AI-900 Practice Test 13 — 15 Questions

Question 1

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.

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To automatically collect and label training data

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To automatically deploy trained models to production

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To automatically monitor models for performance degradation

Question 2

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.

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A: Accuracy

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B: Precision

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C: Recall

Question 3

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.

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How quickly the model makes predictions

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How much memory the model uses during inference

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The number of training examples used to build the model

Question 4

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.

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Azure Face API

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Azure Custom Vision

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Azure Video Indexer

Question 5

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.

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The physical location where training images were captured

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The minimum image resolution required for accurate model training

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

Question 6

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.

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AI systems should run as fast as possible regardless of accuracy

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AI systems should be available 24/7 without any downtime

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

Question 7

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.

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Custom Text Classification

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Key Phrase Extraction

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Sentiment Analysis

Question 8

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.

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Image Analysis (describing the image content)

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Face API

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Custom Vision

Question 9

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.

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Displaying a list of items in alphabetical order

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Storing customer records in a database

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Formatting text documents

Question 10

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.

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Image Classification

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Object Detection

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Optical Character Recognition (OCR)

Question 11

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.

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

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Setting the temperature parameter to a high value

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Using the Azure OpenAI embeddings API

Question 12

A customer support team wants to automatically analyze customer emails to determine if the sentiment is positive, negative, or neutral. Which Azure service should they use?

Accepted Answer

Text Analytics. The Text Analytics service (part of Azure Cognitive Services) provides pre-built sentiment analysis, which can classify text as positive, negative, or neutral. This directly matches the requirement to automatically analyze customer emails for sentiment without needing to build custom machine learning models.

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Speech

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Translator

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QnA Maker

Question 13

A government agency needs to digitize thousands of handwritten application forms so that the text can be searched and processed. Which Azure Computer Vision capability should they use?

Accepted Answer

Optical Character Recognition (Read API). Optical Character Recognition (Read API), because the agency needs to extract printed or handwritten text from images of application forms and make it searchable and processable. The Read API is specifically designed for this purpose, handling both printed and handwritten text, and is part of Azure Computer Vision's OCR capabilities.

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Object detection

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Image classification

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Face detection

Question 14

A historical society has scanned hundreds of books printed in the 19th century. They want to convert the scanned images into searchable, editable text. Which Azure Computer Vision capability should they use?

Accepted Answer

Optical Character Recognition (OCR). Optical Character Recognition (OCR) is the Azure Computer Vision capability designed to extract printed or handwritten text from images and convert it into machine-readable, searchable, and editable text. For the historical society's scanned books, OCR can detect characters and words from the 19th-century prints and output them as digital text, enabling full-text search and editing.

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Object detection

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Image classification

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Facial detection

Question 15

A hospital is developing an AI system to assist doctors in diagnosing diseases from medical images. The system's predictions can influence patient treatment. Which Microsoft responsible AI principle is most important to ensure the system's decisions are accurate and reliable?

Accepted Answer

Reliability and Safety. In a medical diagnosis system, accuracy and reliability are paramount because incorrect predictions can directly lead to patient harm. The Reliability and Safety principle ensures the AI system performs consistently under expected conditions, with appropriate fail-safes and validation, which is the core requirement for clinical decision support.

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Fairness

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Privacy and Security

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Inclusiveness