AI-900 Practice Test 14 — 15 Questions

Question 1

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.

Answer

Speech

Answer

Translator

Answer

QnA Maker

Question 2

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.

Answer

Object detection

Answer

Image classification

Answer

Face detection

Question 3

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.

Answer

Object detection

Answer

Image classification

Answer

Facial detection

Question 4

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.

Answer

Fairness

Answer

Privacy and Security

Answer

Inclusiveness

Question 5

Drag and drop the steps to analyze an image with Azure Computer Vision into the correct order.

Accepted Answer

Create an Azure Computer Vision resource, then obtain the endpoint and key, then call the API with the image and desired features, then parse the JSON response.. Image analysis requires resource setup, API call with features, and understanding the response.

Answer

Obtain the endpoint and key, then call the API, then create the resource, then parse the response.

Answer

Call the API with the image, then parse the response, then create the resource, then obtain endpoint and key.

Answer

Create the resource, then call the API, then obtain endpoint and key, then parse the response.

Question 6

A retail store uses ceiling-mounted cameras to analyze customer traffic flow. They need to detect when a person enters a specific aisle and determine the direction they are walking. Which Azure Computer Vision capability should they use?

Accepted Answer

People counting (Spatial Analysis). Spatial Analysis, part of Azure Computer Vision, uses ceiling-mounted cameras to track people's movement and direction in a physical space. It specifically provides people counting and trajectory analysis, making it ideal for detecting when a person enters an aisle and determining their walking direction.

Answer

Image Analysis dense captioning

Answer

Facial recognition

Answer

Optical Character Recognition (OCR)

Question 7

Drag and drop the steps to create a knowledge base in QnA Maker (now Language service) into the correct order.

Accepted Answer

Create a QnA Maker resource, then create a knowledge base and add QnA pairs, then test the knowledge base, then publish the knowledge base.. Creating a QnA knowledge base involves setting up the resource, adding QnAs, testing, and publishing.

Answer

Create a QnA Maker resource, then test the knowledge base, then create a knowledge base and add QnA pairs, then publish the knowledge base.

Answer

Create a knowledge base and add QnA pairs, then create a QnA Maker resource, then test the knowledge base, then publish the knowledge base.

Answer

Create a QnA Maker resource, then create a knowledge base and add QnA pairs, then publish the knowledge base, then test the knowledge base.

Question 8

Match each Azure AI service to its associated API or SDK.

Accepted Answer

Azure Computer Vision: Computer Vision API. The correct matches are: Azure Computer Vision with Computer Vision API, Azure Face Service with Face API, Azure Speech Service with Speech-to-Text API, and Azure Translator Text with Translator Text API. Common confusions include mixing up services with similar capabilities, such as attributing the Face API to Computer Vision or the Speech-to-Text API to Translator Text.

Answer

Azure Computer Vision: Face API

Answer

Azure Translator Text: Speech-to-Text API

Question 9

Match each Azure AI concept to its definition.

Accepted Answer

Anomaly Detector: Identifies unusual patterns or outliers in data.. The correct matches are: Anomaly Detector detects unusual patterns, Computer Vision analyzes visual data, NLP processes human language, and Speech Services handles audio conversion. The distractors swap definitions between Anomaly Detector and Computer Vision.

Answer

Anomaly Detector: Extracts information from images, videos, and other visual inputs.

Answer

Computer Vision: Identifies unusual patterns or outliers in data.

Question 10

A data scientist is training a regression model to predict house prices. The data scientist wants to evaluate the model using a metric that penalizes large prediction errors significantly more than small errors. Which evaluation metric should the data scientist choose?

Accepted Answer

Root Mean Squared Error (RMSE). Root Mean Squared Error (RMSE) is the correct choice because it squares the residuals before averaging, which heavily penalizes large prediction errors (outliers) more than small errors. This aligns with the requirement to penalize large errors significantly more than small ones, as the squaring operation amplifies the impact of larger deviations.

