azure AI Vision uses video analytics to detect and track people in a physical space, analyzing their movements, positions, and interactions over time. It leverages computer vision models to understand spatial relationships and patterns, such as how people move through a store or queue at a counter.

Answer

Analysing the geographic distribution of Azure data centres globally

Answer

Mapping pixels in an image to three-dimensional coordinates

Answer

Categorising images by their physical dimensions and file size

Question 2

A company deploys an AI system to screen job applications. The system is a complex neural network that learns patterns from historical hiring data. A rejected candidate asks for an explanation, but the development team cannot describe how the decision was reached. Which Microsoft responsible AI principle is most directly violated?

Accepted Answer

Transparency. The system's inability to explain how it reached a decision violates the transparency principle, which requires AI systems to be understandable and interpretable. Complex neural networks often act as black boxes, making it impossible to provide meaningful explanations to users, directly contradicting Microsoft's responsible AI guideline that decisions should be explainable.

Answer

Fairness

Answer

Privacy and security

Answer

Reliability and safety

Question 3

A robotics company is training a drone to fly autonomously through an obstacle course. The drone receives positive rewards for staying on course and avoiding obstacles, and negative rewards for collisions. The system learns by trial and error to maximize its cumulative reward. Which type of machine learning is being used?

Accepted Answer

Reinforcement learning. Reinforcement learning is the correct choice because the drone learns by interacting with its environment, receiving rewards (positive for staying on course, negative for collisions), and adjusting its behavior through trial and error to maximize cumulative reward. This is the defining characteristic of reinforcement learning, where an agent learns a policy from feedback signals rather than from labeled data or hidden patterns.

Answer

Supervised learning

Answer

Unsupervised learning

Answer

Semi-supervised learning

Question 4

A self-driving car company develops an AI system that is highly accurate in testing but fails to consistently detect pedestrians during heavy rain. Which Microsoft responsible AI principle is most directly violated?

Accepted Answer

Reliability and safety. The system fails to consistently detect pedestrians during heavy rain, which is a failure of the AI to perform reliably under real-world conditions. Microsoft's 'Reliability and safety' principle requires AI systems to operate dependably and safely across all expected scenarios, including edge cases like adverse weather. This directly violates that principle because the system's accuracy drops in a common environmental condition, posing safety risks.

Answer

Fairness

Answer

Privacy and security

Answer

Transparency

Question 5

What is the purpose of Azure Machine Learning's automated ML (AutoML) feature?

Accepted Answer

To automatically try multiple algorithms and hyperparameters to find the best model. Azure Machine Learning's automated ML (AutoML) feature automates the process of algorithm selection and hyperparameter tuning. It iterates through various machine learning algorithms and their hyperparameter combinations, evaluating each based on a primary metric (e.g., accuracy, AUC_weighted) to identify the best-performing model for the given dataset and task (classification, regression, or forecasting). This significantly reduces the manual effort and time required for model development.

Answer

To automatically collect and label training data

Answer

To automatically deploy trained models to production

Answer

To automatically monitor models for performance degradation

Question 6

A bank uses a machine learning model to predict credit card fraud. The model's output is a probability score. The business wants to minimize the number of false positives (legitimate transactions incorrectly flagged as fraud) because these cause customer dissatisfaction. At the same time, they must also catch most fraudulent transactions. Which metric should the bank optimize to balance these two goals?

Accepted Answer

D: F1 score. The F1 score is the harmonic mean of precision and recall, making it the ideal metric when a balance between minimizing false positives (precision) and catching most fraudulent transactions (recall) is required. In this credit card fraud detection scenario, optimizing F1 ensures the model reduces customer dissatisfaction from false positives while still maintaining high detection of actual fraud.

Answer

A: Accuracy

Answer

B: Precision

Answer

C: Recall

Question 7

What does 'model accuracy' measure in machine learning classification?

Accepted Answer

The proportion of correct predictions out of total predictions. Model accuracy in classification measures the ratio of correctly predicted instances to the total number of predictions made. It is calculated as (True Positives + True Negatives) / (Total Predictions). This metric is fundamental in evaluating classification models on Azure Machine Learning, where it is reported in the model evaluation metrics.

Answer

How quickly the model makes predictions

Answer

How much memory the model uses during inference

Answer

The number of training examples used to build the model

Question 8

A logistics company needs to automatically extract printed and handwritten text from scanned shipping labels. Which Azure Computer Vision capability should they use?

Accepted Answer

Azure Computer Vision Read API. The Azure Computer Vision Read API is specifically designed to extract printed and handwritten text from images and documents, such as scanned shipping labels. It uses optical character recognition (OCR) to process text in various languages and formats, making it the correct choice for this logistics scenario.

Answer

Azure Face API

Answer

Azure Custom Vision

Answer

Azure Video Indexer

Question 9

What is the purpose of image 'ground truth' in training computer vision models?

Accepted Answer

The verified, accurate labels or annotations for training images that the model learns to predict. In computer vision, 'ground truth' refers to the verified, accurate labels or annotations for training images. The model uses these correct labels during supervised learning to learn the mapping from image features to outputs, enabling it to make accurate predictions on new, unseen data.

Answer

The physical location where training images were captured

Answer

The minimum image resolution required for accurate model training

Answer

The baseline accuracy of a computer vision model before fine-tuning

Question 10

Which of the following is a consideration for responsible AI regarding fairness?

Accepted Answer

AI systems should not perpetuate or amplify societal biases against specific groups. Fairness in responsible AI means that AI systems should be designed and tested to avoid perpetuating or amplifying societal biases against specific groups. This involves careful data selection, bias detection, and mitigation techniques to ensure equitable outcomes across different demographics.

Answer

AI systems should run as fast as possible regardless of accuracy

Answer

AI systems should be available 24/7 without any downtime

Answer

AI systems should always produce the same output for the same input

Question 11

A medical transcription service wants to automatically identify and extract medical terms such as diagnoses, medications, and procedures from doctor's notes. The notes are unstructured text. They want to use a pre-trained Azure AI Language feature that can understand medical terminology. Which feature should they use?

Accepted Answer

Named Entity Recognition (NER) for healthcare. B is correct because the medical transcription service needs to extract specific medical entities (diagnoses, medications, procedures) from unstructured doctor's notes. Azure AI Language's Named Entity Recognition (NER) for healthcare is a pre-trained model specifically designed to identify and categorize medical terminology, including conditions, treatments, and medications, directly from clinical text without requiring custom training.

Answer

Custom Text Classification

Answer

Key Phrase Extraction

Answer

Sentiment Analysis

Question 12

A logistics company needs to automatically read handwritten addresses from package labels using cameras on a conveyor belt. The handwriting varies greatly in style, size, and orientation. Which Azure Computer Vision capability should they use?

Accepted Answer

OCR (Read API). The OCR (Read API) is specifically designed to extract text from images, including handwritten text, and is optimized for varied styles, sizes, and orientations. Unlike standard OCR, the Read API uses deep-learning models to handle unstructured documents and real-world scenarios like package labels on a conveyor belt.

Answer

Image Analysis (describing the image content)

Answer

Face API

Answer

Custom Vision

Question 13

Which of the following is an example of an AI workload that uses prediction?

Accepted Answer

Predicting equipment failure based on sensor data. Predicting equipment failure based on sensor data is a classic example of a predictive AI workload. It uses historical sensor data (e.g., temperature, vibration, pressure) to train a machine learning model that forecasts when equipment is likely to fail, enabling proactive maintenance. This falls under the AI workload category of prediction, where the model outputs a future outcome or probability.

Answer

Displaying a list of items in alphabetical order

Answer

Storing customer records in a database

Answer

Formatting text documents

Question 14

A medical research team needs to analyze CT scans to identify and outline the exact boundaries of lung nodules. Which Azure Computer Vision capability should they use?

Accepted Answer

Semantic Segmentation. Semantic segmentation is the correct capability because it classifies each pixel in an image, enabling precise delineation of object boundaries. For CT scans, this allows the model to outline the exact shape and contour of lung nodules, which is essential for medical analysis. Image classification and object detection only provide labels or bounding boxes, not pixel-level boundaries.

Answer

Image Classification

Answer

Object Detection

Answer

Optical Character Recognition (OCR)

Question 15

A marketing team wants to use Azure OpenAI to generate blog post outlines. They have a single example of an outline that follows their preferred structure: introduction, three key points, conclusion. They want the model to generate new outlines that follow the same structure without retraining the model. Which technique should they use?

Accepted Answer

Providing the example outline in the prompt (few-shot learning). Few-shot learning involves providing a small number of examples (in this case, one example outline) directly in the prompt to guide the model's output format and structure without any retraining. This technique leverages the model's in-context learning ability to mimic the given pattern, making it ideal for generating new outlines that follow the same structure.

Answer

Fine-tuning the model on a large dataset of blog outlines

Answer

Setting the temperature parameter to a high value

Answer

Using the Azure OpenAI embeddings API