Answer

Mean Absolute Error (MAE)

Answer

R-squared (R²)

Answer

Mean Absolute Percentage Error (MAPE)

Question 11

A data scientist trains a multiclass classification model to identify different species of flowers (Iris setosa, Iris virginica, Iris versicolor). The overall accuracy is 94%, but the accuracy for the Iris virginica class is only 60%. Which additional metric should the data scientist examine to better understand the model's performance on the minority class?

Accepted Answer

F1-score. The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both false positives and false negatives. Since the model has high overall accuracy but poor performance on the minority class (Iris virginica), the F1-score is ideal for evaluating the model's effectiveness on that class, as it accounts for class imbalance better than accuracy alone.

Answer

Precision

Answer

Recall

Answer

Mean Absolute Error (MAE)

Question 12

A customer support team wants to analyze chat logs to automatically identify the most common reasons for customer complaints and track how customer sentiment changes throughout a conversation. They plan to use prebuilt Azure AI Language features without any custom training. Which combination of features should they use?

Accepted Answer

Key phrase extraction and sentiment analysis. Key phrase extraction identifies the most common reasons for complaints by pulling out important terms from the chat logs, while sentiment analysis tracks how customer sentiment changes throughout a conversation by assigning positive, negative, or neutral scores per utterance. Both are prebuilt Azure AI Language features that require no custom training, making them the correct combination for this scenario.

Answer

Entity recognition and language detection

Answer

Text summarization and question answering

Answer

Conversational language understanding and personal identification

Question 13

A legal firm needs to automatically process thousands of court documents. The system must identify and redact sensitive personal information such as names, addresses, and social security numbers. Additionally, it must extract legal-specific entities like case numbers, judge names, and statute references. The firm has a small set of manually annotated documents with these legal entities. Which combination of Azure AI Language features should they use?

Accepted Answer

PII detection and custom named entity recognition (NER). The firm needs both PII detection to redact sensitive personal information and custom NER to extract legal-specific entities like case numbers and judge names from a small set of annotated documents. Azure AI Language provides a prebuilt PII detection feature for common sensitive data and a custom NER capability that can be trained on the firm's annotated documents to recognize domain-specific entities.

Answer

Prebuilt entity recognition and key phrase extraction

Answer

Sentiment analysis and language detection

Answer

PII detection only

Question 14

A wildlife research team uses drone imagery to monitor penguin populations in a remote area. The penguins are small, blend into the rocky background, and are often only partially visible. The team has a limited set of 500 labeled drone images showing penguins. They want to build a system that accurately detects and counts penguins. Which approach should they take using Azure AI services?

Accepted Answer

Train a Custom Vision object detection model using the labeled images.. The pre-built Computer Vision object detection API is optimized for common objects and may not perform well on small, camouflaged penguins in rocky terrain. Custom Vision allows the team to train a dedicated object detection model using their 500 labeled images, enabling the model to learn the specific visual features of penguins in this challenging environment. This approach is ideal for domain-specific detection tasks where off-the-shelf models lack accuracy.

Answer

Use the pre-built Computer Vision object detection API directly.

Answer

Use the Computer Vision Image Analysis API with the 'dense captioning' feature.

Answer

Train a Custom Vision image classification model with the labeled images.

Question 15

A data scientist is training a classification model on a dataset with 100 features and only 500 labeled samples. The model achieves 99% accuracy on the training data but only 68% accuracy on a held-out test set, indicating overfitting. Which technique is most appropriate to directly address this problem?

Accepted Answer

Reduce the number of features used for training. Reducing the number of features directly combats overfitting by decreasing model complexity and the risk of learning noise from irrelevant or redundant features. With only 500 samples and 100 features, the model has a high variance problem; feature selection or dimensionality reduction (e.g., using Azure Machine Learning's Filter-Based Feature Selection or PCA) simplifies the hypothesis space, improving generalization to the test set.

Answer

Increase the amount of training data by collecting more samples

Answer

Increase the complexity of the model by adding more layers

Answer

Train for more epochs